Electric Road Systems

A feasibility study investigating a possible

future of road transportation

Archit Singh

Master of Science Thesis EGI_2016-090 MSC

KTH Sustainable Energy Engineering

Energy and Environment

SE-100 44 STOCKHOLM

i

Master of Science Thesis EGI_2016-090 MSC

Electric Road Systems

A feasibility study investigating a possible

future of road transportation

Archit Singh

Approved

2016-10-10

Examiner

Hatef Madani

Supervisor

Björn Hasselgren

Company Supervisor

Gunnar Asplund

Contact person

Björn Hasselgren

Abstract

The transportation sector is a vital part of today’s society and accounts for 20 % of our global total

energy consumption. It is also one of the most greenhouse gas emission intensive sectors as almost

95 % of its energy originates from petroleum-based fuels. Due to the possible harmful nature of

greenhouse gases, there is a need for a transition to more sustainable transportation alternatives. A

possible alternative to the conventional petroleum-based road transportation is implementation of

Electric Road Systems (ERS) in combination with electric vehicles (EVs). ERS are systems that

enable dynamic power transfer to the EV's from the roads they are driving on. Consequently, by

utilizing ERS in combination with EVs, both the cost and weight of the EV-batteries can be kept

to a minimum and the requirement for stops for recharging can also be eliminated. This system

further enables heavy vehicles to utilize battery solutions.

There are currently in principal three proven ERS technologies, namely, conductive power transfer

through overhead lines, conductive power transfer from rails in the road and inductive power

transfer through the road. The aim of this report is to evaluate and compare the potential of a full-

scale implementation of these ERS technologies on a global and local (Sweden) level from

predominantly, an economic and environmental perspective. Furthermore, the thesis also aims to

explore how an expansion of ERS might look like until the year 2050 in Sweden using different

scenarios. To answer these questions two main models (global and Swedish perspective) with

accompanying submodels were produced in Excel.

The findings show that not all countries are viable for ERS from an economic standpoint, however,

a large number of countries in the world do have good prospects for ERS implementation. Findings

further indicated that small and/or developed countries are best suited for ERS implementation.

From an economic and environmental perspective the conductive road was found to be the most

attractive ERS technology followed by overhead conductive and inductive road ERS technologies.

The expansion model developed demonstrates that a fast expansion and implementation of an

ERS-based transportation sector is the best approach from an economical perspective where the

conductive road technology results in largest cost savings until 2050.

ii

Examensarbete EGI_2016-090 MSC

Elektriska vägsystem

Genomförbarhetsstudie kring en möjlig

framtid för vägstransport

Archit Singh

Godkänt

2016-10-10

Examinator

Hatef Madani

Handledare

Björn Hasselgren

Företagshandledare

Gunnar Asplund

Kontaktperson

Björn Hasselgren

Sammanfattning

Transportsektorn är en viktig del av dagens samhälle och står för 20% av den totala globala

energiförbrukningen. Det är också en av de sektorer med mest växthusgasutsläpp, där nästan 95%

av energin härstammar från petroleumbaserade bränslen. På grund av växthusgasers potentiellt

skadliga karaktär finns det ett behov för en övergång till mer hållbara transportmedel. En möjlig

alternativ till den konventionella petroleumbaserade vägtransporten är implementering av

elektriska vägsystem (ERS) i kombination med elfordon. Elektriska vägsystem är system som

möjliggör dynamisk kraftöverföring till fordon från vägarna de kör på. Sålunda kan man genom

att använda ERS i kombination med elbilar, minimera både kostnaden och vikten av batterierna

samt även minska eller eliminera antalet stopp för omladdningar. Dessutom möjliggör detta system

att även tunga fordon kan använda sig av batterilösningar.

Det finns för närvarande i princip tre beprövade ERS-tekniker, nämligen konduktiv

kraftöverföring genom luftledningar, konduktiv kraftöverföring från räls i vägen och induktiv

kraftöverföring genom vägen. Syftet med denna rapport är att utvärdera och jämföra potentialen

för en fullskalig implementering av dessa ERS-teknik på en global och lokal (Sverige) nivå från,

framförallt, ett ekonomiskt- och ekologiskt perspektiv. Rapporten syftar också till att undersöka,

med hjälp av olika scenarier, hur en utbyggnad av ERS i Sverige skulle kunna se ut fram till år

2050. För att besvara dessa frågor producerades två huvudmodeller (global och lokal perspektiv)

med kompletterande undermodeller i Excel.

De erhållna resultaten visar att ERS inte är lönsamt ur ett ekonomisk perspektiv i precis alla de

undersökta länder, dock har ett stort antal länder i världen visat sig ha goda förutsättningar för

ERS. Vidare visar resultaten att små och/eller utvecklade länder är bäst lämpade för ERS. Ur ett

ekonomiskt- och ekologiskt perspektiv har konduktiv kraftöverföring från räls i väg tekniken visat

sig vara den mest attraktiva, följt av konduktiv kraftöverföring genom luftledningar och induktiv

kraftöverföring genom väg teknikerna. Expansionsmodellen som utvecklats visar att en snabb

expansion och implementation av en ERS-baserad vägtransportsektor är det bästa alternativet, där

tekniken för konduktiv kraftöverföring från räls i väg ger de största kostnadsbesparingar fram till

2050.

iii

Acknowledgements

I would like to express my upmost gratitude towards Elways and the department of Energy

Technology at the Royal Institute of Technology for supporting and enabling this master thesis.

Especially, I would like to thank my supervisor Gunnar Asplund at Elways who gave me the

inspiration for the master thesis and has since helped and supported me meticulously throughout

the process. I am also very grateful toward my supervisor Björn Hasselgren at the Royal Institute

of Technology for being a great mentor, contributing with insightful inputs and active engagement

during my thesis. Furthermore, I would like to give a special thanks to my examiner Hatef Madani

for his support and guidance during the thesis. Last but not least, I would like to extend my

appreciation to my fellow students Mårten Lundqvist, Martin Isacsson and Eric Schmidt for their

perceptive feedbacks while proof reading the thesis report.

Archit Singh

Stockholm, August 2016

iv

Table of Contents ABSTRACT ..................................................................................................................................................................... I

SAMMANFATTNING ....................................................................................................................................................... II

ACKNOWLEDGEMENTS ................................................................................................................................................... III

LIST OF TABLES ............................................................................................................................................................ VI

LIST OF FIGURES ........................................................................................................................................................... VII

NOMENCLATURE AND ABBREVIATIONS .............................................................................................................................. IX

1 INTRODUCTION........................................................................................................................................ 1

1.1 BACKGROUND AND PROBLEM DESCRIPTION .............................................................................................................. 1

1.2 PURPOSE............................................................................................................................................................ 3

1.3 METHOD ............................................................................................................................................................ 3

1.4 DELIMITATIONS ................................................................................................................................................... 4

1.5 ASSUMPTIONS..................................................................................................................................................... 5

1.5.1 Model 1 – World .................................................................................................................................... 7

1.5.2 Model 2 – Sweden ................................................................................................................................. 8

2 FRAME OF REFERENCE ....................................................................................................................... 10

2.1 ELECTRIC ROAD SYSTEMS .................................................................................................................................... 10

2.1.1 Overhead Conductive Transmission Technology ................................................................................. 11

2.1.2 Conductive Power Transfer from Road ................................................................................................ 14

2.1.3 Inductive Power Transfer from Road ................................................................................................... 16

2.1.4 Stakeholders ........................................................................................................................................ 18

2.2 ELECTRIC VEHICLES ............................................................................................................................................. 20

2.2.1 Batteries .............................................................................................................................................. 22

2.2.2 Fast Chargers ....................................................................................................................................... 24

2.3 ROAD NETWORK IN THE WORLD ........................................................................................................................... 25

2.4 FUELS .............................................................................................................................................................. 27

2.4.1 Gasoline ............................................................................................................................................... 27

2.4.2 Diesel ................................................................................................................................................... 28

2.5 THE ELECTRICITY MARKET ................................................................................................................................... 29

2.5.1 Global Electricity Mix ........................................................................................................................... 30

2.5.2 Nordic Electricity Mix........................................................................................................................... 31

3 MODELS ................................................................................................................................................... 33

3.1 MODEL 1 – WORLD ........................................................................................................................................... 34

3.1.1 Computing the Optimal Electrified Road Length ................................................................................. 34

3.1.2 Comparison – Petroleum-based Road Transport against ERS and EV Combination ........................... 41

3.1.3 Comparison – Pure Battery Electric Car Fleet against ERS and Electric Car Combination ................... 43

3.2 MODEL 2 – SWEDEN .......................................................................................................................................... 45

3.2.1 Submodel 1 – Comparing a Petroleum-based Road Transport System against ERS and EV

Combination ...................................................................................................................................................... 45

3.2.2 Submodel 2 – Comparing a Pure Battery Electric Car Fleet against ERS and Electric Car Combination

47

3.2.3 Submodel 3 – Finding the Breakeven Point ......................................................................................... 47

3.2.4 Submodel 4 – Expansion ...................................................................................................................... 48

4 RESULTS .................................................................................................................................................. 54

4.1 PROSPECT OF ELECTRICAL ROAD SYSTEMS IN THE WORLD .......................................................................................... 54

v

4.1.1 Comparison – Petroleum-based Road Transport against ERS and EV Combination ........................... 54

4.1.2 Comparison – Pure Battery Electric Car fleet against ERS and Electric Car Combination ................... 59

4.2 PROSPECT OF ELECTRICAL ROAD SYSTEM IN SWEDEN ................................................................................................ 61

4.2.1 Submodel 1 – Comparing a Petroleum-based Road Transport System against ERS and EV

Combination ...................................................................................................................................................... 61

4.2.2 Submodel 2 – Comparing a Pure Battery Electric Car Fleet against ERS and Electric Car Combination

62

4.2.3 Submodel 3 – Finding the Breakeven Point ......................................................................................... 64

4.2.4 Submodel 4 – Expansion ...................................................................................................................... 66

5 DISCUSSION ............................................................................................................................................ 70

5.1 PROSPECT OF ELECTRIC ROAD SYSTEMS COMPARED TO THE CONVENTIONAL ROAD TRANSPORTATION SYSTEM .................... 70

5.1.1 The Global Perspective ........................................................................................................................ 70

5.1.2 The Swedish Perspective...................................................................................................................... 71

5.1.3 Validity and Limitations of the Model ................................................................................................. 72

5.2 COMPARING A PURE BATTERY ELECTRIC CAR FLEET AGAINST AN ERS BASED FLEET ......................................................... 73

5.2.1 Global and Swedish Perspective .......................................................................................................... 73

5.2.2 Validity and Limitations of the Model ................................................................................................. 74

5.3 BREAKEVEN POINT AND EXPANSION ....................................................................................................................... 75

5.3.1 Breakeven Point .................................................................................................................................. 75

5.3.2 Expansion Scenarios ............................................................................................................................ 76

5.3.3 Validity and Limitations of the Model ................................................................................................. 76

5.4 DISCUSSING THE ELECTRIC ROAD SYSTEM TECHNOLOGIES .......................................................................................... 77

5.5 IMPLICATIONS FOR GOVERNMENTAL POLICIES AND STRATEGIES .................................................................................. 79

6 CONCLUSIONS ........................................................................................................................................ 80

7 RECOMMENDATIONS FOR FUTURE WORK...................................................................................... 82

8 REFERENCES .......................................................................................................................................... 83

9 APPENDIX ................................................................................................................................................ 89

9.1 APPENDIX A – ASSUMPTIONS............................................................................................................................... 89

9.1.1 Overall Assumptions ............................................................................................................................ 89

9.1.2 Model 1 – World .................................................................................................................................. 89

9.1.3 Model 2 – Sweden ............................................................................................................................... 90

9.2 APPENDIX B – COMPARISON BETWEEN PETROLEUM-BASED ROAD TRANSPORT VS ERS AND EV COMBINATION ................... 91

9.3 APPENDIX C – COMPARISON BETWEEN A PURE BATTERY ELECTRIC CAR FLEET VS ERS AND ELECTRIC CAR COMBINATION ...... 97

vi

List of Tables

Table 1. Yearly vehicle kilometer for all vehicles per road category during 2015. ...................... 49

Table 2. Yearly vehicle kilometer for heavy-duty trucks per road category during 2015. ........... 49

Table 3. Expansion specifications for each scenario. .................................................................... 53

Table 4. Most important global results for each ERS technology in countries where the yearly

savings are positive, assuming 2015 battery pricing. .................................................................... 55

Table 5. Most important global results for each ERS technology in countries where the yearly

savings are positive, assuming future battery pricing. .................................................................. 55

Table 6. The cost difference in percent between the two cases for all 184 countries assuming 2015

battery pricing. .............................................................................................................................. 59

Table 7. The cost difference in percent between the two cases for all 184 countries assuming future

battery pricing. .............................................................................................................................. 59

Table 8. Number of countries where the ERS solution is cheaper and the cost difference between

the two cases assuming 2015 battery pricing. ............................................................................... 59

Table 9. Number of countries where the ERS solution is cheaper and the cost difference between

the two cases assuming future battery pricing. ............................................................................. 60

Table 10. The most important results obtained for the three ERS technologies in Sweden assuming

2015 battery pricing. ..................................................................................................................... 61

Table 11. The most important results obtained for the three ERS technologies in Sweden assuming

future battery pricing. .................................................................................................................... 61

Table 12. The cost difference between the two cases for the two examined ERS technologies

assuming 2015 battery pricing. ..................................................................................................... 62

Table 13. The cost difference between the two cases for the two examined ERS technologies

assuming future battery pricing. .................................................................................................... 62

Table 14. The pros and cons of each technology attained in the study from an economic,

environmental and aesthetic perspective. ...................................................................................... 78

Table 15. Countries where the yearly savings from an ERS based transportation system are positive

using the current battery price ((Conductive Road Case).. ........................................................... 91

Table 16. Countries where the yearly savings from an ERS based transportation system are positive

using the future battery cost (Conductive Road Case). ................................................................. 94

Table 17. A list with countries where the ERS plus battery combination is cheaper than a pure

battery based passenger car fleet. .................................................................................................. 97

vii

List of Figures

Figure 1. The main steps of the method used are shown in the figure. The writing of report was

done continuously throughout the project. ...................................................................................... 3

Figure 2. Principal design of an ERS. (Source: [10]) .................................................................... 11

Figure 3. The picture illustrates a truck driving on an electrified road using overhead lines. (Source:

Scania [22]) ................................................................................................................................... 12

Figure 4. Example of an intelligent pantograph developed by Siemens. (Source: [23]) .............. 13

Figure 5. Siemens eHighway system test track in Groß Dölln, Germany. (Source: [24]) ............ 13

Figure 6. The picture shows how the electrical power is fed from the grid to the vehicle. (Source:

[30]) ............................................................................................................................................... 15

Figure 7. Cross section of the road with a rail in each half and cables buried outside the roadway.

(Source: [30]) ................................................................................................................................ 15

Figure 8. The figure illustrates the principle behind the inductive power transfer from road

technology. (Source [37]) .............................................................................................................. 16

Figure 9. This figure illustrates the basis of the primove Highway architecture. (Source [8]). .... 17

Figure 10. The major stakeholders in the ERS. (Source [8]) ........................................................ 18

Figure 11. A Thomas Parker’s electric car from the 1880s. (Source: [39]) .................................. 20

Figure 12. Worldwide number of electric vehicles in use from 2012 to 2015. (Source: [41]) ..... 21

Figure 13. Estimated Costs of EV Batteries through 2020. (Source: [54]) ................................... 23

Figure 14. The charging profile of a Tesla Supercharger. (Source: [60]) ..................................... 24

Figure 15. The major road network in orange in United States. ................................................... 25

Figure 16. The major road network in orange in parts of Europe. ................................................ 26

Figure 17. The major road network in orange in Scandinavia. ..................................................... 26

Figure 18. The world transportation consumption by fuel. (Source: [64]) ................................... 27

Figure 19. The cost development of gasoline in Sweden excluding VAT from the year 1981 –

2015. (Source: [69]) ...................................................................................................................... 28

Figure 20. The cost development of diesel excluding VAT in Sweden between the years 2001 to

2015. (Source: [69]) ...................................................................................................................... 29

Figure 21. World total final energy usage by source for 2013. (Source: [73]) ............................. 29

Figure 22. World electricity generation from all energy sources in 2014. (Source: [78]) ............ 30

Figure 23. Electricity prices in U.S. dollar cents per kilowatt hour excluding taxes. (Source: [79])

....................................................................................................................................................... 31

Figure 24. Power production by source in the Nordic region in 2013. (Source: [82]) ................. 32

Figure 25. An illustrative flow chart over the two models with accompanying submodels produced

to investigate the thesis objectives. ............................................................................................... 33

Figure 26. Example of how a sectioning of Germany into quadratic road sections with ERS

installation can be conceptualized (Note that the meshed road sections would in reality only be on

the land area). ................................................................................................................................ 35

Figure 27. Viewing one square in the quadratic grid-mesh. ......................................................... 36

Figure 28. Explanation of why the circumferential length of a square in a large grid-mesh was

approximated as 2x. ....................................................................................................................... 36

Figure 29. Cost development as a function of length x for battery, ERS installation and the

combination of these two factors. ................................................................................................. 39

Figure 30. Vehicle kilometer as a function of road length. ........................................................... 50

Figure 31. Traffic intensity as a function of road length. .............................................................. 50

viii

Figure 32. Number of countries where the yearly profit is positive for different ERS technologies

and battery cost scenarios. ............................................................................................................. 56

Figure 33. Percentage of total number of vehicles worldwide in countries where the yearly saving

is positive for different ERS technologies and battery cost scenarios. ......................................... 56

Figure 34. Percentage of total number of heavy vehicles worldwide in countries where the yearly

saving is positive for different ERS technologies and battery cost scenarios. .............................. 57

Figure 35. Percent of global total GHG emissions reduced by implementing the different ERS

technologies in countries where the yearly savings are positive for different and battery cost

scenarios. ....................................................................................................................................... 57

Figure 36. The percentage of the countries in the world/continents that are suitable for conductive

power transfer from road solution assuming the current battery pricing of 350 USD/kWh. ........ 58

Figure 37. The percentage of the countries in the world/continents that are suitable for conductive

power transfer from road solution assuming the predicted future battery pricing of 120 USD/kWh.

....................................................................................................................................................... 58

Figure 38. A summarization of all the cost differences presented in the previous tables ............. 60

Figure 39. A summarization of the yearly savings or losses for each ERS technology and battery

cost scenario. ................................................................................................................................. 62

Figure 40. The number of cars required until an ERS based system is cheaper for the two examined

ERS technologies assuming 2015 battery pricing. ........................................................................ 63

Figure 41. The number of cars required until an ERS based system is cheaper for the two examined

ERS technologies assuming future battery pricing. ...................................................................... 64

Figure 42. The frequency breakeven points for inductive and conductive ERS technologies. ..... 65

Figure 43. The frequency breakeven point for overhead conductive ERS technology. ................ 65

Figure 44. Accumulated result for the three different scenarios for conductive road ERS

technology. .................................................................................................................................... 66

Figure 45. Accumulated result for the three different scenarios for overhead conductive ERS

technology. .................................................................................................................................... 67

Figure 46. Accumulated result for the three different scenarios for inductive road ERS technology.

....................................................................................................................................................... 67

Figure 47. Accumulated result obtained from Scenario 1 for all ERS technologies. ................... 68

Figure 48. The yearly development of GHG emission reduction for the three different scenarios

for conductive and inductive ERS technology. ............................................................................. 69

ix

Nomenclature and Abbreviations

Notations

Symbol Description

∆ Quadratic grid-mesh size in kilometer

x Length of a side in kilometer of a square in the quadratic grid-mesh

k1 The cost of battery per kilometer and car

k2 Cost per car per km2 grid

B(x) Function of battery cost by distance (km)

ERS(x) Function of ERS installation cost by distance (km)

f(x) Combined function of battery and ERS installation cost by distance (km)

Abbreviations

WTW Well-to-wheel

GHG Greenhouse gas emissions

CO2 Carbon Dioxide

EU European Union

MK1 Miljöklass 1

EV Electric Vehicle

DC Direct current

ERS Electric Road System

ICE Internal Combustion Engine

1

1 INTRODUCTION

This thesis is the final assignment for the master Sustainable Energy Engineering given under the

program Energy and Environment at KTH Royal Institute of Technology in cooperation with

Elways.

The thesis examines the prospect of Electric Road Systems, abbreviated as ERS, to be a solution

to our dependency on fossil fuels and thus the future of transportation. ERS are technologies that

enable continuous electricity transfer to vehicles in motion. In this report, the potential of different

ERS technologies are examined from a technical, economic and environmental standpoint from a

global and a Swedish perspective.

1.1 Background and Problem Description

The global energy usage has seen a constant increase since the industrial revolution and the trend

is likely to continue for a foreseeable future. According to the U.S. Energy Information

Administration, the world energy consumption is estimated to increase by 56 % until 2040 [1].

Transportation is a vital part of today’s energy-intensive society, and accounts for 20 % [2] of our

global total energy usage. Furthermore, as almost 95 % of this energy originates from petroleum-

based fuels, this results in a huge emission of greenhouse gases (GHG). In fact, in 2014, the

transport sector alone accounted for approximately 14% of the total GHG emissions [3]. This

development has compelled governments around the world to start setting goals to mitigate the

increasing global pollution. For example, the Swedish Government has published a statement

proposing a goal to make the Swedish transportation sector entirely fossil fuel neutral by the year

2030 [4]. Similarly, the European Union aims to have at least 10 % of the energy used in the

transportation sector come from renewable energy sources by 2020 [5].

Although some difference of opinion and disputes remain regarding how dangerous greenhouse

gases actually are for the environment, most experts agree that the peak-oil scenario is approaching

and will eventually make fossil fuels too expensive to extract and use. As a result, it is of utmost

importance that we as smoothly and quickly as possible transition to more sustainable fuels.

Vehicles powered by electricity using batteries, also known as electric vehicles (EV), are an

alternative to traditional petroleum-powered vehicles. EVs have experienced tremendous progress

during the last decennium due to a number of reasons, with the foremost reasons being the recent

rapid development in the battery technology and the situation in the world where we are trying to

progressively move away from fossil fuels. Even though electric vehicles might be a good potential

alternative to petroleum-based vehicles, there still are a number of challenges that need to be

addressed before EVs can become competitive enough to achieve the required breakthrough.

One of the major limitations for electrical vehicles are the capabilities for the current battery

technology. Despite the fact that considerable development has been made in recent times within

the field, the energy density of batteries is still substantially lower than the energy density of

petroleum-based fuels [6]. Likewise, the charging time required is a major challenge in making

electric vehicles more commercially attractive. Even with the latest fast charging technology, it

2

still takes up to 40 minutes to charge a battery with less range than what a full tank of diesel can

provide [7]. Moreover, if we also want to electrify heavy vehicles (e.g. trucks and buses), it will

not, in the foreseeable future, be possible to run these solely on batteries since the energy storage

capacity and the output power of batteries is not enough for long-distance transportation of heavy

vehicles [8]. Hence, the main obstacle towards electrifying heavy vehicles is the size and weight

required for on-board storage of electrical energy. As an example, a road truck weighing 40 tons

travelling 1,000 kilometers would require batteries weighing approximately 20 tons. The batteries

would in this case take up far too much of the cargo space and would thus make the heavy vehicles

highly unprofitable. To make the EV less reliant on the battery, especially for long distance heavy

transport, and at the same time reduce the vehicle cost, a possible solution could be to transfer

power to the vehicle from the road.

ERS are systems that enable dynamic power transfer to the vehicles from the roads they are driving

on. In an ERS based road transportation system, the largest roads would be electrified so that the

bulk distance travelled by vehicles would be done using external electric power. The remaining

distance outside the ERS network could be performed by for example either using an internal

combustion engine (ICE) or by using (small) on-board batteries optimized for shorter routes. By

utilizing ERS, both the cost and weight of the batteries could be kept to a minimum and the

requirement for stops for recharging would also be eliminated since it would be possible to

recharge while driving [8]. There are a number of ways and technologies available that can be

utilized in transferring power from the roads to the vehicle. However, there are currently in

principal three ERS technologies that have shown potential and are considered in the industry.

These are more specifically the conductive power transfer through overhead lines, conductive

power transfer from rails in the road and inductive power transfer through the road [9].

As the ERS industry is relatively new, not many research reports have as of yet been published in

this field. Furthermore, the reports that have been published are usually limited to examining only

one ERS technology and look at the potential of this technology on a specific road case. For

example, Viktoria Swedish ICT has in collaboration with different stakeholders compiled a

detailed evaluation of both the inductive road technology and the overhead conductive road

technology ( [10], [8]). However, both these reports assess the potential of the technologies from

only a heavy vehicle transportation perspective and include cost estimates for a full deployment

of a road section between Stockholm and Gothenburg. Similarly, the report published by

Andersson and Edfeldt [11] compares the potential of ERS heavy-duty trucks with conventional

and hybrid heavy-duty trucks from a haulage contractor companies’ perspective by examining

different road cases [11]. A couple of reports that aim to summarize the ERS concept and evaluate

its potential have been published ( [12], [13], [14]). Moreover, reports that examine different

business models and payment methods for ERS have also been produced ( [15], [16]).

Consequently, it was found that not much research existed which looked at a full-scale

implementation of the different ERS technologies from a global but also a Swedish perspective

and which compared the ERS technologies between each other. This thesis aims to therefore

evaluate and compare the potential of a full-scale implementation of different ERS technologies

in different countries of the world from predominantly, an economic and environmental

perspective.

3

1.2 Purpose

The purpose of this study is to investigate whether it is possible to replace the majority of the

transport sector's dependency on petroleum-based fuels by introducing ERS. More specifically,

the purpose is to:

1) Investigate the possibility of a full-scale implementation, predominantly from an economic

standpoint, for different ERS technologies around the world. Subsequently, to identify the

top countries/regions with the best and the worst prospects.

2) Examine the potential for implementation of ERS in Sweden and compare the different

technologies available from a primarily economic and environmental perspective.

Furthermore, this thesis explores using different scenarios, how an expansion might look

like until the year 2050.

1.3 Method

The thesis method is essentially split into four distinct parts, namely, literature review, modelling,

analysis of findings, and report writing. This is illustrated using different subparts in Figure 1

where the writing process was continuous throughout all the parts. The process was performed

iteratively in order to facilitate improvements continuously.

As in most projects, the first step was to study relevant articles and reports in order to understand

and create a broad picture about ERS and the surrounding topics. Early on, the scope and

appropriate limitations were discussed and decided on together with supervisors at KTH and

Litterature Review

- Reports

- Articles

- Interviews

- Internet

Modelling

- Excel

- Economic and environmental model

- Scenario analysis

- Sensitivity analysis

Analysis

- Evaluation and improvement of

findings

Figure 1. The main steps of the method used are shown in the figure. The writing of report was done

continuously throughout the project.

4

Elways. Reports and articles were then collected from various different sources which included

administrative authorities, ERS companies, and stakeholders within the ERS field just to name a

few. Collecting information from different stakeholders in the field was done in order to capture

different views and aspects of ERS technology. A number of interviews, both in person or through

a communication medium i.e. telephone, were performed with relevant stakeholders. Open

questions were mainly used to ensure that the responses were formulated by the interviewee him-

/herself which promoted unbiasedness. Naturally, the questions were varied depending on the field

of expertise of the person being interviewed. Both qualitative and quantitative data was acquired

from these interviews. However, literature in the form academic reports, articles, web pages of

companies, administrative authorities, etc., were mainly used as sources of information for the data

used in the modelling process.

To be able to answer the proposed inquiries, the thesis was split into evaluating the ERS field using

two different conceptual models. These models aimed to analyze and compare the prospect of

different ERS technologies from a technical, economic and environmental standpoint for a global

and a Swedish perspective. The methodological approach applied for each model was different.

Therefore, the modelling procedures used for the two models are presented in more details in

Chapter 3. All modelling was performed in the spreadsheet program Microsoft Excel. This

program was chosen because of a number of reasons, firstly, it was found to give a great overview

over the modelling process. It also made following equations step by step simpler. Moreover,

adding/changing inputs and altering equations could also be accomplished smoothly. For each

model, specific input data was collected which was mostly compiled from literature and through

making various assumptions (See chapter 1.5 for further details).

During the course of the thesis, work was carried out in a close contact with both the supervisor at

KTH and the supervisor at Elways. Both have contributed with feedback, information and quality

checks. This close collaboration was upheld through frequent meetings held approximately every

second week.

1.4 Delimitations

Some overall limitations of the thesis are presented in this section. Additionally, other limitations

are presented continuously in the report when judged necessary. The major limitations are:

The economic calculations made in this thesis only consider the techno-economic direct

costs and are thus only a partial cost of the actual complete economic expenditures. For

example, the cost for society in terms of lost jobs in the fossil fuel related markets are not

considered.

The operational costs are only considered in the expansion model (submodel 4) and are

hence neglected in the remaining models.

Only 184 countries could be studied in the global perspective model due to insufficient

data being available for the remaining countries.

The different alternatives of payment systems for ERS have not been investigated in this

thesis.

5

The probability that the electricity cost will most likely increase in the future due to wider

implementation of renewable energy sources and possible imposition of new taxes is not

considered.

No speculation over the future price trend was made for gasoline and diesel, hence,

contemporary prices were used for future scenarios as well.

In the calculations, the cost of expanding the electric grid to enable connections to the

electrified roads has been neglected. Furthermore, the power grid and the electricity

distribution technologies required for ERS are not considered.

The installation price of the different ERS technologies is kept the same as the current

anticipated prices when modelling the future.

Electricity is the only energy source for vehicles that has been compared as an alternative

to fossil fuels. Hence, other potential alternative fuels such as biofuels, hydrogen fuel cells,

etc. are not considered.

Battery storage is the only hybridization alternative considered for electric vehicles in

combination with ERS.

The current models do not take into consideration that when vehicles are travelling on the

electrified roads, the efficiency becomes higher than when utilizing the battery as energy

source. This is due to the fact that the electric motor in the EVs receive the energy directly

from the ERS, thus the efficiency loss obtained from battery storage is bypassed. This

would in reality result in a lower energy consumption per kilometer for the vehicles while

travelling on electrified roads.

Any form of energy regenerative systems in the vehicles and/or in the ERS have been

ignored.

1.5 Assumptions

In this report certain assumptions and simplifications were made to enable various calculations

and models, which otherwise would have made the problem excessively complex. Since the

correct assumptions are a vital part of a well-conducted scientific report, each assumption was

heavily scrutinized and studied before it was adopted. The overall major assumptions made for

this thesis are presented in this section. Moreover, for each model, more specific key assumptions

are presented in subsections. Finally, in Appendix C, some additional assumptions that were

judged to be too detailed for the main report body are presented.

In these models, motor vehicles were split into four main vehicle types. Namely, passenger

cars, light-duty trucks, heavy-duty trucks and buses.

It was assumed that all the current motor vehicles only use either diesel or gasoline as fuel,

thus vehicles that already use alternative fuels are disregarded in the models.

It was assumed that each country will phase out all the fossil fuel based vehicles by

replacing them with only electric vehicles that use batteries as energy source.

6

Three different ERS technologies were examined in this thesis, namely Elways conductive

road technology, Siemens overhead conductive technology and lastly, the inductive road

technology of Bombardier.

It was assumed that the conductive road and inductive ERS could be used by all vehicle

categories. However, the overhead conductive ERS was presumed to only be used by

heavy-duty trucks and buses, denoted together as heavy vehicles.

It was assumed that the road network in all countries can be approximated as quadratic

grid-meshes. This assumption is not that far from reality where many countries have a well-

connected road system in a meshed manner as shown in the frame of reference chapter

about the road network in the world.

The cost of installing inductive road, overhead conductive and conductive from road

technologies was assumed to be 15 MSEK/kilometer, 6 MSEK/kilometer and 4

MSEK/kilometer respectively. The lowest predicted cost for each technology was used, as

a construction at such a huge scale would probably lead to the lowest prices due to

economies of scale. In the case of inductive road technology, the cost per kilometer was

assumed to be lower than what was found through the literature study. This was chosen by

the author to fathom the potential of the technology in a best case scenario.

Cost of the pickup arm for conductive road power transfer has been estimated to be roughly

4500 SEK. According to Gunnar Asplund, this is a reasonable assumption as the prices

would decrease considerably if the pickup arm was mass produced due to economies of

scales. The cost of the pantograph for overhead conductive is assumed to be 55 000 SEK

and the inductive pickup system is assumed to have the same cost as the conductive road

technology. Naturally, this will result in an underestimation of the real cost as the pickup

arm for conductive road power transfer is expected to be cheapest out of the two options.

However, this method will still show the situation under a best case scenario for the

inductive road technology.

The average fuel consumption for all the categories of vehicles, taking into account both

diesel and petrol driven vehicles, was computed to be 0.1 l/km. On the other hand, the

average fuel consumption for heavy-duty trucks and buses was calculated as 0.38 l/km.

Due to the fact that the cost of diesel and gasoline have seen a sharp decrease in pricing

during the last year. It was judged necessary to use an average of the price over some years.

The price used in this model was the average gasoline and diesel price excluding VAT for

the last five years in Sweden, taking into account the vehicle categories, and was computed

to be 5.8 SEK/l. Even though the Swedish gasoline and diesel price is a bit on the expensive

side when considering a global perspective. It was concluded that the assumption still gave

adequately accurate output for a global perspective. The diesel price used for the heavy

transports was set as 5.6 SEK/l.

Additionally, the combined average emission from diesel and gasoline for all the vehicle

categories was computed to be 2747 g CO2e/l. Whereas, the emission from diesel was set

to be 2820 g CO2e/l.

For an average electric car, the energy usage used in this report was 0.16 kWh/km. This

was estimated by comparing multiple factual sources. The average electricity usage

7

considering all the vehicle categories was estimated to be 0.24 kWh/km. Additionally, the

average electricity usage for heavy vehicles was calculated to be 1.06 kWh/km.

The technical lifespan of ERS, batteries and pickup apparatuses are assumed to be 20, 8

and 15 years respectively.

To simplify calculations, the exchange rate for 1 USD was set to a constant value of 8 SEK.

Similarly, 1 EUR was assumed to be have an exchange rate of 9 SEK.

Cost of battery in 2015 is assumed to be 350 USD/kWh and battery prices in the future

(after 2020) as 120 USD/kWh.

The real interest rate has been assumed to be 4 % when using the fixed-rate mortgage

method as used by Sweco and the Swedish Transport Administration [17].

1.5.1 Model 1 – World

This model was developed with the aim to get a general idea about which countries and regions in

the world that are the most attractive for ERS solutions. The main questions that the model targeted

to answer were: If a full-scale implementation of ERS was performed globally in combination with

electric vehicles, which countries would currently be the most appealing from primarily an

economic standpoint. Also, comparing the cost of implementing a full-scale ERS for passenger

cars against the cost of converting all passenger cars to pure battery electric cars, which alternative

would be the most economically beneficial? To be able to answer these questions a number of

additional assumptions were found necessary to be made and the most important of these are

presented below. Specific assumptions for each submodel are reported under subheadings.

It was assumed that the ERS solutions are already completely implemented in the countries

and thus, the building phase was not considered. Instead a steady-state case was estimated

where the yearly costs and savings were analyzed.

As the exact distribution for the different motor vehicle types could not be acquired for

each country, the division of the four categories of vehicles for Sweden and their percentile

of vehicle kilometer was assumed to be the same for all the countries in the world.

Consequently, this resulted in the fact that the average distance travelled, fuel consumption,

electricity consumption, fuel price and emission was assumed to be the same as for Sweden

presented in Model 2. This assumption has been made to facilitate the computations as not

enough reliable data could be acquired regarding the exact distribution of the different

categories of vehicles in each country.

Only the land area of the studied countries was used where land area is defined as the sum

of all surfaces delimited by international boundaries and/or coastlines, excluding inland

water bodies (lakes, reservoirs, rivers).

To ensure that the EVs would have sufficiently big batteries and thus would not run out of

charge, it was assumed that the batteries should be able to drive at least the length of a side

of a quadratic grid-mesh, which are the electrified roads.

0.7 SEK/kWh, which is the rough median of the global electricity price, was used as the

average global electricity cost as it was judged to be more accurate than the mean.

Additionally, the average global electricity emission used in the model was 500 g

CO2e/kWh.

8

Comparison – Petroleum-Based Road Transport against ERS and EV

Combination

The cost of a conventional petroleum-based vehicle is estimated to be the same as for an

electric vehicle without the battery and the pick arm apparatus that connects to the

electrified roads. This has been computed by comparing the current vehicle prices.

As a result, the yearly cost of an ERS based transport sector is assumed to be the annual

cost based on life expectancy of the electrified roads, the new batteries, the new pick up

arms and the electricity consumption from all the electric vehicles.

The total yearly cost of the current petroleum-based transport system was assumed to be

the cost of fuel used by the vehicles.

Comparison – Pure Battery Electric Car Fleet against ERS and Electric

Car Combination

In this model, only the passenger cars were examined. Thus, only the conductive road and

inductive road electric road technologies are considered.

It was assumed that the cost of electric passenger cars without the battery is always the

same, regardless of the size of the battery.

The yearly cost of a pure battery electric car based fleet was assumed to be the yearly

battery plus the infrastructure costs.

The cost of the two ERS technologies was assumed to be 1/5 of the normal price, due to

the fact that the ERS would be dimensioned for only passenger cars and thus would require

lower loads. This estimation was made by Elways [18].

The acceptable driving range that an electric car needs to travel to be a viable competitor

against conventional vehicle was assumed to be a bit lower than the average driving

distance for conventional vehicle, which is roughly 600 kilometers. Therefore, for this

model, the minimum acceptable driving range for an electric passenger car was assumed

to be 500 kilometers.

It was assumed that the only infrastructure investment for a pure battery electric car fleet

would be fast chargers.

When it comes to the cost for a fast charger, it was found that the average cost of building

a Tesla supercharging station with five Superchargers is 137 500 USD which translates to

that each supercharger/fast charger would cost roughly 220 000 SEK.

1.5.2 Model 2 – Sweden

The goal of this model was to further investigate how the prospect for the different ERS

technologies is in Sweden. This was done by developing four different submodels that examined

the problem from different perspectives. The first two models were the same as in Model 1, with

chief difference that the input data was optimized for Sweden. The third submodel aimed to

investigate the frequency of electric vehicles per day needed on an electrified road section that

would result in a repayment of the investment. Finally, the last model intended to explore how an

expansion and implementation of ERS could look like in the future. For each model, some

assumptions were found necessary to be made and the most important ones are presented in this

9

chapter. Furthermore, the chief specific assumptions for the last two submodels are reported under

the coming subheadings.

The values from 2015 were used for population, land area, total GHG emissions, yearly

GDP and electricity consumption.

The cost of Nordic electricity mix for the year of 2013 has been assumed, which was 0.355

SEK/kWh.

The input for number of vehicles, including all the vehicle categories, in Sweden year 2015

was found to be roughly 5.9 million. The number of heavy vehicles in Sweden year 2015

was estimated to be about 114 500.

Submodel 3 – Finding the Breakeven Point

The daily investment cost per kilometer for conductive road, overhead conductive and

inductive road electric road technologies were found to be 818 SEK/day, 1226 SEK/day

and 3066 SEK/day respectively and was computed by using the fixed-rate mortgage

method.

Submodel 4 – Expansion

The value for the total kilometer of electrified road needed for a full-implementation for

the three different ERS technologies was assumed to be the values computed using Model

1 for future battery price.

For this model it was assumed that the most heavily trafficked road sections would be the

first to be electrified as they would lead to the largest cost savings.

The model considered the time period from 2016 to 2050. It was assumed that a full

implementation of ERS and a full electric-based transport sector would be attained until,

at the latest, 2050.

The total yearly vehicle kilometer, new vehicles per year and the total number of vehicles

are assumed to always be constant and have the same values as in 2015. These assumptions

were found to be essential in producing the functions relating vehicle kilometer and vehicle

frequency with road length.

The electricity and fuel prices were presumed to be constant, thus no prediction of the

future cost development was made.

The average fuel consumption, average electricity consumption, average fuel and

electricity emissions were assumed to be constant.

The cost for road maintenance was assumed to be 60 SEK/km/day for all ERS technologies.

This cost was obtained from Elways [18], and is derived from the road maintenance cost

of regular roads.

The cost of electrifying the roads for each technology was assumed to be 2.5 times more

expensive than the normal cost for the first 100 km and 1.5 times more expensive for the

first 1000 meters. This was done to mimic that the initial installments would be more

expensive compared to later ones due to economy of scales.

10

2 FRAME OF REFERENCE

This chapter gives a broad picture of the current knowledge in the field of ERS and other relevant

areas. The information presented in this chapter is the base for the modelling and is used as input

parameters for the different models.

2.1 Electric Road Systems

Electric Road Systems (ERS) are in principle electrified roads that are able to perform dynamic

power transfer to the vehicles on the road. Normally an electric engine receives power from an

external power source which has been integrated into the surface of the road. The transmission of

the electrical power is made while the vehicles are in motion in a similar fashion as that for trolley

buses, where the main difference is that ERS-vehicles can connect and disconnect from the power

supply while in motion. Consequently, the roads with installed ERS can be used by both

conventional fossil fuel based vehicles as well as ERS-vehicles. To make ERS-vehicles more

flexible they are often equipped with batteries or smaller internal combustion engines (ICE) so

that they also can be used on conventional roads [10].

The key actors in the industry believe that the ERS technology is technologically feasible and

could be a potential solution in reducing society’s fossil fuel dependency and thus also the

emissions in the transportation sector [10]. Though there are disagreements among experts over

how long a full transition to ERS will take to be realized (anything between 10 to 50 years has

been proposed), there is a consensus that this switch is possible to make. Naturally, the change is

expected to take place progressively, starting with smaller demonstration projects and closed road

systems to gradually incorporating major national and international highways. Several

demonstration projects have already been initiated around the world to explore and evaluate the

different ERS technologies and their full-scale commercial prospective [10]. Even though a

deployment of ERS for road transportation will require huge investments in the infrastructure, a

full-scale implementation could lead to significant advantages compared to the currently existing

fossil fuel dependent transportation system.

One of these advantages obtained would be reduced operation costs due to ERS being more energy

efficient and electricity being cheaper than fossil fuels. Additionally, the electric engines would

reduce the noise generated, which would allow the heavier vehicles to run during off-traffic hours.

Consequently, this would result in decreased congestion and a balancing of the energy demand.

There is also a potential that as electric engines are simpler and lighter than traditional internal

combustion engines, the vehicle maintenance costs would experience a reduction [8].

11

Figure 2. Principal design of an ERS. (Source: [10])

There are essentially three physical ways that a vehicle can be charged while being on the road,

namely from above, the side or from below. Unfortunately, charging from the side is considered

to be too dangerous for pedestrians and bicyclists due to the fact that an arm apparatus would need

to stick out from the side of the vehicle, this approach is not considered as an alternative [19]. As

a result, there are currently in principal three technologies of ERS that are considered in the

industry, namely, conductive power transfer through overhead lines, conductive power transfer

from rails in the road and inductive power transfer through the road [9]. In the coming chapters

these technologies are presented in more detail.

2.1.1 Overhead Conductive Transmission Technology

The overhead contact line technology has existed for many years and is conductive based; in fact

it is the same technology that is used in today’s trains, trams and trolleybuses. Simplified, in such

a system, electricity is continuously transferred from the overhead lines to the vehicle through a

so called pantograph. A pantograph is a component that connects the overhead lines to the vehicle

and can in such way transfer electricity between the overhead line and the vehicle. Along the roads

there are electricity pylons that support the electricity wires. The systems using overhead contact

lines today are commonly closed systems. They usually involve vehicles that travel along a pre-

set path while continuously been connected to the overhead lines and in most cases also are

connected to a rail in the ground [20].

However, when it comes to overhead contact lines for ERS-vehicle applications, it is of utmost

importance that the vehicle should be able to connect to and disconnect from the overhead lines

while moving. Once an ERS-vehicle is disconnected it would automatically switch to a secondary

source for propulsion energy such as a hybrid-diesel engine or batteries [21]. At present, the

technology with overhead contact lines is only supported for large vehicles such as trucks and

hence cannot be used by passenger cars and other low vehicles [13]. This is mainly due to the fact

that height regulations state that the electrified overhead lines on roads need to be above a certain

height from the ground. For example, the Swedish National Electrical Safety Board claim that high

voltage wires need to be located at least 6 meter and low-voltage 5.1 meter above the ground due

to safety reasons. Therefore, using the overhead technology for passenger cars would require the

cars to be equipped with a pickup arm of several meters which would not only be technologically

challenging but a huge safety hazard [20].

12

Figure 3. The picture illustrates a truck driving on an electrified road using overhead lines. (Source: Scania [22])

Traditionally, when the overhead transmission technology is used for trams and trains, the rail is

used to handle the return circuit. But as the ERS lack rails, a two-pole system is needed to be

implemented so that both the power in-feed as well the out-feed can be managed. With this system

it would also be possible to feed the regenerative braking power from the trucks back to the

overhead line and into the energy grid so that it can be used by other vehicles in the system. The

specially designed overhead contact lines are made in a way that ensures that secure energy supply

can be performed for speeds up to 90 km/h [21].

Active Pantograph

The overhead transmission line technology that will be used for the ERS will need to be dynamic

and intelligent, as the vehicles travelling on the electrified roads will need to be able to perform a

number of complex procedures. This involves for example overtaking other vehicles, passing

under bridges and driving over non-electrified parts of the road network, exiting highway etc. Not

to mention the fact that as the vehicles on an electrified road system would not be travelling on a

fixed rail, there would be some lateral displacement of the vehicle on the road as it would be

impossible to travel in a perfectly straight line. Naturally, it is thus of paramount importance to

design a pantograph that is flexible and can intelligently handle different kind of traffic situations

without problems [20]. Such an intelligent pantograph is required to enable continuous electricity

transmission between the overhead line and the vehicle even during high travelling velocities and

when the vehicle is somewhat laterally displaced compared to the transmission line. It also has to

be able to handle vertical changes in the form of bumps or the road being raised due to frozen

ground. Another important aspect of such a pantograph is the need of an automatic search system

that can find the overhead lines when they are available. Several scanning technologies are

available or are being developed to meet this need. It has been suggested that as a traditional

pantograph cost somewhere between 30 000 – 80 000 SEK it is not unlikely to propose that more

intelligent pantograph will have a price range between 120 000 – 240 000 SEK [20].

Even though several companies in the industry have developed overhead conductive technologies,

in this thesis, only the Siemens eHighway overhead conductive concept is studied in detail.

13

Figure 4. Example of an intelligent pantograph developed by Siemens. (Source: [23])

Siemens – eHighway

Siemens has developed the eHighway system that uses the overhead transmission line technology.

It enables hybrid trucks to travel using electricity from overhead contact lines via an active

pantograph, which makes it possible for the trucks to disconnect and connect at speeds up to 90

km/h. Due to the fact that direct transmission is used, it permits the system to have an 80-85 %

well-to-wheel (WTW) efficiency, which is twice as high as that for conventional diesel engine.

The eHighway system also makes it possible to regenerate the breaking energy which further

increases the system WTW efficiency, lowers the emissions and the energy costs [23].

The active pantograph developed for the eHighway system is considered to be one of the key

innovations. Apart from enabling the vehicles to connect and disconnect from the overhead lines,

it furthermore has a specially designed sensor technology that permits the pantograph to

automatically adjust its position to compensate for any lateral movement of the truck compared to

the contact lines. This contrivance also minimizes the wear induced by the pantograph and thus

possibly enables a longer lifecycle [23].

Figure 5. Siemens eHighway system test track in Groß Dölln, Germany. (Source: [24])

14

For the sections on the roads that are not electrified with overhead contact lines, the eHighway

adapted trucks are able to switch to a hybrid drive. There are no restrictions with regards to the

type of hybrid drive that can be used by the trucks. Serial and parallel concepts with internal

combustion engines, battery solutions, fuel cells etc. are all possible to implement [25].

Since the technology is not yet in commercial use, it is difficult to estimate the investment costs

for the technology. However, the consulting firm, Grontmij, stated in their report from 2012 for

the Swedish Transport Administration and the Swedish Energy Agency that the cost of building

such a system would be around 10 million SEK per kilometer. The Swedish Transport

Administration made its own estimation in 2012 that the cost would be somewhere around 6-18

million SEK per kilometer, where electrification at a larger scale would result in costs in the lower

end of the span [26].

2.1.2 Conductive Power Transfer from Road

The conductive power transfer from the road is a newer technology compared to that of overhead

contact lines. It was the French company Alstom with their conductive road transfer technology,

Aesthetic Power Supply (APS), who opened the first tramway system in Bordeaux (2003) which

used conductive road transfer technology. Not only did the APS technology prove that conductive

road transfer is possible, but also exhibited that the technology was safe to use and more

aesthetically pleasing according to some then compared to traditional overhead contact lines. Since

then several cities around the world have incorporated the APS system [27]. In 2015 Alstom

launched SRS, which is an innovative ground-based static charging system for both trams and

electrical buses based on the proven APS technology. Alstom has furthermore been working in

collaboration with Volvo on an ERS that allows continuous conductive power transfer from road

for trucks [28]. The Swedish company Elways has also developed its own technology of

conductive power transfer from road known as Elways. Unlike the ERS developed by Alstom and

Volvo which is designed only for trucks, Elways has instead developed a system that can be

utilized by all types of vehicles [18] . In this thesis, only the conductive road transfer technology

developed by Elways is examined due to time and data restrictions and as such other companies’

conductive road transfer solution are not considered.

Elways

In summary, Elways technology enables electricity transmission from the power grid to rails in

the road. This means that there is a need for a current collector or pickup arm to connect the rails

in the road to the vehicles. To increase the overall safety for humans and animals, the power supply

rails is segmented, similarly to the APS system. For Elways technology, a distance of 50 meters is

electrified at a time. This segment is fed with electricity from a low voltage AC cable with a voltage

of 800 V which is positioned in the close vicinity of the road. The electricity input to the rails in

the road is in turn made via a fast switch box located in the ground. This low voltage cable is then

connected to a medium voltage cable (24 or 36 kV) which is placed next to the low voltage cable

or further away from the road area. The transmission between the medium voltage cable and the

low voltage cable occurs through a transformer station which is positioned every one or two

kilometers. Lastly, the medium voltage cable is connected to the high voltage grid via a transformer

station located every 50 kilometer or so [29].

15

Figure 6. The picture shows how the electrical power is fed from the grid to the vehicle. (Source: [30])

To minimize the risk of accidents due to the high voltages in the rails, Elways have positioned the

conductors in trenches below the surface of the road. Hence, making it near impossible for humans

or animals to reach the electrified part while walking over the rails. Furthermore, as the rails are

only energized in segments of 50 meters at a time, the risk of getting electrocuted is reduced even

further. The system also only supplies electricity to vehicles that are travelling over a certain

velocity; this means that when a vehicle stops the current is likewise turned off. Another factor

that can be a problem for ground based conductive rail systems is the weather. Elways have tested

and proven that small objects such as stones, snow and rain can all be rinsed off the tracks

automatically, using patented solutions, when the traffic intensity is sufficiently high [31]. Several

tests have been performed in all weathers at Arlanda (outside Stockholm) test tracks which show

that the system works satisfactorily [32]. A special add-on plough for a plough car has been

proposed which can take care of the ice and snow in the rails during extreme winter conditions.

Moreover, a heating system in the rails can also be implemented to eliminate this problem [31].

Figure 7. Cross section of the road with a rail in each half and cables buried outside the roadway. (Source: [30])

As with the overhead contact line technology, a key technology in conductive road transmission

is the electricity pickup that is to be used by the vehicles. The pickup arm developed by Elways

has a flexible mechanical construction that enables it to follow the rail even when the vehicle is

not perfectly aligned to the rails. In addition, the pickup arm has a sensor that allows it to

automatically connect or disconnect from the rail when a rail is available or while the vehicle is

overtaking [33]. Transmission of power from the track to the car through the pickup has been

performed for speeds up to 50 km/h. The pickup arm is estimated to cost around 5000-10 000 SEK

and will be constructed to have a lifespan similar to that of vehicles. The system has been proven

16

to be able to conduct voltage and current that together correspond to 200 kW per track, which is

the power necessary to charge trucks [34]. The lifespan of the contacts in the rails are estimated to

have a technical lifespan of approximately 20 years depending on the traffic intensity [18]. Higher

intensity would naturally lead to more frequent replacement of the rail.

Elways has stated that the cost of a large-scale expansion, more than 1000 kilometers, will cost

around 4-5 million SEK per kilometers. It has also been estimated that the construction of the first

100 kilometers will cost 7 to 10 million SEK per kilometers. Additionally, the cost of road

maintenance in the form of cleaning of the rails from dirt, water and snow has been predicted to

be about 60 SEK per day and kilometers [29]. Gunnar Asplund has stated that the well-to-wheel

efficiency of the Elways system will be somewhere between 85-95 % depending on the choice of

the voltage and the quality of the electrical components [35].

2.1.3 Inductive Power Transfer from Road

Inductive ERS use induction principles to transfer electricity wirelessly to moving vehicles, which

means that no mechanical contact is required [36]. Simplified, the inductive power transfer

technology utilizes the AC transformer principle. Figure 8 can be used to describe this principle.

Typical AC transformers, which commonly are used in power distribution systems, have laminated

iron core that lead the magnetic flux from a primary winding through a secondary winding with

miniscule efficiency loss due to loss and leaking. This is displayed in picture A. However, to enable

the inductive power transfer technology, the core is split into two separate parts as shown in picture

B. Consequently, the inductive power transfer by road technology works by having the secondary

winding of the transformer placed in the vehicle while the primary winding is elongated and

installed into the road. This allows magnetic flux to be transferred between the road and the

vehicle, thus enabling continuous power transfer as shown in picture C [8]. To allow the

transmission, also this solution requires a type of pick-up arm, which corresponds to the second

side of the transformer as exemplified in picture C.

Figure 8. The figure illustrates the principle behind the inductive power transfer from road technology. (Source

[37])

17

Today, there are a number of different inductive electric road technologies that have been

developed by enterprises all over the world. Korea is one of the countries that have invested most

heavily into developing the inductive power transfer technology. The KAIST online electric

vehicle project, also known as OLEV, is a well-known inductive road transfer technology that has

been under development for many years. However, in this thesis, only the Bombardier’s primove

Highway inductive technology has been considered and further details of this technology is

presented in the following section.

Primove Highway – Bombardier

The primove Highway system utilizes inductive charging and the primary windings are embedded

into the road in segments of 20 meters. The windings are thereafter covered with asphalt to ensure

that no cables or connections are exposed. The segments are only energized when a certified

primove vehicle transmits proper code to the system. Substations with rectifier stations are used

to connect the segments to the medium voltage power grid at regular intervals (Figure 9).

Furthermore, DC power is distributed to the so called Wayside Power Converters Inverter which

generate the required 20 kHz AC to energize each segment. If a rectifier station were to fail the

system operation would not be affected. Yet, if two or more stations would shut-down, then in this

case the voltage may be too low to sustain power transfer in that section but the system will still

operate. The installation of primove windings in the road are performed by removing a 200 mm

deep and 800 mm wide strip of asphalt. The primove winding is installed in a carrier to maintain

the winding shape which thereafter is fixed to the roadbed and connected to the Wayside power

converter. The installation is similar to installing snow melting cables [8].

Figure 9. This figure illustrates the basis of the primove Highway architecture. (Source [8]).

It has been proven through various testing that power transfer above 150 kW can be performed for

speeds of varying ranges (20-70 km/h). Additionally, it has been demonstrated that a misalignment

of 100 to 150 mm has marginal impact on the power transmission between the road and the pickup

system. A global efficiency of the primove Highway system has been estimated to be somewhere

between 78-88 %. The primove system is designed to operate in all weather conditions and has

18

demonstrated to be inherently insensitive to weather conditions. It has also shown to meet the

regulations and requirements regarding electromagnetic field emissions. Humans are unlikely to

be exposed to high magnetic fields, which are situated close to the pickup under the vehicles, as

the segments are only energized when a vehicle is in motion. Furthermore, shielding and receiver

design are implemented in the vehicles to protect cargo and humans. The immediate area next to

a primove Highway road has been found to have small magnetic fields which are very much below

the standard exposure limits [8].

A total investment cost per kilometer for an inductive ERS between Stockholm and Gothenburg

was computed to be roughly 30 million SEK per kilometer [8]. However, it can be expected that

an implementation on a national scale might push the prices down due to economies of scale.

2.1.4 Stakeholders

In a report published by VTI Viktoria, relevant stakeholders were identified for the ERS [8]. Figure

10, which is derived from the report, displays these major stakeholders.

Figure 10. The major stakeholders in the ERS. (Source [8])

The automotive firms have to develop technical competence and a business model for vehicles

that can be incorporated into the ERS, no matter which power transfer technology that becomes

the standard.

Petroleum firms will continue to be important but will need to handle the fact that they will most

probably become secondary fuel suppliers. Thus, a need for reinvention and implementing

complementing new businesses, such as fast charging and battery swapping stations will most

likely be required.

The Construction firm’s role will be to integrate the electric transfer technologies in the roads as

well as the electric grid in a safe and durable manner. A new market for public-private partnerships

could be a possibility.

ERS

Automotive firm

Road Power Technology

firm

Users

Government and agencies

Petroleum firm

Electric utility

Construction firm

19

Government and agencies will probably have to pay a vital role in facilitating the investments in

ERS. The main motivation for them will be to reduce the environmental impact, oil imports and

increase the energy efficiency. New national and international policies will most likely be required

as loss in oil taxes and currency savings from oils imports will be removed. Also, export of ERS

technologies could become a new market of income.

Users could potentially lower their fuel costs due to increased energy efficiency and the

comparably lower electricity costs (however, the electricity cost are expected to rise). Moreover,

the user could utilize a system that is more environmental friendly.

Road power technology firms could be companies providing the different power transfer

technologies. The exact role in the ERS business model for these companies is yet to be identified.

Lastly, the Power companies producing electricity will become the primary fuel supplier and must

hence also develop and dimension the power grid to be able to handle the increase in the electricity

consumption required. Furthermore, designing and delivering power stations connecting the

electrified roads is a new potential business market in combination with increasing the sales of

electricity for the power utility companies.

20

2.2 Electric vehicles

Electric vehicles, abbreviated as EV, are vehicles that use electricity as their energy source. They

are propelled by one or more electric motors and rechargeable battery packs or other energy storage

devices are used to store the electricity [7]. Electrical cars are not a new invention, as a matter of

fact, EV’s were first introduced more than a 100 years ago. During the early 1900s, electric cars

accounted for around a third of all vehicles on the roads. Due to a number of reasons, mainly

decreasing oil prices and Henry Ford’s mass-produced Model T which reduced the cost of a

petroleum-based vehicle significantly. The result was that the electric vehicles more or less

disappeared by 1935 [38]. Not much advancement was made to the EV technology the following

30 years due to gasoline being cheap and abundant. It was not until the gasoline shortages during

the early 1970s that a new spark of interest was initiated and novel developments were made to

the electric cars. However, the electric cars back then could never really compete with the gasoline-

powered cars due to limited performance. Only having a top speeds of 70 km/h and a travelling

range of just 65 kilometer before needing to be recharged [38].

Figure 11. A Thomas Parker’s electric car from the 1880s. (Source: [39])

The next rise in popularity for the electric vehicles came in the 1990s, this time it was the concern

for the environment that drove the electric vehicles forward. Several new federal and state

regulations in U.S regarding the environment such as the 1990 Clean Air Act Amendment and the

1992 Energy Policy Act, in combination with new transportation emissions regulation, initiated

intense research in many parts of the world. Over the next decades several automobile makers

started producing electric or hybrid cars and, since Tesla joined the market, huge advancement has

been made in the electric vehicle field [38]. Mainly due to advancements in new battery

technology, the plug-in electric vehicle’s range has seen a remarkable increase. The battery

research and development has helped cut electric vehicle battery costs by 50 percent in the last

four years, while simultaneously improving the vehicle batteries’ performance (meaning their

power, energy and durability).

This in turn has led to lower costs for electric vehicles, making them a more affordable option for

consumers. Today, there are over 23 plug-in electric and 36 hybrid models available in all sizes

and shapes from a range of different automobile companies [38]. As of 2015, the total number of

electric vehicles on the road worldwide was 740 000, having increased tremendously the last

couple of years (Figure 12). Furthermore, registrations of new electric cars increased by 70%

21

between 2014 and 2015, with over 550 000 vehicles being sold worldwide in 2015. In seven

countries, namely Norway, the Netherlands, Sweden, Denmark, France, China and the United

Kingdom, the market shares of electric cars rose above 1%. In Norway the market share reached

an astounding 23% and in the Netherlands almost 10 % during 2015. China has emerged as the

main electric vehicle market; its booming electric car sales were larger in 2015 compared to even

the United States [40].

Figure 12. Worldwide number of electric vehicles in use from 2012 to 2015. (Source: [41])

Even though the initial investment might be more for an electric vehicle compared to a

conventional vehicle, a recent study made by the Electric Power Research Institute (EPRI) shows

that the lifetime costs of ownership for an electric vehicle are competitive with conventional

gasoline and hybrid vehicles [42].

Bloomberg New Energy Finance have stated in a recently published study that the electric cars

will by 2022 cost the same as their internal combustion counterparts, which will be the point of

liftoff for EVs. They have also predicted that by 2040 electric vehicles will stand for 35 % of the

global new car sales [43]. The average cost for consumers for the electric cars released during

2015 to early 2016 is about 400 000 SEK, where the median is roughly 300 00 SEK [44].

As the electric cars use electric motors, their tank to wheel efficiency is about 70 %, compared to

conventional gasoline vehicles that only use 15 % - 17 % of the energy stored in the gasoline (

[45], [46]). The driving range of an EV is still considerable less than conventional vehicles and

lies around 100 – 200 kilometer compared to roughly 600 kilometer for a gasoline car [47].

However, a few Tesla models have a range of up to 300 to 480 km [48] but the prices of these

models increase tremendously due to the bigger batteries that are required and usually cost in the

range of one million SEK. Yet, Tesla has announced that the new model 3 which is scheduled to

be released sometime in 2017 will only cost around 300 000 SEK and will boast a driving range

22

of at least 350 kilometers [49]. Similarly to how the energy consumption for petrol and diesel

engine vehicles are given in liter per kilometer, for electric cars the energy usage is given in

kilowatt-hours per kilometer. Although the energy consumption depends a lot on factors such as

car model and manufacturer, by comparing different sources, an average energy consumption of

0.16 kWh/km was obtained for passenger cars ( [50], [51], [52]). The recharging time to fully

recharge a battery pack can take between 4 to 8 hours, while “fast charging” to 80 % of the battery

capacity normally takes 40 minutes.

According to the Bloomberg New Energy Finance (BNEF), for the EVs to achieve widespread

adoption, one of four things need to happen, specifically:

1. Governments need to offer incentives to lower the costs.

2. Manufacturers need to accept extremely low profit margins.

3. Customers need to be willing to pay more to drive electric vehicles.

4. The cost of batteries needs to come down.

Even though the first three points have already partly been happening in the EV market, this

possibly cannot be sustained in the long run. Luckily, the costs of batteries have been falling

rapidly and are thus heading in the desired direction [53].

2.2.1 Batteries

Batteries can be used to power the propulsion on EVs and are one of the key technologies in

electric vehicles. The drastic development in battery technology during the last decade which has

improved the battery performance in power, energy and durability, is one of the major reasons for

EVs increase in popularity [38]. In fact, the battery cost has more than halved between the years

2008 to 2012 and as the batteries account for a third of the cost of building an electric car, this is

a significant development [53]. According to the U.S. Department of Energy (U.S. DOE), the cost

of batteries (excluding warranty cost or profit and assuming a production volume of at least 100

000 batteries annually) have decreased from 1000 USD/kWh in 2008 to 485 USD/kWh in 2012

[54]. Estimation for Internal Combustion Engine (ICE) parity target, according to IEA, is 300

USD/kWh (Figure 13).

23

Figure 13. Estimated Costs of EV Batteries through 2020. (Source: [54])

In 2015, the price for lithium-ion battery packs is reaching 350 USD/kWh as per a study published

by BNEF. They also project that long-range electric cars will cost less than 22 000 USD expressed

in 2016 dollars and the battery costs will be well below 120 USD/kWh by 2030 to further fall in

the future with new chemistries becoming available. Major goals made by Batteries and Energy

Storage subprogram for 2022 include decreasing the battery cost to 125 USD/kWh and increasing

the density from 100 Wh/kg to 250 Wh/kg [55]. Tesla Motors and General Motors have also stated

that they believe that the battery prices might reach around 100 USD/kWh until 2020-2021 [56].

When it comes to the specific energy, meaning how much energy a battery can hold in weight

(W/kg), batteries are still far behind fossil fuels in this aspect. One kilogram of battery delivers

about 120 W, compared to one liter of gasoline that produces roughly 100 times that, about 120

kW of energy. In spite of the fact that the electric motors have a much better efficiency compared

to ICE, the energy storage capability of a battery is still a fraction of that of fossil fuels [57].

Another important aspect for EV batteries is their useful lifespan. It has been widely accepted by

many drivers and prior literature that the EV batteries should be retired after the battery has lost

20 % of its energy storage or power delivery capability. However, a study performed by the

Berkeley Lab found that the travel needs of drivers continue to be met well beyond these levels of

battery degradation [58]. Most EV batteries are guaranteed for 8-10 years or 160 000 km, but

upholding the performance is a challenge as batteries are sensitive to cold and heat, thus degrading

faster in these climates.

24

2.2.2 Fast Chargers

As mentioned earlier, one of the major limitations with batteries is the recharging time. Using

standard chargers, charging can take up to 4 to 8 hours. This is way too long for batteries to be a

viable competitor against petroleum-based fuels. Due to this fact, the EV and battery

manufacturers have developed so called fast chargers that can charge the batteries many times

faster. The faster charging time can be achieved by increasing the output capacity, which for fast

chargers can lie anywhere between 50-150 kW [59]. However, this also requires automakers to

produce cars that can make use of the higher output capacities.

Currently, Tesla’s version of a fast charger, called Supercharger, is considered to be the fastest

charging station in the market. Superchargers consist of multiple chargers that work in parallel and

can thus deliver up to 120 kW of DC power directly to the battery. The car’s onboard computer is

used to constantly monitor the charging process to ensure that peak performance can be

maintained. By applying this method, Tesla’s fast charger is able to charge 80 % of a 90 kWh large

battery in roughly 40 minutes (Figure 14). However, it is important to note that the actual charging

rate can be affected by a number of external factors, such as ambient temperatures, utility grid

restrictions and charging traffic, to name a few [60].

Figure 14. The charging profile of a Tesla Supercharger. (Source: [60])

The cost for building a supercharging station for Tesla is approximately between $100,000 and

$175,000 depending on the station. A significant portion of the costs originate from modifications

that need to be made at the site to support the Superchargers. The construction of Superchargers is

considerably more expensive than putting up standard chargers, this is due to the fact that

Superchargers deliver around five times the power of standard charging stations in the same

amount of time. As a result they are more demanding in terms of infrastructure changes. A property

owner who chooses to acquire a supercharging station is not required to stake any monetary

commitment, but is required to offer up between four or five spaces. This means that there are

normally between 4-5 Superchargers per supercharging station [61]

25

2.3 Road Network in the World

Roads have always been a vital part of human society and have existed in different forms for

thousands of years. Since the invention and employment of motor vehicles, the road network has

undergone a massive expansion and a growing number of roads are being asphalted [62]. Today,

approximately, 64 million kilometer of road network has been laid down in the world [63]. In most

developed countries, the road network is well-connected due to its importance for the

transportation sector. Using Google Maps and the Swedish Transport Administration‘s own maps,

pictures of the road network for major roads could be acquired for different regions of the world.

The major road network in parts of Europe, United States and Scandinavia are illustrated in Figure

15, Figure 16 and Figure 17. From the pictures it can be observed that the global road network on

average is well-linked. Furthermore, as these illustrations only show the major roads, in reality if

other roads were also displayed, the road mesh would in that case be even finer. After studying a

significant number of road maps from various regions of the world, it was concluded that

estimating the global road network as being quadratically meshed is an acceptable assumption that

will not decrease the accuracy of the results notably.

Figure 15. The major road network in orange in United States.

26

Figure 16. The major road network in orange in parts of Europe.

Figure 17. The major road network in orange in Scandinavia.

27

2.4 Fuels

The global transportation energy consumption is dominated by two fuels, namely, motor gasoline

(including ethanol blends) and diesel (including biodiesel blends). This is illustrated in Figure 18

which shows the world’s transportation consumption for all transportation categories. Gasoline is

mainly used for light-duty vehicles such as private cars to transport people whereas diesel is more

commonly used for heavy-duty vehicles such as trucks [64]. Petroleum fuels, which include

gasoline and diesel fuel, are made from crude oil and liquids from natural gas processing [65]. In

this chapter, some information about these fuels is presented.

Figure 18. The world transportation consumption by fuel. (Source: [64])

2.4.1 Gasoline

Gasoline, also known as petrol, is the transportation fuel that is used by most vehicles in the world

(Figure 18). The exact composition and the prices for gasoline vary widely around the world due

to difference in various taxes and subsidies for gasoline [66]. For this thesis, it has been assumed

that all gasoline vehicles in the world use Swedish unleaded 95 Gasoline. All unleaded 95 gasoline

have 5 volume % of ethanol which has very good combustion characteristics and is therefore

ideally suitable as a renewable component in gasoline [67]. The fuel has an energy content of 43.2

MJ/kg and a well-to-wheel life-cycle greenhouse-gas emission of 2700 g CO2e/l [68]. According

to Svenska Petroleum och Biodrivmedel Institutet (SPBI), the average price of gasoline excluding

VAT was 5.11 SEK/l in 2015 [69] and the European commission for February 2016 showed a

weighted average price for the European countries to be roughly 3.50 SEK/l without taxes [70].

However, as the gasoline price has reduced significantly the last year, the gasoline prices during

the last five years in Sweden were considered instead. From this, the average gasoline price

excluding VAT for the last five years was computed to be 5.9 SEK/l.

28

Figure 19. The cost development of gasoline in Sweden excluding VAT from the year 1981 – 2015. (Source: [69])

2.4.2 Diesel

Diesel, as mentioned earlier, is the second biggest fuel source that dominates the road

transportation sector. Even though it is mainly used for heavy-duty vehicles, a lot of light-duty

vehicle such as passenger cars are also known to use it. Due to the same reasons mentioned for

gasoline, it is assumed for this thesis that all diesel-driven vehicles in the world use the Swedish

MK1 diesel [71]. This diesel has an energy content of 42.9 MJ/kg and a well-to-wheel life-cycle

GHG of 2820 g CO2e/l [72]. For the year of 2015 the cost of diesel excluding taxes was

approximately 4.6 SEK/l [69] and 3.5 SEK/l was the weighted average for the European countries

in February 2016 [70]. Similarly to gasoline prices however, the diesel prices have also decreased

significantly the last year. To get a better picture, the diesel prices during the last five years were

considered and thus, an average price of 5.6 SEK/l excluding VAT was obtained.

0

1

2

3

4

5

6

7SE

K/L

YEAR

Cost of Gasoline without VAT

29

Figure 20. The cost development of diesel excluding VAT in Sweden between the years 2001 to 2015. (Source: [69])

2.5 The Electricity Market

In 2013, the world’s total final energy usage was 108 171 TWh [73] where electricity accounted

for 18 % of the usage as seen in Figure 21. Consequently, electricity is highly coveted due to its

high exergy value and easy to trade flexibility, making it a big business. In many instances,

electricity is generated by a power company that in the end does not deliver to the end-users.

Instead, the produced electricity is repeatedly bought and re-sold a number of times before finally

being used [74]. This is the case for a so called wholesale electricity market which a majority of

the world’s countries operate under [75].

Figure 21. World total final energy usage by source for 2013. (Source: [73])

0

1

2

3

4

5

6

7

8SE

K/L

YEAR

Cost of Diesel without VAT

Price w/o. VAT

30

2.5.1 Global Electricity Mix

As illustrated by Figure 22, the world’s electricity production for 2014 was 22 433 TWh. By far

the largest energy sources for the electricity were fossil-fuels which corresponded to 66 % of the

total electricity mix. Naturally, due to the high percentage of fossil fuels used, the GHG-emissions

for electricity production are quite high when looking at a global perspective. According to the

International Energy Agency, the average world CO2e emission for the years 2005-2007 for

electricity production was about 504 g CO2e/kWh [76]. Using the data acquired from IPCC studies

regarding the life cycle global warming potential of selected electricity sources [77], a naive model

was made by the author which also gave similar result of approximately 500 g CO2e/kWh for the

world’s electricity production.

Figure 22. World electricity generation from all energy sources in 2014. (Source: [78])

When it comes to the electricity prices, they can vary widely between the countries but also within

a country itself depending on different factors such as infrastructure and geography (Figure 23). It

is mainly the selection of fuels used to generate electricity that decides the cost of the electricity

prices. This is certainly true for Italy, which at the moment has the highest electricity cost in the

world. Due to the fact that Italy choose not to build any nuclear power plants, their electricity

generation mix consists primarily of fossil fuel sources which are more expensive compared to

nuclear or hydroelectric power plants. On the flipside, Sweden enjoys some of the cheapest

electricity in the world due to having mainly nuclear and hydro power as their primary electricity

generation sources [79]. Using the data from Figure 23, a rough world average for the electricity

price per kilowatt hour excluding taxes was calculated to be 0.8 SEK/kWh and the median as 0.7

SEK/kWh.

31

Figure 23. Electricity prices in U.S. dollar cents per kilowatt hour excluding taxes. (Source: [79])

2.5.2 Nordic Electricity Mix

In 1996, the Swedish electricity market underwent a deregulation that divided the market into three

different parts. These three parts are electricity production, distribution and electricity sales. The

electricity that is produced is sold, most commonly through the power market place Nord Pool

where Sweden, Norway, Finland and Denmark are involved, to electricity sales companies who in

turn re-sell the electricity to the end consumers. Large customers, such as big industries, sometimes

buy electricity directly from an electricity production company. Moreover, the electricity

distribution companies are responsible for providing the power grids for a certain geographical

area which results in them having monopoly over said area. This is therefore the part of the

electricity market that is still regulated in Sweden, the purpose of deregulating the electricity

market was to increase the competition in the electricity market, giving the customers more choices

while consequently decreasing the prices [80].

The electricity trading between the countries involved in Nord Pool, occurs one day in advance.

Thus, the prices for the coming day are set one day prior. The electricity producers bid on the price

that they are willing to sell their electricity for and the electricity sales companies in turn bid on

the prices they are willing to buy for. Where the supply and demand meets, the price is set,

similarly to any other commodity market [81]. The so called marginal pricing is used when prices

are set for the electricity produced per hour. This means that the price for all electricity produced

for a particular hour is the price at the highest accepted producing bid. Consequently, this results

in the fact that the electricity produced during that hour will be paid at the cost to produce power

from the most expensive power source. Hence, power sources with low marginal costs, which are

used for base load e.g. nuclear and hydro power, are used first, whereas power sources with high

marginal cost such as oil- and gas-driven turbines are used only when the power demand is very

32

high. The price of electricity as a result differs over the span of a trading day for the electricity

companies [81].

Figure 24. Power production by source in the Nordic region in 2013. (Source: [82])

The electricity production in the Nordic region is dominated by hydropower which stands for 53

% of the total power generation followed by nuclear power at 23 % (Figure 24). As only 12 % of

the electricity production stems from fossil fuels the Nordic electricity mix produces low

greenhouse gas emissions. In the year of 2013 the Nordic countries together use 380.5 TWh, where

Sweden consumed 137 TWh. Naturally, the exact share of the electricity mix fluctuates between

different years depending on various factors such as weather conditions, water availability and

availability of the energy sources [81]. However, for this thesis, the Nordic electricity mix for the

year of 2013 has been assumed. The average spot price for electricity in Nord Pool was 38.1

euro/MWh which roughly corresponds to 0.355 SEK/kWh. The GHG emissions for Nordic

electricity mix is roughly 90 g CO2e/kWh [83] and for European electric mix, it is approximately

400 g CO2e/kWh [84].

33

3 MODELS

Two models were produced in order to investigate the prospect of different electric road

technologies from a global and a Swedish perspective. This was accomplished through comparing

the three main ERS technologies against each other, against the conventional petroleum-based

vehicle fleet and against a pure electric passenger car fleet. Several submodels were hence required

to be created for each model to produce these comparisons. Additionally, a model simulating an

expansion of ERS in Sweden using different scenarios was also produced.

In Figure 25, the two models and their accompanying submodels are presented in an illustrative

manner. The figure also displays the thesis objectives that the different models intended to answer.

As can be seen, Model 1 and its submodels aimed to investigate the first thesis objective (as

presented in chapter 1.2) from a global perspective. Similarly, submodel 1 and 2 in Model 2

intended to study the same thesis objective but from a Swedish perspective. For all these model,

two battery cost scenarios were scrutinized, the current battery pricing and a projected future

battery pricing. It is important to note that the only input changed between the scenarios was the

battery pricing. Lastly, submodel 3 and 4 were produced to explore the second purpose question

which aimed to examine a possible ERS expansion in Sweden.

Figure 25. An illustrative flow chart over the two models with accompanying submodels produced to investigate the

thesis objectives.

In the following sections, the main building blocks for each of the models introduced and the

results they yield are described in detail.

34

3.1 Model 1 – World

The first model was produced to investigate two different questions. Firstly, it was to identify

countries and regions in the world where implementing, on a full-scale, different ERS technologies

in combination with electric vehicles would result in savings compared to the current petroleum-

based transportation system. For this model, one of the most important assumptions made was that

the cost of a conventional petroleum-based vehicle was estimated to be equal to an electric vehicle

without the battery and the pick-up arm apparatus which connects to the electrified roads. As a

result, in a steady state case where the ERS solutions are already completely implemented in the

countries, the cost of an electric vehicle excluding the battery and the pickup arm apparatus and

the cost of a conventional vehicle could be neglected from the cost calculations. Hence, the yearly

cost of an ERS was the annual cost of the electrified road investment, the new batteries, the new

pick-up arms and the electricity usage from all the electric vehicles. Similarly, the yearly cost for

the conventional vehicles in the current petroleum-based transport system would thus only be the

cost of fuel used by the vehicles. By comparing these costs, it was possible to conclude whether

an implementation of the different technologies of ERS would result in savings or not for all the

studied countries.

The second question that the model aimed to investigate was to compare the cost of implementing

a full-scale ERS for passenger cars against the cost of converting all passenger cars to pure battery

electric cars. Also for this model, an assumption was made that the cost of an electric passenger

car without the battery is the same regardless of the size of the battery. The yearly cost of an ERS

was in this case the annual cost of the electrified road investment, the new batteries and the new

pick-up arms. The yearly cost of a pure battery electric car based transportation sector would then

be the cost for new batteries plus the annual infrastructure costs.

The thought process behind these models is explained in more detail in the impending sections.

However, to model both questions the first step was to find the optimal length of electrified road

needed in each country.

3.1.1 Computing the Optimal Electrified Road Length

The basic idea behind the methodology to find the optimal electrified road length was to divide

the land area of the studied countries into quadratic grid-meshes. The meshes symbolize the

electrified roads and the space in-between implies areas for battery drive. A naive example of how

such a segmentation could look like for Germany is illustrated in Figure 26. As can be seen, the

red grid-meshes represent the road areas that need to be electrified and the blue gridlines inside

represent roads where the vehicles will use batteries as their energy source. In this model, it was

assumed that the road system of each country is meshed in a quadratic manner as stated in the

assumption chapter. Hence, the fundamental idea was that the main factors that affect how much

of the road that needs to be electrified is the cost of batteries compared to the cost of constructing

ERS. Consequently, the optimal size of the grid-meshes, which is represented by ∆ in the figure,

could be found by optimizing these two factors.

35

Figure 26. Example of how a sectioning of Germany into quadratic road sections with ERS installation can be

conceptualized (Note that the meshed road sections would in reality only be on the land area).

The method of optimizing the size of the quadratic grid ∆ used for this model was mainly

developed by Gunnar Asplund from Elways and was further modified in this thesis. It is based on

the fact that the bigger the size of the electrified road grid-mesh is, the cheaper the ERS installation

will be in the country as the length of electrified road needed would decrease. However, this will

also lead to a need for bigger batteries, due to a longer driving range requirement, which in turn

would result in increased battery costs. Consequently, by balancing the length of the electrified

road and the battery size required, we could thus obtain the optimal grid-size ∆ for each country.

In the following paragraphs, this method is explained further.

If we consider only one square in the quadratic grid-mesh and assume that the side of the square

is 𝑥, as illustrated in Figure 27, it can be concluded that the area of this square would hence be 𝑥2.

From this, using the land area of an arbitrary country, the number of squares 𝑛𝑠𝑞𝑢𝑎𝑟𝑒 with length

𝑥 that fit in that country could then be expressed using Equation (1).

𝑛𝑠𝑞𝑢𝑎𝑟𝑒 =

𝐿𝑎𝑛𝑑 𝐴𝑟𝑒𝑎

𝑥2

(1)

36

Figure 27. Viewing one square in the quadratic grid-mesh.

Similarly, the circumference of the square, which in reality would be the length of the electrified

road, can be expressed as 4𝑥. This is true when considering only one square. However, when

considering several squares together, it is quite clear that the circumference of each square would

be less than 4𝑥 as they overlap. It was found, through experimenting, that when an area is meshed

very finely into quadratic grid-meshes. The circumferential length of the squares can be

approximated as 2𝑥 instead of 4𝑥. This conclusion can be clarified with the help of Figure 28. In

the figure, a grid of squares was produced by adding only two sides of a square at a time. At first,

doing this for only one square led to a big error as half the sides of the grid were not considered.

But as one continued to go towards bigger 𝑛, the sides of the grid not considered became

increasingly smaller compared to the rest of the grid. Hence, it was derived from the table that the

sides neglected by this method increased linearly whereas the sides considered increased

quadratically. As such, it was found to be a fair assumption to disregard the outer sides of the grid-

mesh as 𝑛 → ∞ and approximate the length of a square in a large grid as 2𝑥. This was further

supported by the fact that in reality the borders of countries, which correspond to the neglected

sides, usually do not have roads.

Figure 28. Explanation of why the circumferential length of a square in a large grid-mesh was approximated as 2x.

Consequently, using this information the length (L) of 𝑛𝑠𝑞𝑢𝑎𝑟𝑒, which relates to the total length of

the electrified road system, could be obtained by applying Equation (2).

𝐿(𝑥) = 2𝑥 ∙ 𝑛𝑠𝑞𝑢𝑎𝑟𝑒 = 2𝑥 ∙

𝐿𝑎𝑛𝑑 𝐴𝑟𝑒𝑎

𝑥2=

2 ∙ 𝐿𝑎𝑛𝑑 𝐴𝑟𝑒𝑎

𝑥

(2)

37

From this, as the cost of constructing the different ERS technologies per kilometers was known,

the cost of constructing an ERS technology could thus be expressed as a function of 𝑥 as shown in

Equation (3) and (6).

Furthermore, by computing the car density in a country the cost of installing an ERS technology

could instead be expressed in cubic kilometer of road cost per vehicle (SEK/km2/vehicle) using

the known inputs as shown in Equation (4) and (5).

𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 =

𝑁𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠

𝐿𝑎𝑛𝑑 𝐴𝑟𝑒𝑎

(4)

𝐶𝑜𝑠𝑡 𝑝𝑒𝑟 𝑘𝑚2 𝑔𝑟𝑖𝑑 𝑝𝑒𝑟 𝑣𝑒ℎ𝑖𝑐𝑙𝑒 (𝑘2) = 2 ∙ 𝐶𝑜𝑠𝑡 𝐸𝑅𝑆 ∙

𝐿𝑎𝑛𝑑 𝐴𝑟𝑒𝑎

𝑁𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠

(5)

The cost per km2 per vehicle was then defined as 𝑘2 and the function ERS(x) was simplified to the

following:

Going back to Figure 27, the battery cost could similarly be expressed as a function of 𝑥. This was

achieved by considering an electric vehicle inside the electrified road mesh. From this it was

concluded that the theoretical maximum travelling range required from a battery would be 1

2𝑥.

However, to ensure that the battery charge would not be depleted mid-drive, an extra driving range

factor was added and could be chosen as preferred. Consequently, as the battery cost in SEK/km

was known and the average electricity consumption of electric vehicles in kWh/km could be

computed using the method described later in Model 2. The cost of battery per kilometer and car

(SEK/km/car), denoted as 𝑘1, could thus be computed using Equation (7).

𝑘1 = 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝐸𝑉 ∙ 𝐶𝑜𝑠𝑡 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 ∙ 𝐸𝑥𝑡𝑟𝑎 𝑅𝑎𝑛𝑔𝑒 (7)

However, as the ERS building costs and the battery costs needed to be expressed in terms of yearly

costs for the coming calculations, there were a number of steps that were required to be taken. To

begin with, the economic lifespan had to be estimated.

The economic lifespan of a product is difficult to predict as it depends on a number of

unpredictable factors, this is especially true for a product that does not actually exist on a

commercial scale yet. Thus, in this report, the economic lifespan was computed using the technical

lifespan of the products and the fixed-rate mortgage method. The fixed-rate mortgage method takes

into consideration both the interest and the amortization of an investment. By applying the fixed-

rate mortgage equation in combination with the technical lifespan on an arbitrary cost, the

economic lifespan was obtained as shown in the subsequent steps.

𝐸𝑅𝑆(𝑥) = 𝐶𝑜𝑠𝑡 𝐸𝑅𝑆 ∙ 𝐿(𝑥) → 𝐸𝑅𝑆(𝑥) = 𝐶𝑜𝑠𝑡 𝐸𝑅𝑆 ∙

2 ∙ 𝐿𝑎𝑛𝑑 𝐴𝑟𝑒𝑎

𝑥

(3)

𝐸𝑅𝑆(𝑥) =

𝑘2

𝑥

(6)

38

The fixed-rate mortgage equation can be expressed as:

𝑃𝑎𝑦𝑏𝑎𝑐𝑘 = 𝐿 ∙

𝑟(1 + 𝑟)𝑛

(1 + 𝑟)𝑛 − 1

(8)

Where,

L = Loan amount from the beginning

r = Interest in decimal form, either annual or monthly rate

n = Number of payment occasion

Converting this equation to apply to our models resulted in Equation (9).

𝐶𝑜𝑠𝑡𝑦𝑒𝑎𝑟𝑙𝑦 = 𝐴𝑏𝑟𝑖𝑡𝑎𝑟𝑦 𝐶𝑜𝑠𝑡 ∙

𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 ∙ (1 + 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 )𝑇𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛

(1 + 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 )𝑇𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 − 1

(9)

From this the economic lifespan was obtained to be:

𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 =

𝐴𝑟𝑏𝑖𝑡𝑎𝑟𝑦 𝐶𝑜𝑠𝑡

𝐶𝑜𝑠𝑡𝑦𝑒𝑎𝑟𝑙𝑦

(10)

Once the economic lifespan was computed for the different ERS technologies, battery and pick-

up arm, these were then related to the economic lifespan of ERS. Doing this, the total cost for

battery and pick-up arm could be computed for a period that corresponded to the ERS economic

lifespan. Coefficients that correlated the battery and the pick-up arms economic lifespan to the

ERS economic lifespan were derived as shown in Equation (11) and (12).

𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑡𝑜 𝐸𝑅𝑆 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 =

𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 𝐸𝑅𝑆

𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 𝐵𝑎𝑡𝑡𝑒𝑟𝑦

(11)

𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡𝑃𝑖𝑐𝑘𝑢𝑝 𝑎𝑟𝑚 𝑡𝑜 𝐸𝑅𝑆 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 =

𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 𝐸𝑅𝑆

𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 𝑃𝑖𝑐𝑘𝑢𝑝 𝐴𝑟𝑚

(12)

Coming back to defining the battery equation, the battery lifespan coefficient was combined with

𝑘1 to take into account that the battery economic lifespan is shorter than the ERS lifespan. Thus,

the batteries would be required to be changed more frequently than ERS. This resulted in a new

definition for 𝑘1 and the “Cost battery” factor as demonstrated in Equation (13) and (14).

𝐶𝑜𝑠𝑡 𝐵𝑎𝑡𝑡𝑒𝑟𝑦𝐸𝑅𝑆 𝑙𝑖𝑓𝑒𝑠𝑝𝑎𝑛 = 𝐶𝑜𝑠𝑡 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 ∙ 𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑡𝑜 𝐸𝑅𝑆 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 (13)

𝑘1 = 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝐸𝑉 ∙ 𝐶𝑜𝑠𝑡 𝐵𝑎𝑡𝑡𝑒𝑟𝑦𝐸𝑅𝑆 𝑙𝑖𝑓𝑒𝑠𝑝𝑎𝑛 ∙ 𝐸𝑥𝑡𝑟𝑎 𝑅𝑎𝑛𝑔𝑒 (14)

Finally, the equation for the battery cost as a function of 𝑥 could be derived as shown below.

𝐵(𝑥) = 𝑘1𝑥 (15)

39

It is important to note however, that the cost of battery is not entirely linear in reality as the

difference in cost of producing a small size and a big size battery is not linear. However, for the

sake of simplification and due to the fact that the price of a battery is usually given in SEK/kWh

in literature, a linearization of the problem was judged not only to be acceptable but also necessary.

In the next step, by merging Equations (15) and (6), a function was obtained that displayed how

the combined cost of ERS installation and batteries varied with increasing 𝑥 (Equation (16)). Thus,

the fundamental function that the modelling is based on was obtained. In Figure 29, the cost

development as a function of 𝑥 is illustrated in an imaginary manner for batteries, ERS installation

and the combination of both these factors. The combined function, denoted as 𝑓(𝑥), was found to

have a minimum where the length 𝑥 of the electrified roads and the battery size needed was

optimized in a manner that led to an overall lowest cost.

𝑓(𝑥) = 𝑘1𝑥 +

𝑘2

𝑥

(16)

Figure 29. Cost development as a function of length x for battery, ERS installation and the combination of these two

factors.

The modelling function was derived and thus the value of 𝑥 where the combined cost was at its

minimum was computed. The procedure is presented below:

𝑓′(𝑥) = 𝑘1 −

𝑘2

𝑥2

(17)

Inserting zero instead of 𝑓′(𝑥), the value of 𝑥 which corresponds to the minimum cost could be

found as shown in Equation (18).

40

0 = 𝑘1 −

𝑘2

𝑥2 → 𝑥2 =

𝑘2

𝑘1 → 𝑥 = ±√

𝑘2

𝑘1

(18)

The negative solution was discarded, as it was known that costs always are positive, this finally

resulted in that the optimization equation was derived.

𝑥𝑜𝑝𝑡𝑖𝑚𝑎𝑙 = √

𝑘2

𝑘1

(19)

Once the 𝑥𝑜𝑝𝑡𝑖𝑚𝑎𝑙 was computed, the rest of the parameters required to answer the modelling

questions could then be calculated. The computation steps for the most important parameters are

presented below.

The battery size necessary for the optimal grid-mesh was computed as shown in Equation (20),

where extra driving range was added due to the fact mentioned previously. Likewise, the total cost

of batteries for each vehicle in the country for a period that corresponds to the economic lifespan

of ERS was then calculated using Equation (21).

𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑆𝑖𝑧𝑒 = 𝑥𝑂𝑝𝑡𝑖𝑚𝑎𝑙 ∙ 𝐸𝑥𝑡𝑟𝑎 𝑅𝑎𝑛𝑔𝑒 ∙ 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝐸𝑉 (20)

𝑇𝑜𝑡𝑎𝑙 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐶𝑜𝑠𝑡 = 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑆𝑖𝑧𝑒 ∙ 𝑁𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠 ∙ 𝐶𝑜𝑠𝑡 𝐵𝑎𝑡𝑡𝑒𝑟𝑦𝐸𝑅𝑆 𝑙𝑖𝑓𝑒𝑠𝑝𝑎𝑛 (21)

The total length 𝐿(𝑥𝑂𝑝𝑡𝑖𝑚𝑎𝑙) and the cost of the grid 𝐸𝑅𝑆(𝑥𝑂𝑝𝑡𝑖𝑚𝑎𝑙) needed could thus be

calculated using the following equations for each country. Additionally, the size of the grid was

compared with the existing real road network in each country.

𝐿(𝑥𝑂𝑝𝑡𝑖𝑚𝑎𝑙) = 2 ∙

𝐿𝑎𝑛𝑑 𝐴𝑟𝑒𝑎

𝑥𝑂𝑝𝑡𝑖𝑚𝑎𝑙

(22)

𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑠𝑡 𝑜𝑓 𝐺𝑟𝑖𝑑 = 𝐿(𝑥𝑂𝑝𝑡𝑖𝑚𝑎𝑙) ∙ 𝐶𝑜𝑠𝑡 𝐸𝑅𝑆 (23)

% 𝑜𝑓 𝐸𝑥𝑖𝑠𝑡𝑖𝑛𝑔 𝑅𝑜𝑎𝑑 𝑁𝑒𝑡𝑤𝑜𝑟𝑘 =

𝐿(𝑥𝑂𝑝𝑡𝑖𝑚𝑎𝑙)

𝐸𝑥𝑖𝑠𝑡𝑖𝑛𝑔 𝑅𝑜𝑎𝑑 𝑁𝑒𝑡𝑤𝑜𝑟𝑘 𝐿𝑒𝑛𝑔𝑡ℎ

(24)

Furthermore, using the cost of a pickup arm that varies between the different ERS technologies.

The cost of installing a pickup arm in each vehicle for a period that corresponds to the economic

lifespan of ERS was computed through using Equation (25).

𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑠𝑡 𝑜𝑓 𝑃𝑖𝑐𝑘𝑢𝑝 𝐴𝑟𝑚𝑠 = 𝑁𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠 ∙ 𝐶𝑜𝑠𝑡 𝑃𝑖𝑐𝑘𝑢𝑝 𝐴𝑟𝑚 (25)

41

The yearly costs were the most interesting as they were used to answer the modelling questions,

the total investment costs of battery, pick arm and grid were divided by the economic lifespan of

ERS (Equations (26), (27) and (28)).

𝑌𝑒𝑎𝑟𝑙𝑦 𝐺𝑟𝑖𝑑 𝐶𝑜𝑠𝑡 =

𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑠𝑡 𝑜𝑓 𝐺𝑟𝑖𝑑

𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 𝐸𝑅𝑆

(26)

𝑌𝑒𝑎𝑟𝑙𝑦 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐶𝑜𝑠𝑡 =

𝑇𝑜𝑡𝑎𝑙 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐶𝑜𝑠𝑡

𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 𝐸𝑅𝑆

(27)

𝑌𝑒𝑎𝑟𝑙𝑦 𝑃𝑖𝑐𝑘𝑢𝑝 𝐴𝑟𝑚 𝐶𝑜𝑠𝑡 =

𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑠𝑡 𝑜𝑓 𝑃𝑖𝑐𝑘𝑢𝑝 𝐴𝑟𝑚𝑠

𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 𝐸𝑅𝑆

(28)

3.1.2 Comparison – Petroleum-based Road Transport against ERS and

EV Combination

Once the optimal grid-mesh size was computed, the annual costs for an ERS based road transport

sector could be produced. The parameters required to find out which countries in the world that

would save money by implementing different technologies of ERS could then be computed. As

mentioned previously, due to the assumption that the cost of a conventional petroleum-based

vehicle is the same as the cost of an electric vehicle without the battery and the pickup arm

apparatus, the yearly savings or losses of an ERS based transport sector compared to the current

petroleum-based system could be calculated. This was achieved by computing the difference

between the annual ERS system plus the electricity consumption costs from the EVs and the

petroleum fuel costs in the current system. The procedure is explained in more detail in the coming

paragraphs.

To begin with, the annual electricity cost of a hundred percent electric based transportation sector

was computed by applying Equation (29) and (30). Where a weighted average distance travelled

and average electricity consumption taking into account the different vehicle categories was

computed using the method explained in Model 2.

𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑙. 𝑈𝑠𝑎𝑔𝑒 = 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑇𝑟𝑎𝑣𝑒𝑙𝑙𝑒𝑑 ∙ 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐸𝑙. 𝑈𝑠𝑎𝑔𝑒 ∙ 𝑁𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠 (29)

𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑙. 𝐶𝑜𝑠𝑡 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑙. 𝑈𝑠𝑎𝑔𝑒 ∙ 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑡𝑦 𝐶𝑜𝑠𝑡 (30)

To get a general idea about how much more electricity that would be needed to be produced in

each country compared to now, Equation (31) was used.

% 𝑜𝑓 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑡𝑦 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 =𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑙. 𝑈𝑠𝑎𝑔𝑒

𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛

(31)

42

Similarly, the annual fuel cost was computed for a hundred percent fossil-fuel based vehicle fleet

using Equation (32) and (33). The fuel price used was also a weighted average.

𝐴𝑛𝑛𝑢𝑎𝑙 𝐹𝑢𝑒𝑙 𝑈𝑠𝑎𝑔𝑒 = 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑇𝑟𝑎𝑣𝑒𝑙𝑙𝑒𝑑 ∙ 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐹𝑢𝑒𝑙 𝑈𝑠𝑎𝑔𝑒 ∙ 𝑁𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠 (32)

𝐴𝑛𝑛𝑢𝑎𝑙 𝐹𝑢𝑒𝑙 𝐶𝑜𝑠𝑡 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝐹𝑢𝑒𝑙 𝑈𝑠𝑎𝑔𝑒 ∙ 𝐹𝑢𝑒𝑙 𝑃𝑟𝑖𝑐𝑒 (33)

Finally, by subtracting the annual fuel costs and the combined annual costs of electricity, batteries,

pick-up arms and the electrified roads, the yearly savings/losses that an ERS would result in for a

country was obtained.

𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑅𝑆 𝐶𝑜𝑠𝑡 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑙. 𝐶𝑜𝑠𝑡 + 𝑌𝑒𝑎𝑟𝑙𝑦 𝐺𝑟𝑖𝑑 𝐶𝑜𝑠𝑡 + 𝑌𝑒𝑎𝑟𝑙𝑦 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐶𝑜𝑠𝑡 (34)

Where,

𝑌𝑒𝑎𝑟𝑙𝑦 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐶𝑜𝑠𝑡 = 𝑌𝑒𝑎𝑟𝑙𝑦 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐶𝑜𝑠𝑡 + 𝑌𝑒𝑎𝑟𝑙𝑦 𝑃𝑖𝑐𝑘𝑢𝑝 𝐴𝑟𝑚 𝐶𝑜𝑠𝑡

Thus we get,

𝑌𝑒𝑎𝑟𝑙𝑦 𝑆𝑎𝑣𝑖𝑛𝑔𝑠 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝐹𝑢𝑒𝑙 𝐶𝑜𝑠𝑡 − 𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑅𝑆 𝐶𝑜𝑠𝑡 (35)

From this, the countries where implementing ERS would result in savings could hence be

identified and sorted. From this the total number of vehicles in these countries could be computed

and related to the total number of vehicles in the world using Equation (36).

% 𝑜𝑓 𝑇𝑜𝑡𝑎𝑙 𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠 =

𝑁𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑎𝑟𝑠 𝑖𝑛 𝐶𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠 𝑤ℎ𝑒𝑟𝑒 𝑌𝑒𝑎𝑟𝑙𝑦 𝑆𝑎𝑣𝑖𝑛𝑔𝑠 > 0

(36)

Similarly, the yearly GHG emissions that can be reduced if the ERS solution were to be

implemented was summed for each country where the yearly savings are positive. In addition, the

reduction in yearly GHG emission was then compared to the total yearly GHG emissions in the

countries/globally by implementing Equation (37).

% 𝑅𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑇𝑜𝑡𝑎𝑙 𝐺𝐻𝐺 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 =

𝐺𝐻𝐺 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑟𝑒𝑑𝑢𝑐𝑒𝑑

𝑇𝑜𝑡𝑎𝑙 𝐺𝐻𝐺 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛

(37)

To get an idea about how much the total ERS investment cost would be related to the studied

country’s GDP, Equation (38) was applied.

% 𝑜𝑓 𝑌𝑒𝑎𝑟𝑙𝑦 𝐺𝐷𝑃 =

𝑇𝑜𝑡𝑎𝑙 𝐸𝑅𝑆 𝐶𝑜𝑠𝑡

𝑌𝑒𝑎𝑟𝑙𝑦 𝐺𝐷𝑃

(38)

This whole modelling procedure was then performed for each of the three studied ERS

technologies with two different pricing for batteries, namely the current battery price and the

predicted future battery price.

43

3.1.3 Comparison – Pure Battery Electric Car Fleet against ERS and

Electric Car Combination

In this part of the model, the question to be answered was whether the cost of having a 100% pure

battery electric passenger car fleet was more expensive than implementing the combination of ERS

with electric passenger cars. It should be noted that for this comparison, only the inductive road

and conductive road technology were studied as the overhead conductive technology is not

applicable for passenger cars. In the same way as for the comparison between the petroleum-based

transport sector and ERS, the first step was to compute the optimal grid-size. However, as only the

passenger cars were to be compared in this part of the model, there were a couple of changes that

were implemented. To begin with, the ERS was to be used only by passenger cars. It would be

dimensioned for only electric cars, which in turn would lead to cheaper construction costs. It was

estimated that the cost per kilometer for an ERS that was dimensioned for only passenger cars

would be only 1/5 of the standard price. Thus, the standard costs of the different ERS technologies

were reduced using Equation (39).

𝐶𝑜𝑠𝑡 𝐸𝑅𝑆 𝑂𝑛𝑙𝑦𝐶𝑎𝑟𝑠 =

1

5∙ 𝐶𝑜𝑠𝑡 𝐸𝑅𝑆

(39)

Additionally, as the input data collected regarding the number of vehicles in each country was for

all categories of motor vehicles, the number of vehicles in each country was multiplied by a

percentage coefficient (from Sweden) to obtain the number of passenger cars.

𝑁𝐶𝑎𝑟𝑠 = 𝑁𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠 ∙ 𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝐶𝑎𝑟𝑠 (40)

After performing these changes, the rest of the calculations to obtain the yearly ERS costs were

the same as described previously.

Once the yearly ERS costs were computed, the yearly cost of a pure battery electric based car fleet

was also computed. This was done, to start with, by defining the acceptable minimum driving

range that would be required from the battery electric cars so that they could compete against

conventional passenger cars. From this, the size of the battery could thus be calculated as shown

in Equation (41). Consequently, the cost of all the batteries that would be needed for a time period

that coheres to the ERS economical lifespan could then be equated by applying Equation (42) for

each country. Lastly, by using Equation (43), the yearly cost of batteries could be attained.

𝐸𝑉 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑆𝑖𝑧𝑒 = 𝐴𝑐𝑐𝑒𝑝𝑡𝑎𝑏𝑙𝑒 𝐷𝑟𝑖𝑣𝑖𝑛𝑔 𝑅𝑎𝑛𝑔𝑒 ∙ 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝐸𝑉 (41)

𝑇𝑜𝑡𝑎𝑙 𝐸𝑉 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐶𝑜𝑠𝑡 = 𝐸𝑉 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑆𝑖𝑧𝑒 ∙ 𝐶𝑜𝑠𝑡 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 ∙ 𝑁𝐶𝑎𝑟𝑠 (42)

𝑌𝑒𝑎𝑟𝑙𝑦 𝐸𝑉 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐶𝑜𝑠𝑡 =

𝑇𝑜𝑡𝑎𝑙 𝐸𝑉 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐶𝑜𝑠𝑡

𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 𝐸𝑅𝑆

(43)

To somehow also include the infrastructure costs that will occur in a complete electric car based

transportation sector, the cost of installing fast chargers, using the assumption for cost and

frequency per car presented in Appendix C, was computed using Equation (44). Furthermore, it

was assumed that the cost of an electric car without the battery is the same for both of the scenarios.

44

The total yearly cost of a pure battery electric based car fleet was obtained through the summation

of the yearly battery costs and the yearly infrastructure costs (Equation (45) and (46).

𝑇𝑜𝑡𝑎𝑙 𝐼𝑛𝑓𝑟𝑎𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒 𝐶𝑜𝑠𝑡 =

𝑁𝐶𝑎𝑟𝑠

𝐶𝑎𝑟 𝑝𝑒𝑟 𝑐ℎ𝑎𝑟𝑔𝑒𝑟∙ 𝐶𝑜𝑠𝑡 𝐶ℎ𝑎𝑟𝑔𝑒𝑟

(44)

𝑌𝑒𝑎𝑟𝑙𝑦 𝐼𝑛𝑓𝑟𝑎𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒 𝐶𝑜𝑠𝑡 =

𝑇𝑜𝑡𝑎𝑙 𝐼𝑛𝑓𝑟𝑎𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒 𝐶𝑜𝑠𝑡

𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 𝐸𝑅𝑆

(45)

𝑌𝑒𝑎𝑟𝑙𝑦 𝑃𝑢𝑟𝑒 𝐸𝑉 𝐶𝑜𝑠𝑡 = 𝑌𝑒𝑎𝑟𝑙𝑦 𝐼𝑛𝑓𝑟𝑎𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒 𝐶𝑜𝑠𝑡 + 𝑌𝑒𝑎𝑟𝑙𝑦 𝐸𝑉 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐶𝑜𝑠𝑡 (46)

In conclusion, the difference in cost between the two options was hence found by applying

Equation (47) for the different countries. From this the countries where having an ERS based

transport sector was more cost effective than a pure battery electric based sector were identified

and analyzed.

% 𝐶𝑜𝑠𝑡 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 =

𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑅𝑆 𝐶𝑜𝑠𝑡

𝑌𝑒𝑎𝑟𝑙𝑦 𝑃𝑢𝑟𝑒 𝐸𝑉 𝐶𝑜𝑠𝑡

(47)

Same as before, this entire modelling procedure was thereafter implemented for each of the two

studied ERS technologies with two different pricing of battery, namely the current battery price

and the predicted future battery price.

45

3.2 Model 2 – Sweden

The aim of this model was to take a closer look at the potential of the different ERS technologies

in Sweden using various modelling approaches. There were, in short, four different sub-models

that were produced which scrutinized the prospect of ERS from different perspectives. These sub-

models are explained in more details in the following sections.

3.2.1 Submodel 1 – Comparing a Petroleum-based Road Transport

System against ERS and EV Combination

The first sub-model was largely based on Model 1, but was optimized using input data for Sweden.

Most of the optimized input data was Sweden specific information gathered from literature and

Internet which could be applied directly, for example data regarding the fuel cost, electricity cost,

the number of motor vehicles etc. However, some of the input data was required to be calculated

before it could be used in the model. One of the most important input data that was computed was

the total vehicle kilometer per year that each of the four vehicle types stood for. This information

was paramount in calculating a number of important factors such as the average distance travelled,

average electricity consumption of the vehicles etc. which are used both in Model 1 and Model 2.

The first step in computing the total vehicle kilometer per year was to find and summarize the total

number of vehicles for each group. Furthermore, the average fuel consumption and average yearly

distance travelled for each vehicle type was obtained by manipulating the different statistic data

collected from Statistics Sweden. A simplified account of how the average fuel consumption and

average yearly distance travelled was acquired for each vehicle type is summarized below.

For the passenger cars, the average distance travelled was simply found by looking at data tables

provided by Statistics Sweden, as was the fuel consumption (l/km) for diesel and gasoline

passenger cars. By using the statistics over the gasoline and diesel consumption per inhabitant, the

total consumption and thus the percent division between gasoline and diesel was acquired

(Equation (48) and (49)). From this, the average fuel consumption in liter per kilometer, taking

into account both gasoline and diesel cars, was obtained through using Equation (50).

𝑇𝑜𝑡𝑎𝑙 𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 = 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 ∙

𝐷𝑖𝑒𝑠𝑒𝑙 𝑈𝑠𝑒

𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛+ 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 ∙

𝐺𝑎𝑠𝑜𝑙𝑖𝑛𝑒 𝑈𝑠𝑒

𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛

(48)

% 𝐹𝑢𝑒𝑙 𝑇𝑦𝑝𝑒 =

𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝑏𝑦 𝐹𝑢𝑒𝑙 𝑇𝑦𝑝𝑒

𝑇𝑜𝑡𝑎𝑙 𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛

(49)

𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝐶𝑎𝑟𝑠𝐴𝑣𝑒𝑟𝑎𝑔𝑒 = %𝐷𝑖𝑒𝑠𝑒𝑙 ∙ 𝐷𝑖𝑒𝑠𝑒𝑙

𝑙

𝑘𝑚+ %𝐺𝑎𝑠𝑜𝑙𝑖𝑛𝑒 ∙ 𝐺𝑎𝑠𝑜𝑙𝑖𝑛𝑒

𝑙

𝑘𝑚

(50)

The process for computing these factors for light-duty trucks and heavy-duty trucks was a little

different. The average distance travelled for each type could be obtained by manipulating the

compiled statistic data. But, as the compiled data was split into several weight classes (See Model

2 Excel file), an overall average for light-duty trucks and heavy-duty trucks was calculated. This

was done by finding information regarding the fuel consumption for different sized trucks, and

where information was missing, relevant assumptions were made. Using these estimates by truck

46

size and the collected data regarding the vehicle kilometer per year for a number of truck sizes, the

average fuel consumption for light-duty trucks and heavy-duty trucks was equated. The Equations

(51) and (52) were applied separately for the truck sizes that fell under light-duty trucks and which

were defined as heavy-duty trucks.

𝑇𝑜𝑡𝑎𝑙 𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 = ∑ 𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛𝐵𝑦 𝑠𝑖𝑧𝑒 ∙ 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝑘𝑚𝐵𝑦 𝑠𝑖𝑧𝑒

(51)

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 =

∑ 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝑘𝑚𝐵𝑦 𝑠𝑖𝑧𝑒

𝑇𝑜𝑡𝑎𝑙 𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛

(52)

Lastly, the average distance travelled and average fuel consumption for buses were found in the

compiled statistic data (See Model 2 Excel file). Once these inputs were obtained for all the four

vehicle types, the total vehicle kilometer per year could hence be computed. This was done through

using Equation (53).

𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑚𝑦𝑒𝑎𝑟𝑙𝑦 = 𝑁𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠 ∙ 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑇𝑟𝑎𝑣𝑒𝑙𝑙𝑒𝑑𝐴𝑣𝑒𝑟𝑎𝑔𝑒 ∙ 𝐹𝑢𝑒𝑙 𝑈𝑠𝑎𝑔𝑒𝐴𝑣𝑒𝑟𝑎𝑔𝑒 (53)

Once the yearly vehicle kilometer that each vehicle category contributed to was computed, the

percentage of each category of the total vehicle kilometer in Sweden was obtained (Equation

(54)).

% 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟 𝑏𝑦 𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑦 =

𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟 𝑏𝑦 𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑦

𝑇𝑜𝑡𝑎𝑙 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟

(54)

These values were afterwards an integral part in computing the weighted overall averages for a

number of important inputs. The average fuel consumption, average distance travelled, average

fuel price, average fuel emissions were all computed using the percentage fraction of vehicle

kilometer by vehicle category. The weighted averages were compiled using the same analogy as

for the average fuel consumption as shown in Equation (55). This procedure was performed to find

overall weighted averages for all the vehicle categories as well as for only heavy vehicles. This

was performed to represent the vehicle types that each ERS technology can utilize.

𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 = ∑ 𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 ∙ % 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟 𝑏𝑦 𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑦 (55)

Another important input that was computed in this part of the model, and which was used both in

Model 1 and Model 2, was the average electricity consumption for each of the vehicle categories.

The average electricity consumption for passenger cars was obtained from statistic data compiled

through literature survey. However, due to the fact that not many pure electric based vehicles exist

on a commercial scale for the other categories, a different approach was taken in order to find their

average electricity consumptions. The calculated fuel consumption for each vehicle type in

combination with the fuel energy content, density and the vehicle well-to-wheel efficiency was

used to compute the energy consumption per kilometer. This procedure is summarized by Equation

(56) and (57).

47

𝐹𝑢𝑒𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑛𝑡𝑒𝑛𝑡 (𝑀𝐽

𝑙) =

𝐹𝑢𝑒𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑛𝑡𝑒𝑛𝑡 (𝑀𝐽𝑘𝑔

)

𝐹𝑢𝑒𝑙 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 (𝑘𝑔𝑚3

)

(56)

𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 (

𝑀𝐽

𝑘𝑚) = 𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 ∙ 𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑛𝑡𝑒𝑛𝑡 ∙ 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦

(57)

Once the energy consumption per kilometer was computed, the energy needed for electric drive

was equated using an approximated well-to-wheel efficiency for electric vehicles. Lastly, the

electricity consumption for each vehicle type was obtained as shown below where 0.28 was used

to convert MJ/km to kWh/km.

𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝐷𝑟𝑖𝑣𝑒 (𝑀𝐽

𝑘𝑚) =

𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 (𝑀𝐽𝑘𝑚

)

𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦𝐸𝑉𝑠

(58)

𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 (𝐾𝑊ℎ

𝑘𝑚) =

𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝐷𝑟𝑖𝑣𝑒 (𝑀𝐽𝑘𝑚

)

0.28

(59)

The weighted average for the electricity consumption was computed in the same manner explained

previously. Once these inputs were computed and other inputs were optimized for Sweden using

different information sources, the rest of the procedure was exactly like Model 1.

3.2.2 Submodel 2 – Comparing a Pure Battery Electric Car Fleet

against ERS and Electric Car Combination

The second model was also built on Model 1, but on the second part, which compared an ERS

based passenger car fleet against a pure battery electric passenger car fleet. This comparison was

applied on Sweden with optimized input data. An addition to the model was however made which

sought to answer the question: How many electric cars would there need to be on the roads before

having an ERS based transportation fleet would be cheaper than having a pure battery electric car

fleet.

Hence, this addition in the model aimed to find the point when an ERS would become cheaper and

the number of electric cars this point would correspond to. This procedure was performed quite

simply by applying the modelling approach described in Model 1 for increasing number of

passenger cars. From this, the cost of ERS and the cost of pure electric cars were acquired for a

range of different number of cars. These costs were subsequently plotted against the number of

cars, which finally gave the point of intersection between the two cost estimates. This method was

done for the ERS technologies that support electric cars for different battery pricing.

3.2.3 Submodel 3 – Finding the Breakeven Point

This submodel was developed with the goal of investigating the frequency of electric vehicles per

day needed on an electrified road section that would result in a repayment of the investment. To

be able to answer this question, a one kilometer electrified road length was analyzed. Using the

fixed-rate mortgage method, the daily cost for a kilometer long section of electrified road was

computed by applying Equation (60) and (61).

48

𝐶𝑜𝑠𝑡𝑦𝑒𝑎𝑟𝑙𝑦 = 𝐶𝑜𝑠𝑡 𝐸𝑅𝑆 ∙

𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 ∙ (1 + 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 )𝑇𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛

(1 + 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 )𝑇𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝐿𝑖𝑓𝑒𝑠𝑝𝑎𝑛 − 1

(60)

𝐶𝑜𝑠𝑡𝑑𝑎𝑖𝑙𝑦 =

𝐶𝑜𝑠𝑡𝑦𝑒𝑎𝑟𝑙𝑦

360

(61)

Once the daily cost was computed, a range of frequency of vehicles per day was produced. For

each different frequency, the fuel consumption cost and the electric consumption cost were

computed as shown in the following equations.

𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡 =

𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠

𝑑𝑎𝑦∙ 𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 ∙ 𝐹𝑢𝑒𝑙 𝐶𝑜𝑠𝑡

(62)

𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡 =

𝑉𝑒ℎ𝑖𝑐𝑙𝑒

𝑑𝑎𝑦∙ 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 ∙ 𝐸𝑙. 𝐶𝑜𝑠𝑡

(63)

From this the economic savings from an electric based vehicle fleet compared to the current

petroleum-based car fleet on this road could be calculated using Equation (64).

𝑆𝑎𝑣𝑖𝑛𝑔𝑠 = 𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡 − 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡 (64)

This was subsequently done for increasing frequency of vehicle per day until the breakeven point

where the savings equaled the ERS investment costs was found. The procedure was repeated for

the two ERS technologies, and lastly, figures were created to present the findings.

3.2.4 Submodel 4 – Expansion

The purpose of developing this submodel was to simulate, using various scenarios, how an

expansion of the different ERS technologies could look like in Sweden until the year 2050. To be

able to approach and model such a vast problem, there were a couple of important assumptions

that were found necessary to be made. To start with, the model was developed in such a way that

it only regarded the expansion from an ERS perspective. To clarify, only the vehicle road work

that was performed on the electrified roads was considered in the calculations and the savings from

using battery drive on the normal roads were excluded. Similarly, only the cost related to

constructing and maintaining the ERS was included in the expenditure, as such, the investment

cost of batteries and pickup arm was not considered. Using this method, the accumulated savings,

investment costs and thus also the result could be obtained for the electrified roads to see if they

would lead to overall cost savings.

Due to the fact that Sweden has ambitious goals of having a fossil-free transport sector in the near

future, it was presumed that the transport sector would be fully electric-based until 2050 at the

latest. Furthermore, the total yearly vehicle kilometer, new vehicles per year and the total number

of vehicles were assumed to be constant as explained in the assumption chapter. From this and

using the data acquired from Swedish Transport Administration regarding the vehicle kilometer,

equations that related the vehicle kilometer and traffic intensity as a function of road length could

be produced.

49

The data regarding the vehicle kilometer per road category for all vehicle and only heavy trucks,

as shown in Table 1 and Table 2, was acquired from the Swedish Transport Administration.

Subsequently, the accumulated road length and vehicle kilometer could be computed.

Table 1. Yearly vehicle kilometer for all vehicles per road category during 2015.

All vehicles (km) (km) (Mkm) (Mkm)

Road length Accumulated

road length

Vehicle kilometer Accumulated vehicle

kilometer

European roads 6,700 6,700 23,000 23,000

National roads 8,900 15,600 14,000 37,000

Primary county roads 10,800 26,400 9,000 46,000

Other county roads 72,100 98,500 12,000 58,000

Total 98,500 58,000

Table 2. Yearly vehicle kilometer for heavy-duty trucks per road category during 2015.

Heavy-duty trucks (km) (km) (Mkm) (Mkm)

Million km Road length Accumulated

road length

Vehicle kilometer Accumulated

Vehicle kilometer

European roads 6,700 6,700 3,170 3,170

National roads 8,900 15,600 1,701 4,871

Primary county roads 10,800 26,400 825 5,696

Other county roads 72,100 98,500 834 6,530

Total 98,500 6,530

The accumulated road length and vehicle kilometer from the tables provided points that related

vehicle kilometer to the road length. However, as a smooth curve was needed that correlated the

vehicle kilometer per each road kilometer, a formula was produced through curve fitting that gave

the correct values. Sequentially, a formula that related the traffic intensity, viz. vehicles per day,

to the road kilometer could also be computed. These curve fitted figures are shown below and they

show that majority of the vehicle kilometer and thus also the highest traffic intensity is on the

major roads in Sweden. A fundamental assumption that was undertaken as a natural implication

from the vehicle kilometer function was that the most traffic intensive roads would be electrified

at first. The reasoning behind this was quite straightforward, as these roads would be where the

biggest savings could be made due to the high traffic intensity. The same function for vehicle

kilometer and traffic intensity by road length were used for heavy vehicles, however, they were

calibrated to fit the data for heavy-duty trucks.

50

Figure 30. Vehicle kilometer as a function of road length.

Figure 31. Traffic intensity as a function of road length.

0

10

20

30

40

50

60

70

BIL

LIO

N V

EHIC

LE K

ILO

MET

ER

ROAD LENGTH IN THOUSAND KILOMETER

Vehicle kilometer as a function of road length

Accumulated distance travelled by vehicles[Trafikverket]

1

10

100

1,000

10,000

100,000

1,000,000

VEH

CIL

ES/D

AY

ROAD LENGTH IN THOUSAND KILOMETER

Traffic intensity as a function of road lengthTraffic intensity

51

Once a function of the vehicle kilometer related to the road length was computed, the rest of the

submodel could be built around it. It is important to note, that each of the calculations described

below were performed for each year in sequence. To begin with, the total expansion needed for

each technology was set as the total kilometer electrified road needed in Sweden as computed in

submodel 1 in Equation (65) for future battery price. A variable input for the yearly kilometer of

road that would be electrified was implemented which could be altered through changing the

annual percentile growth rate and max kilometer electrification per year. All of these inputs were

changed depending on which scenario that was analyzed, and more on the specifics of each

scenario will be explained later.

𝑇𝑜𝑡𝑎𝑙 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑓𝑖𝑒𝑑 𝑅𝑜𝑎𝑑 = ∑ 𝑌𝑒𝑎𝑟𝑙𝑦 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑓𝑖𝑒𝑑 𝑅𝑜𝑎𝑑 𝐸𝑥𝑝𝑎𝑛𝑠𝑖𝑜𝑛

2050

2016

(65)

Using the formula for vehicle kilometer produced earlier, the yearly accumulated vehicle kilometer

that was executed on the electrified roads could then be equated by adding the yearly vehicle

kilometer executed on the new electrified road sections. This procedure is shown in Equation (66)

and (67).

𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟𝑁𝑒𝑤 𝑟𝑜𝑎𝑑 = 𝑓(𝑌𝑒𝑎𝑟𝑙𝑦 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑓𝑖𝑒𝑑 𝑅𝑜𝑎𝑑 𝐸𝑥𝑝𝑎𝑛𝑠𝑖𝑜𝑛) (66)

𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑒𝑑 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟 = ∑ 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟𝑁𝑒𝑤 𝑟𝑜𝑎𝑑

2050

2016

(67)

From this, the vehicle kilometer that was performed by electric vehicles could then be equated

through a number of steps. To begin with, a variable for the percentage of new vehicles sold per

year that would be EVs was defined. This variable was increased yearly by different percentile

values, depending on the scenario, until 100 % of the sales of new vehicles were electric vehicles.

Consequently, the accumulated number of electric vehicles that were added each year could be

computed. These steps are shown in Equation (68) and (69).

𝑁𝑒𝑤 𝐸𝑉𝑦𝑒𝑎𝑟𝑙𝑦 = 𝑁𝑒𝑤 𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠𝑦𝑒𝑎𝑟𝑙𝑦 ∙ % 𝐸𝑉 𝑜𝑓 𝑁𝑒𝑤 𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠𝑦𝑒𝑎𝑟𝑙𝑦 (68)

𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑒𝑑 𝐸𝑉𝑠 = ∑ 𝑁𝑒𝑤 𝐸𝑉𝑦𝑒𝑎𝑟𝑙𝑦

2050

2016

(69)

By each year using the value for accumulated EVs, the percent of the vehicle kilometer performed

on the electrified roads that EVs contributed to could then be computed.

% 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟𝐸𝑉 =

𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑒𝑑 𝐸𝑉𝑠 ∙ 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑇𝑟𝑎𝑣𝑒𝑙𝑙𝑒𝑑

𝑇𝑜𝑡𝑎𝑙 𝑁𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠 ∙ 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑇𝑟𝑎𝑣𝑒𝑙𝑙𝑒𝑑

52

→ % 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟𝐸𝑉 =

𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑒𝑑 𝐸𝑉𝑠

𝑇𝑜𝑡𝑎𝑙 𝑁𝑉𝑒ℎ𝑖𝑐𝑙𝑒𝑠

(70)

Lastly, the vehicle kilometer travelled each year by the electric vehicles can be computed using

Equation (71).

𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟𝐸𝑉 = % 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟𝐸𝑉 ∙ 𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑒𝑑 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟 (71)

Once all these inputs were defined, the yearly and thus also the accumulated expenditure, savings

and results could be derived. Beginning with the expenditures, these were assumed to be the cost

for the electrified roads, road renovation and road maintenance. The yearly electrified road

investment was computed by simply multiplying with the yearly expansion (Equation (72)). The

yearly road renovations were presumed to occur on the old roads once their technical lifespan was

reached. It was calculated in the same way as the road investment. Lastly, the road maintenance

was equated using a cost input for the daily cost per kilometer road length through Equation (74).

𝑅𝑜𝑎𝑑 𝐼𝑛𝑣𝑒𝑠𝑡𝑒𝑚𝑒𝑛𝑡𝑦𝑒𝑎𝑟𝑙𝑦 = 𝑌𝑒𝑎𝑟𝑙𝑦 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑓𝑖𝑒𝑑 𝑅𝑜𝑎𝑑 𝐸𝑥𝑝𝑎𝑛𝑠𝑖𝑜𝑛 ∙ 𝐶𝑜𝑠𝑡 𝐸𝑅𝑆 (72)

𝑅𝑜𝑎𝑑 𝑅𝑒𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑦𝑒𝑎𝑟𝑙𝑦 = 𝑌𝑒𝑎𝑟𝑙𝑦 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑓𝑖𝑒𝑑 𝑅𝑜𝑎𝑑 𝑅𝑒𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 ∙ 𝐶𝑜𝑠𝑡 𝐸𝑅𝑆 (73)

𝑅𝑜𝑎𝑑 𝑀𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒𝑦𝑒𝑎𝑟𝑙𝑦 =

𝐶𝑜𝑠𝑡𝑦𝑒𝑎𝑟𝑙𝑦

𝑘𝑚 ∙ 𝑌𝑒𝑎𝑟𝑙𝑦 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑓𝑖𝑒𝑑 𝑅𝑜𝑎𝑑 𝐸𝑥𝑝𝑎𝑛𝑠𝑖𝑜𝑛

(74)

The accumulated expenditure was thereafter computed by adding these three costs for each year.

Next the yearly and accumulated savings were computed by subtracting the cost of fuel that would

have been used compared to the electricity used by the EVs on the electrified roads. This process

can be seen in Equation (75), (76), (77) and (78).

𝐹𝑢𝑒𝑙 𝐶𝑜𝑠𝑡𝑦𝑒𝑎𝑟𝑙𝑦 = 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟𝐸𝑉 ∙ 𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 ∙ 𝐹𝑢𝑒𝑙 𝑃𝑟𝑖𝑐𝑒 (75)

𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝐶𝑜𝑠𝑡𝑦𝑒𝑎𝑟𝑙𝑦 = 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝐾𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟𝐸𝑉 ∙ 𝐸𝑙. 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 ∙ 𝐸𝑙. 𝑃𝑟𝑖𝑐𝑒 (76)

𝑆𝑎𝑣𝑖𝑛𝑔𝑠𝑦𝑒𝑎𝑟𝑙𝑦 = 𝐹𝑢𝑒𝑙 𝐶𝑜𝑠𝑡𝑦𝑒𝑎𝑟𝑙𝑦 − 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝐶𝑜𝑠𝑡𝑦𝑒𝑎𝑟𝑙𝑦 (77)

𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑒𝑑 𝑆𝑎𝑣𝑖𝑛𝑔𝑠 = ∑ 𝑆𝑎𝑣𝑖𝑛𝑔𝑠𝑦𝑒𝑎𝑟𝑙𝑦

2050

2016

(78)

Finally, the yearly and accumulated result was produced by subtracting the expenditure from the

savings. In Equation (79), the process is shown for only the accumulated result which is done each

year.

𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑒𝑑 𝑅𝑒𝑠𝑢𝑙𝑡 = 𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑒𝑑 𝑆𝑎𝑣𝑖𝑛𝑔𝑠 − 𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑒𝑑 𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒 (79)

The accumulated values can thereafter be plotted against each year to get a figure that shows the

cost development. Additionally, the emission reduction result was also computed, and the method

53

used was analogues to the Equations (75), (76), (77). However, the emission reduction calculations

used the values for emissions instead of costs in the equations.

The Scenarios

As discussed briefly earlier, the expansion model was implemented on different scenarios for each

of the three studied ERS technologies. The scenarios were developed by the author and aimed to

reflect different possible futures which were split into three potential futures. These three scenarios

are explained in detail below.

Scenario 1. In this scenario, the government is convinced that ERS is the future of the

transportation sector. As a result, there is high governmental engagement and

influence for a fast expansion and implementation of ERS. Also, increasing oil

prices due to shortage, in combination with governmental incentives have

resulted in a rapid growth of electrical vehicles in Sweden.

Scenario 2. For this scenario, the government is not completely convinced that ERS is the

solution for Sweden’s road transportation sector. Due to this fact, there is

medium governmental engagement and influence for an expansion and

implementation of ERS. Additionally, oil prices continue to be low and

combined with weak governmental support, has resulted in a moderate increase

in the yearly sales of electric vehicles.

Scenario 3. Lastly, in this scenario there is low governmental commitment and influence

leading to a slow expansion and implementation of ERS. Also, the continuing

low oil prices and low governmental engagement have resulted in a very slow

penetration of electric vehicles in the market.

From these scenarios, some specifications could thus be produced and implemented into the

expansion model. These specifications for each scenario are summarized in Table 3. It is important

to note that these specification and predictions have been developed by the author and are thus

very much speculative. The thinking behind the scenarios is to merely give a notion about how a

potential future expansion of ERS might look like.

Table 3. Expansion specifications for each scenario.

Specifications ERS expansion EV expansion

Scenario 1 – Fast 2016-2027, 11 years - Until 2024, 100 % of new

vehicles sold are EVs.

- All motor vehicles are EVs

until 2035.

Scenario 2 - Medium 2016-2036, 20 years - Until 2030, 100 % of new

vehicles sold are EVs.

- All motor vehicles are EVs

until 2039.

Scenario 3 - Slow 2016-2049, 33 years - Until 2043, 100 % of new

vehicles sold are EVs.

- All motor vehicles are EVs

until 2048.

54

4 RESULTS

In this chapter the results obtained from the models described are presented. In the same way as

the models, the results are split into two main parts. That is, a section that presents the prospect of

ERS from a global perspective and one that investigates the potential more closely for Sweden.

4.1 Prospect of Electrical Road Systems in the World

184 countries were analyzed and evaluated to see whether ERS solutions could be a viable option

for them from primarily an economic standpoint. The modelling was split into two main scenarios

where the prospect of the three current ERS technologies was investigated. Firstly, the ERS

technologies in combination with electric vehicles were compared against the current petroleum-

based transportation system to assess the potential economic and environmental gains. Secondly,

the investment cost of replacing conventional passenger cars with 100 % electric based passenger

cars, including the infrastructure modifications, was computed. This was then compared against

the investment cost of ERS solutions in combination with electric cars to assess which alternative

would be the best from an economic perspective. These two comparisons are presented in more

detail in the following sections.

4.1.1 Comparison – Petroleum-based Road Transport against ERS and

EV Combination

Using the optimization procedure described in the methodology section, the optimal length of the

electrified roads and battery size needed was computed for each country. This procedure was

reiterated for each technology for two different battery cost scenarios. Subsequently, the cost of

installing a full-scale ERS was compared and analyzed with the cost of the current petroleum fuel

based system.

To identify the regions/countries in the world that are the most suited for ERS installations, the

countries where the yearly savings were found to be positive for an ERS based transport system

were sorted. This was done for each ERS technology and battery cost scenario. Consequently,

some global overall factors could then be computed for these countries, the most important of these

are presented in Table 4 and Table 5. In Appendix B, a list with each individual country where the

yearly savings were found to be positive for the conductive road technology can be found. This

list demonstrates how the computing of the main factors was performed for each ERS technology

and battery cost scenario.

55

Table 4. Most important global results for each ERS technology in countries where the yearly savings are positive,

assuming 2015 battery pricing.

Scenario

2015

Countries Yearly

Savings

Electrified

Road

Battery

Size

Percent

of

Existing

Road

Network

Percent of

Total

Electricity

Production

Percent of

Yearly

BNP

(Total

ERS

Cost)

Percent

of GHG

Emission

Reduced

Percent

of Total

Vehicles

Globally

Percent

of Total

Heavy

Vehicles

Globally

# MSEK/year km kWh % % % % % %

Conductive

Road

87 2,500,000 4,300,000 4 19.9% 17.3% 2.7% 4.9% 79% 79%

Overhead

Conductive

57 200,000 610,000 8 4.3% 6.4% 0.2% 0.7% 1% 55%

Inductive

Road

48 900,000 480,000 4 7.5% 22.7% 3.1% 1.9% 30% 30%

Table 5. Most important global results for each ERS technology in countries where the yearly savings are positive,

assuming future battery pricing.

Scenario

Future

Countries Yearly

Savings

Electrified

Road

Battery

Size

Percent

of

Existing

Road

Network

Percent of

Total

Electricity

Production

Percent of

Yearly

BNP

(Total ERS

Cost)

Percent

of GHG

Emission

Reduced

Percent

of Total

Vehicles

Globally

Percent

of Total

Heavy

Vehicles

Globally # MSEK/year km kWh % % % % % %

Conductive

Road

116 3,700,000 3,600,000 8 11.3% 16.7% 1.7% 5.6% 91% 91%

Overhead

Conductive

86 500,000 630,000 17 2.9% 5.2% 0.2% 1.0% 2% 78%

Inductive

Road

76 2,100,000 1,210,000 11 6.0% 17.3% 3.0% 4.6% 75% 75%

To easily illustrate how the cost of different ERS technologies and the batteries affect the number

of countries that are suitable for ERS, Figure 32 has been produced. It displays that the number of

countries that are viable for ERS implementation increase with decreasing ERS installation and

battery costs.

56

Figure 32. Number of countries where the yearly profit is positive for different ERS technologies and battery cost

scenarios.

Similarly, Figure 33 has been produced to illustrate how big percentage of the global vehicle fleet

the countries where the yearly savings are positive account for. Whereas in Figure 34, the same

has been done, but for only heavy vehicles. It also demonstrates that the percentage of the total

global vehicle increase with decreasing ERS installation and battery costs.

Figure 33. Percentage of total number of vehicles worldwide in countries where the yearly saving is positive for

different ERS technologies and battery cost scenarios.

87

5748

116

86

76

0

20

40

60

80

100

120

140

Conductive Road Overhead Conductive Inductive Road

Nu

mb

er

of

cou

ntr

ies

Number of countries where the yearly saving is positive for different ERS technologies and battery cost

Scenario 2015 Scenario Future

79%

1%

30%

91%

2%

75%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Conductive Road Overhead Conductive Inductive Road

% o

f to

talg

lob

al v

ehic

les

Percentage of total global vehicles in countries where the yearly saving is positive

Scenario 2015 Scenario Future

57

Figure 34. Percentage of total number of heavy vehicles worldwide in countries where the yearly saving is positive

for different ERS technologies and battery cost scenarios.

To also include an environmental perspective, Figure 35 was produced. It illustrates how big

reduction in the global total GHG emissions could be achieved by implementing ERS in countries

with positive yearly savings.

Figure 35. Percent of global total GHG emissions reduced by implementing the different ERS technologies in

countries where the yearly savings are positive for different and battery cost scenarios.

79%

55%

30%

91%

78%75%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Conductive Road Overhead Conductive Inductive Road

% o

f to

tal g

lob

al v

eh

icle

s

Percentage of total heavy vehicles in countries where the yearly saving is positive

Scenario 2015 Scenario Future

4.9%

0.7%

1.9%

5.6%

1.0%

4.6%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

Conductive Road Overhead Conductive Inductive Road

% G

HG

em

issi

on

re

du

ctio

n

Percent of total global GHG emissions reduced by countries where the ERS yearly saving are positive

Scenario 2015 Scenario Future

58

To get an overview over which regions and countries that are suitable for ERS, Figure 36 was

produced. It shows the percentage of the countries in the world that are suitable for conductive

ERS solution assuming the current battery pricing. It also presents the country percentage for each

individual continent. It should be noted that for this comparison, all the recognized countries in

the world were counted in.

Figure 36. The percentage of the countries in the world/continents that are suitable for conductive power transfer

from road solution assuming the current battery pricing of 350 USD/kWh.

In the same way, Figure 37 illustrates the percentage of the countries/continents in the world that

are suitable for conductive power transfer from road assuming the predicted future battery pricing.

Figure 37. The percentage of the countries in the world/continents that are suitable for conductive power transfer

from road solution assuming the predicted future battery pricing of 120 USD/kWh.

59

4.1.2 Comparison – Pure Battery Electric Car fleet against ERS and

Electric Car Combination

In similar fashion as for the comparison between ERS and petroleum-based fuels. The process

described in the modelling chapter was applied to produce a comparison between a future where

electric passenger cars use only batteries for propulsion and one where both ERS and batteries are

used in combination. This comparison was made for the conductive and inductive road

technologies using two battery cost scenarios, namely, the current and future predicted battery

pricing. The comparison was done by calculating how much each future would cost and thereafter

computing the cost difference in percent between the two. In Table 6 and Table 7 this has been

done for all the 184 countries by computing a global cost. In this comparison all countries are

included, regardless of whether the ERS plus battery combination is found less expensive or not.

Table 6. The cost difference in percent between the two cases for all 184 countries assuming 2015 battery pricing.

Scenario

2015

Countries Percent of Total Cars

in the World

Yearly ERS +

EV Cost

Yearly Pure EV +

Infrastructure Cost

Cost Difference

# % MSEK/Year MSEK/Year %

Conductive

Road

184 100% 2,600,000 37,200,000 6.9%

Inductive

Road

184 100% 4,600,000 37,200,000 12.2%

Table 7. The cost difference in percent between the two cases for all 184 countries assuming future battery pricing.

Scenario

Future

Countries Percent of Total Cars

in the World

Yearly ERS +

EV Cost

Yearly Pure EV +

Infrastructure Cost

Cost Difference

# % MSEK/Year MSEK/Year %

Conductive

Road

184 100% 1,700,000 13,300,000 12.9%

Inductive

Road

184 100% 2,900,000 13,300,000 21.8%

In the next step, the countries where the cost of ERS in combination with batteries was found to

be lower than a pure battery electric passenger car case, were summarized. Subsequently, the

overall yearly cost in these countries could be computed to find the price difference and other

interesting factors as shown in Table 8 and Table 9. In Appendix C, a complete summarized list

for each country can be found for conductive ERS technology with 2015 battery pricing which

exemplifies how the calculations were performed for each ERS technology and battery pricing.

Table 8. Number of countries where the ERS solution is cheaper and the cost difference between the two cases

assuming 2015 battery pricing.

Scenario

2015

Countries Percent of Total Cars

in the World

Yearly ERS +

EV Cost

Yearly Pure EV +

Infrastructure Cost

Percent Cost

Difference

# % MSEK/Year MSEK/Year %

Conductive

Road

181 99.99% 2,100,000 37,200,000 5.6%

Inductive

Road

173 99.97% 3,600,000 37,200,000 9.7%

60

Table 9. Number of countries where the ERS solution is cheaper and the cost difference between the two cases

assuming future battery pricing.

Scenario

Future

Countries Percent of Total Cars

in the World

Yearly ERS +

EV Cost

Yearly Pure EV +

Infrastructure Cost

Percent Cost

Difference

# % MSEK/Year MSEK/Year %

Conductive

Road

177 99.98% 1,400,000 13,300,000 10.8%

Inductive

Road

164 99.87% 2,300,000 13,300,000 17.5%

Lastly, an illustrative summary has been made of the cost differences for each scenario presented

in the tables. The figure shows that an ERS based car fleet would cost less compared to a pure

battery electric car fleet for all the studied scenarios.

Figure 38. A summarization of all the cost differences presented in the previous tables

5.6%

9.7%

6.9%

12.2%10.8%

17.5%

12.9%

21.8%

0%

5%

10%

15%

20%

25%

Conductive Road Overhead Conductive

% P

rice

dif

fere

nce

Cost difference between the two futures for different ERS technologies and battery pricing

Scenario 2015 Scenario 2015 - All countries Scenario Future Scenario Future - All countries

61

4.2 Prospect of Electrical Road System in Sweden

In this model, the potential of an ERS based transportation system in Sweden was analyzed.

Several submodels were produced that studied the problem from different perspectives. The

findings obtained from the submodels are presented in the following subsections.

4.2.1 Submodel 1 – Comparing a Petroleum-based Road Transport

System against ERS and EV Combination

This submodel was in essence based on the Model 1 comparison between a petroleum-based

transportation system and an ERS based one. However, the biggest difference was that the input

data used in this submodel was optimized for Sweden. Consequently, the yearly savings/losses of

an ERS based transportation system could be computed for each ERS technology and battery price

scenario for Sweden. The most important results computed are shown in Table 10 and Table 11

for each technology and battery cost scenario.

Table 10. The most important results obtained for the three ERS technologies in Sweden assuming 2015 battery

pricing.

Scenario

2015

Yearly

Savings

Electrified

Road

Battery

Size

Total

Cost of

Road

Total

ERS

Cost

Percent

of

Existing

Road

Network

Percent of

Total

Electricity

Production

Percent of

Yearly BNP

(Total ERS

Cost)

Percent

Reduced of

Transport

Emissions

MSEK/year km kWh MSEK MSEK % % % %

Conductive

Road

12,400 40,200 5 161,000 354,000 19% 13% 9% 92%

Overhead

Conductive

-420 9,700 90 60,000 124,000 5% 4% 3% 23%

Inductive

Road

-9,760 20,800 9 311,000 655,000 10% 13% 16% 92%

Table 11. The most important results obtained for the three ERS technologies in Sweden assuming future battery

pricing.

Scenario

Future

Yearly

Savings

Electrified

Road

Battery

Size

Total

Cost of

Road

Total

ERS

Cost

Percent

of

Existing

Road

Network

Percent of

Total

Electricity

Production

Percent of

Yearly BNP

(Total ERS

Cost)

Percent

Reduced of

Transport

Emissions

MSEK/year km kWh MSEK MSEK % % % %

Conductive

Road

21,900 24,000 8 96,000 225,000 11% 13% 5% 92%

Overhead

Conductive

3,000 5,800 150 35,000 77,000 3% 4% 2% 23%

Inductive

Road

8,700 12,400 16 186,000 405,000 6% 13% 10% 92%

The yearly saving or loss for each technology and battery pricing has thereafter been

summarized in a illustrative manner in Figure 39.

62

Figure 39. A summarization of the yearly savings or losses for each ERS technology and battery cost scenario.

4.2.2 Submodel 2 – Comparing a Pure Battery Electric Car Fleet

against ERS and Electric Car Combination

Similarly, this submodel was built on the part of Model 1 that compared a fully expanded pure

battery electric car fleet against a transportation system that utilizes both ERS and electric cars in

combination. The yearly cost for each case could hence be computed and compared with each

other. In Table 12 and Table 13, the cost for each case is presented for the two studied ERS

technologies and battery pricing.

Table 12. The cost difference between the two cases for the two examined ERS technologies assuming 2015 battery

pricing.

Scenario 2015 Yearly ERS + EV

Cost

Yearly Pure EV +

Infrastructure Cost

Percent Cost Difference

MSEK/Year MSEK/Year %

Conductive Road 10,305 173,923 6%

Inductive Road 17,977 173,923 10%

Table 13. The cost difference between the two cases for the two examined ERS technologies assuming future battery

pricing.

Scenario Future Yearly ERS + EV

Cost

Yearly Pure EV +

Infrastructure Cost

Percent Cost Difference

MSEK/Year MSEK/Year %

Conductive Road 7,008 62,285 11%

Inductive Road 11,593 62,285 19%

Furthermore, this model aimed to find the point when an ERS based transportation system would

become cheaper than a pure electric car based one, and the number of electric cars this point would

12,400

-420

-9,760

21,900

3,000

8,700

-15,000

-10,000

-5,000

0

5,000

10,000

15,000

20,000

25,000

Conductive Road Overhead Conductive Inductive Road

MSE

K/Y

ear

The yearly savings/loss for each ERS technology and battery cost scenario

Scenario 2015 Scenario Future

63

correspond to. This was performed for both the conductive road and inductive road ERS

technologies using the two different battery cost scenarios, as demonstrated in Figure 40. The

maximum number of pure electric cars before an ERS based transportation system becomes less

expensive assuming 2015 battery pricing was found to be approximately 15 000 and 45 000 cars

for conductive road and inductive road technologies respectively. Likewise, Figure 41 exhibits the

maximum number of electric cars, assuming future battery pricing, to be roughly 35 000 cars for

conductive technology and 130 000 cars for inductive technology.

Figure 40. The number of cars required until an ERS based system is cheaper for the two examined ERS

technologies assuming 2015 battery pricing.

15,000 Cars

45,000 Cars

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

INV

ESTM

ENT

(MSE

K/Y

EAR

)

NUMBER OF ELECTRIC CARS

Pure EV vs ERS Combination Case - 2015Yearly ERS + EV Cost - ConductiveYearly Pure EV + Infrastructure CostYearly ERS + EV Cost - Inductive

64

Figure 41. The number of cars required until an ERS based system is cheaper for the two examined ERS

technologies assuming future battery pricing.

4.2.3 Submodel 3 – Finding the Breakeven Point

This submodel was developed to find the frequency of electric vehicles required on an electrified

road section to repay the initial investment cost (operation costs are not included) for different

ERS technologies. The frequency breakeven point was hence computed for each technology and

is illustrated in Figure 42 and Figure 43. As illustrated in the figures, it was found that a minimum

frequency of 1650 and 6150 EVs per day is required for conductive and inductive ERS

technologies respectively. Whereas, for overhead conductive technology, a minimum frequency

of 670 heavy electric vehicles per day was computed.

35,000 Cars

130,000 Cars

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

INV

ESTM

ENT

(MSE

K/Y

EAR

)

NUMBER OF ELECTRIC CARS

Pure EV vs ERS Combination Case - FutureYearly ERS + EV cost - ConductiveYearly Pure EV + Infrastructure CostYearly ERS + EV Cost - Inductive

65

Figure 42. The frequency breakeven points for inductive and conductive ERS technologies.

Figure 43. The frequency breakeven point for overhead conductive ERS technology.

1650 EVs/day

6150 EVs/day

0

500

1000

1500

2000

2500

3000

3500

4000

SEK

/DA

Y

ELECTRIC VEHICLES/DAY

Breakeven Points for Inductive and Conductive Road Technologies

Conductive Road Cost Savings Inductive Road Cost

670 HEVs/day

0

200

400

600

800

1000

1200

1400

1600

1800

SEK

/DA

Y

HEAVY ELECTRIC VEHICLES/DAY

Breakeven Point for Overhead Conductive Technology

Conductive Overhead Cost Savings

66

4.2.4 Submodel 4 – Expansion

The final submodel was produced to simulate how an expansion of the different ERS technologies

could look like in Sweden until the year 2050. The investment costs and savings were only viewed

from an electrified road perspective as explained in the MODELS chapter. Three different

expansion scenarios of Electrified Road Systems and electric vehicles were scrutinized and

implemented on the model, where Scenario 1 simulated a fast expansion, Scenario 2 a medium

fast expansion and Scenario 3 a slow expansion. In the following figures the accumulated results,

which were found by subtracting the accumulated savings from the accumulated expenditures, are

presented for each ERS technology and scenario.

Figure 44. Accumulated result for the three different scenarios for conductive road ERS technology.

-100,000

-50,000

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

MSE

K/Y

EAR

Accumulated Result for all Scenarios - Conductive RoadAccumulated Result - Scenario 1 (Fast)

Accumulated Result - Scenario 2 (Medium)

Accumulated Result - Scenario 3 (Slow)

67

Figure 45. Accumulated result for the three different scenarios for overhead conductive ERS technology.

Figure 46. Accumulated result for the three different scenarios for inductive road ERS technology.

-40,000

-30,000

-20,000

-10,000

0

10,000

20,000

30,000

40,000

50,000

MSE

K/Y

EAR

Accumulated Result for all Scenarios - Overhead Cond.Accumulated Result - Scenario 1 (Fast)

Accumulated Result - Scenario 2 (Medium)

Accumulated Result - Scenario 3 (Slow)

-180,000

-160,000

-140,000

-120,000

-100,000

-80,000

-60,000

-40,000

-20,000

0

MSE

K/Y

EAR

Accumulated Result for all Scenarios - Inductive RoadAccumulated Result - Scenario 1 (Fast)

Accumulated Result - Scenario 2 (Medium)

Accumulated Result - Scenario 3 (Slow)

68

As it was found that the largest saving until year 2050 was obtained from Scenario 1 for all the

ERS technologies. The accumulated result from all the technologies has thus been summarized in

Figure 47 for Scenario 1 to display these results side by side.

Figure 47. Accumulated result obtained from Scenario 1 for all ERS technologies.

Lastly, the reduction of GHG emissions per year was also plotted out for the three scenarios. Figure

48 demonstrates how the yearly percentile emission reduction obtained by shifting to an electric

based transportation system would appear for the conductive and inductive road technologies with

the different scenarios. Similarly, the emission reduction for each scenario for the overhead

conductive ERS would have the same plot shape. However, the difference being that the maximum

percentile GHG emission reduction from the transportation sector would in this case be 23 % as

opposed to the 92 % shown in the figure. This, of course, is due to the fact that the overhead

conductive technology can only be utilized by heavy vehicles.

-200,000

-100,000

0

100,000

200,000

300,000

400,000

MSE

K/Y

EAR

Accumulated Result for Scenario 1 - All Technologies

Conductive Road ERS

Overhead Conductive ERS

Inductive Road ERS

69

Figure 48. The yearly development of GHG emission reduction for the three different scenarios for conductive and

inductive ERS technology.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% E

MIS

SIO

N R

EDU

CTI

ON

% GHG emission reduction from transportation sectorReduction in Emissions - Scenario 1 (Fast)

Reduction in Emissions - Scenario 2 (Medium)

Reduction in Emissions - Scenario 3 (Slow)

70

5 DISCUSSION

In this chapter, a discussion of the results and the conclusions that have been drawn during this

thesis is presented. The conclusions are based on the reasoning and arguments invoked during the

analysis and aim to answer the thesis questions presented in the introductory chapter.

5.1 Prospect of Electric Road Systems Compared to the

Conventional Road Transportation System

The results obtained in this thesis for the economic comparison between an ERS based

transportation structure against the current conventional road transportation system are scrutinized

and analyzed in this section for a global and a Swedish perspective.

5.1.1 The Global Perspective

Perhaps the most significant finding revealed by the first model is that an ERS based transportation

system is, in fact, not suitable for every country in the world. This result, albeit being rather

intuitive, still displays that ERS call for certain requirements in a country to be feasible. However,

that been said, looking at Figure 33 it can be seen that a large number of countries in the world

were indeed found to have good prospects for ERS from an economic standpoint. The technology

which was found to result in savings in most number of countries, compared to the current

petroleum-based system, is the conductive road ERS technology followed by overhead conductive

and inductive road ERS technologies. Furthermore, the figure illustrates that the number of

prospective countries for each technology increases with decreasing battery cost. This is a logical

outcome as a lower cost of battery will unsurprisingly also decrease the overall investment cost

for an ERS based transportation system thus making it suitable for even more countries.

However, an interesting deduction that can be derived from viewing the world map over

prospective countries (Figure 36 and Figure 37) is that most of these countries are either small

and/or developed countries. This can be explained by considering the fact that the higher the car

density is in a country, the cheaper the implementation of an ERS becomes. This is directly caused

by the fact that low car densities lead to larger optimal grid-sizes (∆) in the countries, which in

turn results in a need for larger batteries. These parameters together often result in that the yearly

cost of the ERS solutions becomes more expensive than the yearly cost of the current road

transportation system. Consequently, from this it can be said that rich and developed countries,

who typically have high car density, are usually good candidates for ERS. This is further supported

by viewing Figure 33 and Figure 34, which illustrates that even though not more than 2/3 of the

countries at best are found suitable for ERS, the majority of the total global vehicle fleet can be

found in these countries. Hence, the region of the world with by far the most number of countries

suitable for ERS was found to be Europe and the region with least suitable countries was found to

be Africa.

Furthermore, these discoveries point to an interesting conclusion. As the number of vehicles in the

world most likely will keep increasing in the future, this could result in the fact that even more

countries will eventually become attractive for ERS solutions. Additionally, as oil prices

realistically will see an increase the future, this might also contribute to further the appeal for ERS.

However, this will most likely be negated by increasing electricity prices due to wider

71

implementation of renewable energy sources and possible imposition of taxes. The battery

development is another important factor that could affect ERS positively if battery prices continue

to decrease as dramatically as predicted. Thus considering these facts, it is safe to assume that the

ERS technologies will continue to become progressively more attractive.

Parameters that often are mentioned as problematic by sceptics are the huge investment costs, the

increase in electricity production required and the length of the conventional road that needs to be

electrified. Even though the investment costs for an ERS based transportation system are

considerable, when related to other major infrastructure investments such as rail road expansions,

they are found to be comparable. Furthermore, Table 4 and Table 5, display that the average

percent of the yearly BNP that an ERS implementation requires in suitable countries, is around a

few percent. While being a substantial amount of money, it is still not an unrealistic amount in

light of similar major infrastructure projects. Similarly, the length of road necessary to electrify

for the different ERS technologies is but a portion of the total road network available in the

countries. Lastly, it can be seen in the tables that in the worst case, approximately 1/5 of the current

electricity production will be required to power a full-scale ERS transportation sector. This might

seem like a lot at first, but when considering that the energy required from the entire road

transportation is included in this increase, the required electricity production growth could perhaps

be motivated.

To also include the environmental perspective, Figure 35 demonstrates that a considerable part of

the total GHG emissions in suitable ERS countries can be reduced by implementing the ERS

solutions. Naturally, the emission reduced from implementing the overhead conductive ERS

technology is considerably lower than conductive and inductive road technologies. This is due to

the fact that only heavy vehicles, which constitute only a small portion of the total vehicle fleet,

can utilize the overhead solution.

5.1.2 The Swedish Perspective

The results obtained for this comparison for Sweden in Figure 39 display that only the conductive

road technology will give positive yearly savings if considering the current battery pricing. Yearly

savings of approximately 12 billion SEK can be attained which point to that there are business

opportunities as well as benefits for the society to be gained. The main reason why the inductive

and the overhead conductive technologies experience no profit is simply due to the fact that the

battery pricing and the cost of electrifying the roads are is too expensive. Thus, the savings that

are acquired by using electricity instead of petroleum-based fuels are insufficient. However, in the

case where future battery pricing is used, the results are very different. For this case, all three ERS

technologies are projected to result in savings, but still the largest savings are made with a

conductive based ERS. One thing to note, however, is that as the overhead conductive transfer

technology can only be utilized by large/heavy vehicles, the total savings that can be made are

therefore considerably smaller than for conductive and inductive road systems. This is also true

for the other parameters computed in Table 10 and Table 11 on page 60.

Reflecting on these parameters, it is projected that the total investment cost for a full-scale

implementation of an ERS ranges between 2-16 % of the yearly Swedish GDP depending on the

ERS technology used and battery price scenario assumed. There is no doubt that this will require

considerable investments. However, when bearing in mind the economic and environmental gains,

72

decrease in energy usage and reduction in the oil-dependency that will be achieved, and in addition

further considering the Swedish goal of having a fossil-fuel free transportation sector until 2030,

the initial investment costs can thus be motivated as a necessary evil. Moreover, parameters

presenting the percent electricity production increase required and total road needed to be

electrified also show rather large numbers. Yet, bearing in mind the potential gains that can be

acquired, they are perhaps not unrealistic and could be achievable. Thus, it can be concluded that

with current gasoline/diesel prices in this report, all three ERS technologies are economically

viable if the battery prices come down to around 120 USD/kWh. But with current battery prices,

only conductive road technology seems to be a viable solution.

5.1.3 Validity and Limitations of the Model

The model developed for the comparison is a simplification of reality and is thus built on major

assumptions. However, any model developed that is required to mimic a real life system almost

always necessitates simplifications. It may be possible to nit-pick each and every assumption made

for this model to try and improve the models reliability towards the real life system. But, in the

end, one has to also develop a model that is easily understandable and which converts a complex

system into a manageable one. Thus, it is judged that the questions that the model was developed

to answer, which mainly was to get a notion of a) whether the different ERS technologies even are

viable solutions from predominantly an economic standpoint and b) which countries in the world

that have the worst/best prospect, were answered reasonably well. Furthermore, as mentioned in

the delimitation section, this model does in no way calculate the real total economic costs

associated to a full-scale ERS expansion and should not be assumed to do so. It merely aims to

compare the degree of difference in costs between the conventional transportation system and a

potential ERS based transportation system.

That being said however, there are a couple of major assumptions that could have been improved

further to increase the validity of the results. To begin with, one of the fundamental assumptions

that the model is built on is that the road network in all countries has been approximated as being

meshed quadratically and equally over the entire country land area. Even though it has been shown

in the frame of reference sections that many countries do indeed have very quadratically sectioned

road networks, this is not true for every country. Especially the not as developed countries and

countries with large land area usually have regions with dense road network and on the other hand

regions with little to no road network. For example countries such as China, Russia and Australia

have large regions that are completely uninhabited. Hence, it would make more sense to make a

model that only considers the inhabited land areas. This would most probably lead to even more

countries becoming suitable for ERS as less land area will be required to be electrified.

Another aspect that the current model does not consider is that the density of the road network, in

most countries, is not constant over the entire land area. Taking Sweden as an example, the road

network in the south is substantially denser than in the north. This would of course mean that the

vehicles used in the north would need much larger batteries due to there being less road available

to electrify. Hence, it would therefore perhaps make sense to implement dynamic grid-sizing in

the model which varies depending on the road network density. But, this would make the model

vastly more complex and the end result would possibly not be vastly different compared to now.

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Lastly, the additional costs added to enable vehicles to operate in ERS is assumed to simply be the

battery plus the pickup arm apparatus. In reality, however, the cost of for example the required

control and sensor system might be significant, especially for larger vehicles, and should be

incorporated. Furthermore, the costs of the different pickup arm apparatuses for the overhead

conductive and inductive ERS technologies were obtained through assumptions. Also here, the

real cost might be much higher, particularly for the inductive technology, and could result in

greater overall costs. All in all, the limitations with the assumptions discussed in this section were

judged to be necessary due to time and data limitation and acceptable for the overall validity of

the model.

5.2 Comparing a Pure Battery Electric Car Fleet against an

ERS based fleet

The results in this thesis for the economic comparison between a pure battery electric car fleet

against an ERS in combination with an electric car fleet is scrutinized and analyzed in this section

for a global and a Swedish perspective.

5.2.1 Global and Swedish Perspective

One of the major arguments used against an employment of ERS for the passenger car fleet is that

the battery technology by itself is sufficient enough as the energy source. Consequently, it is argued

that there is no need of implementing costly ERS as adequate driving range, power output and

acceptable recharge time can be attained from batteries alone. These arguments are also often used

by people who think that ERS are only going to be feasible for heavy vehicles. Taking these

arguments into consideration, the model developed for this comparison had the intention to see the

difference in the costs between the two systems and thus, to identify which future model that would

be the most economic.

Looking at the results summarized in Table 6 to Table 9 for the global perspective, it is quite

apparent that the ERS option, considering a full-scale implementation, is significantly less

expensive than having pure battery based electric passenger cars. Depending on which ERS

technology and battery cost scenario that is considered, the number of countries where ERS is

found cheaper varies. However, even when considering the most expensive ERS technology

(inductive road) and future battery pricing, it can be seen that the ERS solution is less expensive

in almost 90 % of the studied countries. Furthermore, the cost of a pure battery electric car fleet is

about 17 times higher in the most costly case and 4.5 times in the least costly case depending on

the battery scenario and ERS technology regarded.

Similar results were obtained for Sweden, as shown in Table 12 and Table 13. They also display

that the ERS solution is roughly between 5 to 16 times less costly than the pure electric car scenario

reliant on the battery cost and ERS technology considered. Furthermore, Figure 40 and Figure 41

illustrate the number of electric cars required before an ERS based transportation system becomes

cheaper than a pure battery electric one. It is quite apparent that not many electric cars are needed

in the transportation system before the ERS option becomes less expensive. Even though, the

required number of cars increases with more expensive ERS installation costs and decreasing

battery prices. When considering the entire passenger fleet, the number of cars required is still only

a fraction of the total amount. For this model, the acceptable range of an electric car battery was

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assumed to be 500 kilometer. Some might argue that this is way too much and that studies have

shown that most users can manage with much less. Nevertheless, even when applying a shorter

acceptable range, the cost of a pure battery electric car fleet would still be many times more

expensive.

In view of the findings attained by this model, it is safe to conclude that the best alternative out of

the two is the ERS in combination with electric passenger cars, at least, if viewed from a purely

economic perspective. So is it reasonable to predict that the future passenger car fleet will surely

utilize ERS as it is by far the least expensive option? This question, cannot of course be answered

with certainty as it depends on a number of unpredictable factors. Looking from an economical

perspective, the answer to this question would most undoubtedly be yes. However, as there are

numerous cases throughout history where the most technical sound and/or the cheapest solution

has not ended up being the winner in a market, there is a possibility that this might also happen to

an ERS based passenger car fleet.

Another aspect that could potentially work against an ERS solution is the fact that in order for the

ERS to work efficiently, a large number of stakeholders have to be coordinated and organized.

This might be an obstacle as the current transportation system is much more flexible and

stakeholders such as the automotive manufacturers might not be easily convinced to adapt to the

new system. Rationally, it would make more sense for the automotive firms to continue with a

system that does not require a huge amount of synchronization with other markets. Manufacturing

pure battery electric cars could due to this fact be a simpler and more logical future for the

automotive firms.

This speaks in favor of seeing this as it perhaps being down to the governments in each country to

facilitate and coordinate the various stakeholders in developing and implementing an ERS which

includes passenger cars. Without a governing agency managing and driving the development

forward, it will be difficult to implement ERS solutions for passenger cars due to the complexity

and size of the system required.

5.2.2 Validity and Limitations of the Model

Also this model, as discussed for the previous one, is naturally affected by certain simplifications.

Apart from the major limitations discussed for the previous model, which also apply here, there

are a couple of specific assumptions for this model that need to be discussed. To start with, one

major assumption made was that the cost of an electric car without the battery is the same

regardless of the size and weight of the battery that will be used in the vehicle. This, of course, is

not the case in reality. For the pure battery electric car case, which requires a battery with a range

of up to 500 kilometers, this would result in a battery weighing roughly 800 kilograms. This extra

weight would require the structural construction of the electric cars to be more robust and sturdy,

thus leading to increased manufacturing costs. Moreover, the additional weight would lead to

increased rolling resistance due to friction and would thus require higher effect per kilometer from

the batteries to operate. This would very likely result in increased overall costs as well.

Consequently, this means that the actual cost of a pure battery electric based car fleet would be

higher than the cost that has been computed by the model.

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Similarly, when approximating the infrastructure costs for the pure electric car based

transportation system, certain educated guesses were made. It was hypothesized that the fast

charging stations were the only infrastructure investments that would be required. Hence, other

potential infrastructure investments such as battery swapping stations and normal charging stations

were ignored. The number of fast chargers needed was assumed to be the same as the gas pumps

required today. In reality, it is rational to expect that there will be a need for more fast charging

stations compared to the number of gas pumps. This is due to a number of reasons. Firstly, the

time needed to charge a battery sufficiently with fast charging technology is roughly 30-40

minutes. This is considerably longer than the time needed for refilling gas in a conventional vehicle

and will therefore lead to a requirement for more fast chargers. Similarly, the driving range of

electric cars is usually less than the average driving range of a conventional car, this would result

in a need for more frequent charging, leading to a requirement for a greater number of fast chargers

compared to the gas pumps today.

On the contrary, electric cars have the advantage that they can be charged anywhere with a wall

socket. This could mean that many normal users would usually charge their cars at home during

downtime, and thus nearly never utilize the fast charging stations. Such a scenario would instead

lead to a case where less fast charging stations would be required compared to the current number

of gas pumps. Whichever case is the correct one, there is no doubt that further research needs to

be carried out in this area. Either way, it was found that the costs caused by infrastructure

investment were very small compared to the battery investment costs which stood for almost the

entire total cost of a pure battery electric based transportation system. Thus, it can be concluded

that computing the correct number of fast chargers required is insignificant for the end results.

Even though there are obviously a couple of major problems and limitations with the model

developed. It is concluded, nonetheless, that the comparison of the two transportation system

scenarios was performed satisfactorily. The most probable result of improving the model would

still be that the ERS and electric car combination would be found to be the most economic option.

5.3 Breakeven point and Expansion

In this section, the findings acquired from the models developed to find the investment breakeven

point and simulate the different expansion scenarios are discussed and analyzed.

5.3.1 Breakeven Point

The findings attained for the investment breakeven point for the three ERS technologies are

presented in Figure 42 and Figure 43. The figures show quite clearly, as expected, that the

minimum frequency of vehicles per day required for the conductive road technology is

substantially less than for the inductive road technology. This simply means that a lot more

kilometer of road can be electrified in the conductive case with profit compared to the inductive

case. By considering Figure 31, this fact can be understood. The figure illustrates that the traffic

intensity as a function of road length can be estimated as being a hyperbole shaped curve.

Consequently, this means that the higher the vehicle frequency required, the less of the road

network which results in cost savings there is to be electrified. Thus, it can be assumed, that the

most logical approach when electrifying the road network would naturally be to start with the most

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traffic intensive road sections. As they would lead to the largest cost savings which could thus be

used to finance ERS installations on less traffic intensive roads.

The findings show quite plainly that not all the roads with ERS will produce cost savings. Even

less so, if the cost of the ERS technology is increased. As a consequence, the road sections with

high traffic intensity will be required to subsidies the construction and the operation of roads with

low traffic intensity. However, this is not a unique discovery. There are more than a few examples

in our society where regions with high cost savings facilitate the expansion and operation of said

technology in regions with low or no profits. An example of this can be found by viewing the

expansion of the mobile network in Sweden.

5.3.2 Expansion Scenarios

Viewing the results accumulated for the different scenarios and the three technologies in Figure

44 to Figure 48, they show that Scenario 1, which simulates a fast expansion, is the best alternative

from both an environmental and economic standpoint when considering a time period until 2050.

Even though the initial investment in both the expansion of ERS and electric vehicles is the highest

in this scenario, the largest return can also be yielded until 2050. Looking at the results for each

individual technology, starting with the conductive road technology, it can be seen that profit can

be made from all three scenarios. This result is primarily due to the low ERS investment cost and

the fact that all motor vehicles can utilize the technology. Secondly, for the overhead conductive

ERS, the results display that only the first two scenarios lead to cost savings. But for the slow

expansion scenario, no profit can be made until year 2050. Here, the relatively low investment

costs combined with the vehicle size restriction can be the explanations for the findings.

Finally, for the inductive road ERS technology, all scenarios were found to result in losses for the

entirety of the studied time period. The conclusion that can be drawn from this is that the current

investment cost for this technology is simply too high. At least, this is the case, when viewing the

expansion from an electrified road perspective. In Figure 47, the accumulated results for each ERS

technology acquired from Scenario 1 shows quite clearly the differences between the technologies.

As expected, the largest cost savings are to be made from an expansion of the conductive road

technology. However, even though the cost savings from the overhead conductive technology

appear to be vastly smaller. When considering the fact that only heavy vehicles can use this

technology, the savings attained are undeniably significant in this case.

To sum up, the major conclusions that can be draw from this model is that a fast expansion and

implementation of an ERS based transportation sector is the best tactic. The substantial initial

investments are rather quickly refunded so that considerable savings can be made until year 2050.

5.3.3 Validity and Limitations of the Model

The expansion model, unsurprisingly, also is affected by numerous simplifications and limitations.

Many of the inputs used were assumed to be constant over time, even though that was not always

the case. For example, the yearly vehicle kilometer travelled will most probably increase, as the

total number of motor vehicles in Sweden will continue to grow. This is also true for the number

of new vehicles per year. Making other assumed constant values such as the fuel and electricity

prices more dynamic would perhaps increase the accuracy of the model. To summarize, it can be

said that the major limitation of the model is possibly that too many inputs were assumed to be

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constant when in reality they should be more dynamic. However, it was found that creating a more

dynamic expansion model would be very complex and time consuming. Additionally, as the goal

of the simulations is just to get a notion of which scenario will have the best outcome, it was

determined that a more complex model would not necessarily lead to better results. Subsequently,

a judgement was made that the current model provides sufficiently accurate results for the

expansion simulations.

5.4 Discussing the Electric Road System Technologies

Now that the findings from each model have been discussed and analyzed for the three ERS

technologies, it is time to compare the three technologies and try to define an answer to the

question: Which ERS technology is the best alternative?

The results in all the models indicate that from an economical and environmental perspective, the

conductive road technology seems to be the best option. The low investment costs of the

conductive ERS coupled with the fact that it can be universally used by all motor vehicles result

in a great combination. Even though the inductive technology share these advantages, the steep

investment cost is an obstacle. The overhead conductive transfer technology on the other hand,

has a fairly low investment cost but does however, restrict which type of motor vehicles that can

utilize the system. This factor makes the technology less appealing compared to the conductive

road technology, at least, if regarded from an overall economic and environmental perspective.

Yet, it is important to note that the overhead conductive technology is specifically developed to be

used by heavy vehicles, its goals and market segment focus is thus different from the other two

technologies. Consequently, this could very well mean that the overhead road technology could

be preferred by the heavy vehicle industry as the economic gains are still substantial and it is

possible that the technology will only need to be implemented on certain road sections where the

intensity of heavy vehicles is high, and not everywhere as assumed in this study.

There are naturally certain real life aspects that the models cannot capture. To begin with, as

discussed previously, the synchronization and cooperation required between the different

stakeholders for an ERS to work properly is considerable. Thus, there is no doubt that substantial

influence and supervision will be required from the governments to obtain a successful

implementation. If not obtained, there is a large probability that the passenger car manufacturers

will not opt to choose the ERS solution, but would rather choose the, for them, simpler solution of

manufacturing pure battery electric cars.

This option, however, is not possible for heavy vehicles such as trucks and buses due to the

limitation with the current battery technology. Hence, it can be predicted from this that the heavy

vehicle market will be the driving force in the development and implementation of ERS. As a

result, this could lead to the fact that the ERS technology chosen will be the one which is best

suited for heavy vehicles, whether it be conductive road, overhead conductive or inductive road

technology. Subsequently, even though the computations in the model indicate quite clearly that a

universally motor vehicle including technology is the strongest option economically and

environmentally, the end result (if not enough governmental engagement is attained) might thus

be that a technology which excludes certain vehicle categories might be implemented due to

market forces.

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Analyzing the technical info compiled for each technology, it is quite apparent that they all have

both disadvantages and advantages. The maturity of the overhead technology makes it particularly

appealing from an investment point-of-view, as the technology has previously proven to work and

thus would not require substantial risks to implement. Yet, there are still new technological

developments that even the overhead conductive system has needed to cultivate to obtain a

workable ERS. The sceptics of this technology argue that the fact that only a small part of the

vehicle fleet can utilize this technology is a major drawback as it puts a restriction on the usability

of the ERS. Furthermore, the obvious risks, such as extreme weather, associated with high-hanging

cables is another argument used. Even though the companies producing overhead conductive

technologies have proven that these risks can be handled, sceptics might still argue over the

functionality of exposed electricity cables. Especially when in parallel, the Swedish power

companies are working to dig down all the currently exposed electric lines due to safety and

practicality concerns. One more argument against the overhead system is that it is often found to

be unpleasing from an aesthetic point of view.

Both the conductive and inductive road technologies are comparatively new and have thus far only

been demonstrated to work under test-like conditions. This is particularly true for the conductive

road technology due to it being a rather novel technology. However, the conductive road has in

this study shown to be the least expensive ERS system that caters to all vehicle categories. The

major drawback of the system is that the rail trenches might be affected by weather conditions,

which sceptics point out as a potential obstacle. Similarly, the ever present risk of electrocution

from the rails is another drawback recognized. But these drawbacks have been demonstrated as

being surmountable, at least on an experimental level on test-tracks. Taking a closer look at the

inductive road technology, it has the advantage of being the most aesthetically pleasing out of the

three and also being almost fully immune to weather conditions. The technology does, however,

require substantially more in investment costs and road renovations compared to the other ERS

technologies. More technical specifications such as global efficiency, pick-up arm technology,

system lifespan, etc. were found to be relatively similar and/or based on estimations.

To summarize the arguments against and for the three technologies discussed in this section, Table

14 has been produced which displays the pros and cons from an economic, environmental and

aesthetic standpoint.

Table 14. The pros and cons of each technology from an economic, environmental and aesthetic perspective.

Conductive Road Overhead Conductive Inductive Road

Economic Perspective ++ + -

Environmental Perspective ++ + ++

Aesthetic Perspective + - ++

In conclusion, it can be said that all three technologies have pros and cons, where they all have

shown on an experimental level, that the problems associated with the individual technologies can

be dealt with. Thus, before a more definitive answer concerning which ERS technology is the best

from a technical perspective can be made, more information from the larger demonstration projects

currently underway, needs to be acquired.

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5.5 Implications for Governmental Policies and Strategies

Most countries in the world want to reduce their transportation sector dependency on fossil fuels,

this is particularly true for the European countries and Sweden. As mentioned earlier, Sweden has

set a rather bold goal of achieving a fossil fuel neutral transportation sector until the year 2030.

ERS in combination with electric vehicles have been shown, in this report, to be a possible solution

to this conundrum. However, as discussed in the previous sections, a full-scale implementation of

ERS would be difficult due to path dependencies of our current transportation sector and the

complexity of achieving a well-functioning cooperation between the various ERS stakeholders.

Thus, it can be concluded that active governmental commitment and guidance might be required

in achieving an effective implementation and operation of ERS. It is indicated that perhaps the

market forces by themselves will not be enough in overcoming these difficulties to attain an ideal

ERS, especially if the passenger cars are to be included. The inconvenience of being dependent on

other stakeholders for their cars to function properly might deter passenger car manufacturers from

an ERS path if not appropriate incentives and policies are offered by the governments.

Furthermore, the findings point toward that the best expansion scenario might be a rapid expansion

approach. This not only requires massive cooperation between the different ERS stakeholders but

also require huge initial investments. Even though these in part can be acquired through private

investors, it is rather obvious that an extensive governmental commitment will be essential.

Additionally, as the electric vehicle growth will also need to increase substantially, the dropping

battery prices might not be enough initially to drive this rapid growth and relevant governmental

subsidies might be required. These reasoning’s are supported by the findings published by

Bloomberg New Energy Finance regarding the governments roll being essential in achieving a

widespread adoption of EVs as shown on page 21. It is judged that it might not be a huge leap to

assume that this might also apply for a prevalent adoption of ERS in combination with EVs.

Before an implementation on a full-scale can be realized however, it is of course important that a

standardization of the chosen ERS technology is made on a preferably global scale but at the very

least on a European scale. It is important that the flexibility of being able to travel over borders

without any constraints is conserved, since our current society is dependent on this fact to function

properly. A standardization will furthermore simplify the collaboration between the stakeholders

as each could develop solutions that fit together with the system as a whole instead of being

required to develop various different technical solutions. As a result, it is important that the

governments use the ongoing demo projects for the different ERS technologies to decide on one

technology, which is found to be the best from a technical, economic and environmental

perspective, before initiating a full-scale expansion.

Lastly, countries that are the forerunners in the development of the ERS could potentially acquire

huge exportation opportunities in the future. Sweden is currently one of the world leaders in the

ERS development and thus has a good opportunity to become the leading exporter of ERS related

knowledge and technology. This huge economical market prospect is just another incentive out of

many that motivate a high governmental engagement in the ERS field.

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6 CONCLUSIONS

It is important to note that the conclusions found in this report are based on the specific

assumptions and limitations made for the models and thus, other inputs could possibly lead

to different findings and conclusions.

It was found that an ERS based transportation system is not economically viable in every country

in the world when compared to the current conventional road transportation system. However, a

large number of countries in the world were indeed found to have good prospects for ERS from

an economical and environmental perspective, where the conductive road was found to be the most

attractive ERS technology followed by overhead conductive and inductive road ERS technologies.

It is worth mentioning that the current model considers the entire land area of each country when

computing the economic viability. Subsequently, even though implementing ERS in certain

countries might not be economically sound when considering the whole land area, parts of the

countries could still result in cost savings which unfortunately the current model cannot exhibit.

Findings further indicated to the fact that small and/or developed countries are best suited for ERS

implementation, thus the most attractive region for ERS was found to be Europe and the least

attractive to be Africa. It was found that future developments such as increasing number of

vehicles, decreasing battery prices and oil prices will most likely result in more countries

eventually becoming suitable for ERS.

For Sweden the findings show that only the conductive road transfer technology results in saving

with current battery prices. However, with future battery pricing, all the ERS technologies are

found to be viable with conductive road still resulting in the largest profit. Almost a 90 % reduction

in emission from the transportation sector could potentially be achieved by implementing

conductive road and inductive road ERS technologies. Considerably less reduction is achieved

from the overhead conductive technology (23 %) due to the vehicle usage restrictions.

The comparisons between a pure battery electric car fleet and an ERS based electric car fleet show

rather clearly that the ERS approach is the cheapest option from both a global and a Swedish

perspective. However, governmental influence and incentive is most probably required for the

passenger car industry to embrace an ERS approach as the current market is heading towards a

pure battery electric car future.

The breakeven model shows quite clearly that not all the roads with ERS will produce cost savings.

As a consequence, the road sections with high traffic intensity will be required to subsidies the

construction and the operation of roads with low traffic intensity. Hence, logically the expansion

of ERS should start with electrifying the most traffic intensive road sections.

The expansion model points towards that a fast expansion and implementation of an ERS based

transportation sector is possibly the best approach. The substantial initial investments are rather

quickly refunded so that considerable cost savings can be made until year 2050. Also for the

expansion scenario, the conductive road ERS technology was found to be the most attractive in

this study.

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The results acquired in all the models indicate that from an economical and environmental

perspective, the conductive road technology is the best option. Furthermore, it can be concluded

that the heavy vehicle market will be the driving force in the development and implementation of

ERS as the current battery technology is insufficient.

It is reasoned that an active governmental commitment and guidance might be required in

achieving an effective implementation and operation of ERS as the market forces by themselves

might possibly not be enough. Additionally, a standardization of the chosen ERS technology is

important before a full-scale implementation can be initiated.

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7 RECOMMENDATIONS FOR FUTURE WORK

The modelling approach developed in this thesis could potentially be further improved by

implementing dynamic grid-sizing which would vary depending on the road network density.

Additionally, using the inhabited land area instead of total land area for each country might lead

to more accurate results.

The reliability and validity of the expansion model could be further improved by converting the

assumed constant inputs such as fuel prices and electricity prices into forecasted dynamic inputs.

A number of scenarios where the dynamic inputs could be varied depending on different predicted

futures could hence be produced.

The current models do not take into consideration that when vehicles are travelling on the

electrified roads, the efficiency becomes higher than when using battery drive. As this results in a

lower energy consumption per kilometer for the vehicles, this would mean that also the real overall

electricity consumption would be lower in a full-scale ERS than computed in this model. Finding

a way to implement this improvement could lead to more precise results.

Energy regenerative systems that recover for example the energy from braking can be implemented

in certain ERS technologies. This would of course lead to a decrease in the overall energy

consumption and potentially also result in bigger savings. It would therefore be interesting to

develop a model that included various regenerative systems for the different ERS technologies.

In this thesis, only battery storage is considered as a hybridization alternative for vehicles in

combination with ERS. However, other hybrid solutions where the vehicles utilize an ICE or

hydrogen fuel cells can also be used in combination with ERS. A comparison between these

different alternatives could be a possible area to further explore.

Similarly, it would be interesting to not only compare the ERS technologies between each other

but also compare them against other potential alternative future road transportation systems based

on for example biofuels, hydrogen fuel cells etc. to identify the best solution.

This thesis demonstrates that there is economical profit to be made by implementing full-scale

ERS. However, there needs to be more research concerning how these cost savings will be divided

between the different stakeholders and the payment method that will be implemented for the users.

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89

9 APPENDIX

9.1 Appendix A – Assumptions

9.1.1 Overall Assumptions

The tank-to-wheel efficiency of petroleum-based vehicles was assumed to be between 16-

20 % depending on the vehicle category. However, the tank-to-wheel efficiency of electric

vehicles was assumed to be approximately 70 % for all the vehicles categories.

It was assumed that heavy vehicles accounted for approximately 6.5 % of the global yearly

vehicle kilometer.

The average mileage for all vehicle categories was computed to be approximately 13 000

kilometer. The average distance travelled for only heavy vehicles was found to be roughly

43 360 kilometer.

In this model, it has been assumed that all light duty trucks, heavy duty trucks and buses

only use Swedish MK1 diesel as fuel.

The energy density of batteries in 2015 was assumed to be 0.1 kWh/kg and in the future as

0.25 kWh/kg.

9.1.2 Model 1 – World

The total number of road motor vehicles was used as indata for each country. This includes

automobiles, SUVs, trucks, vans, buses, commercial vehicles and freight motor road

vehicles, where motorcycles and other two-wheelers were excluded. However, in this

thesis the total number of vehicles was split into four main vehicle types. Namely,

passenger cars, light-duty trucks, heavy-duty trucks and buses.

A payback method was used to compute the payback time of the investments. This method

was deemed to be adequate in getting a general idea of how long time it would take until

the investment would pay-off.

The acceptable driving range that an electric car needs to travel to be a viable competitor

against conventional vehicle was assumed to be a bit lower than the average driving

distance for conventional vehicle, which is roughly 600 kilometers. Therefore, for this

model, the minimum acceptable driving range for an electric passenger car was assumed

to be 500 kilometers.

The annual cost of an ERS based car fleet was assumed to be the same as in the previous

comparison model.

To approximate the infrastructure costs that would occur in a completely electric based

transportation sector, the cost of installing sufficient number of fast chargers instead of gas

pumps was calculated. To do this, firstly, the number of fuel stations in Sweden was found

to be 2670 [85]. From this, it was assumed that each of these stations on average hosted 5

90

gas pumps, this resulted in the fact that for each pump there are about 400 cars. Even though

in reality there might be a need for more fast chargers than the number of gas pumps today.

Due to reasons such as the fact that it takes longer time to refill batteries and their smaller

travelling range would lead to a need to charge more frequently. However, for this model

it was judged to be sufficient to assume that the same number of fast chargers as gas pumps

in Sweden today would be required. This will at least give an indication of the minimum

price for the installation of fast chargers during a best case scenario.

The computed value for the number of cars per fast charger for Sweden was assumed to be

the same in all the studied countries.

9.1.3 Model 2 – Sweden

The total road network length used was 214 600 kilometer, and included only asphalted

roads.

The highest value for the traffic intensity was chosen to be 160 000 vehicles/day, which is

the value for Sweden’s most heavily used road segment, namely Essingeleden

91

9.2 Appendix B – Comparison between Petroleum-based Road Transport vs ERS and EV

Combination

Table 15. Countries where the yearly savings from an ERS based transportation system are positive using the current battery price ((Conductive Road Case)..

Country Optimal grid-mesh size

Battery Size

Total Length of grid

% of Existing road network

Cost of grid per year

Car investment per year

Annual Electricity Cost

Annual Fuel Cost

Yearly Saving

% Of Electricity production

% Of yearly BNP

Total GHG Emission reduced

% of GHG Emission reduced

Total number of cars in these countries

% of Total Cars in the World

km kWh km MSEK/Year MSEK/Year MSEK/year MSEK/year MSEK/Year % % tons % %

World 15.2 3.6 4,324,717 20% 1,272,881 1,675,948 2,134,232 7,549,938 2,466,877 17.3% 2.7% 2,061,746,747 7.3% 995,878,405 78.52%

Monaco 0.6 0.1 7 8.5% 2 15 68 242 157 - 66,002 - 31,881

Singapore 2.2 0.5 623 18.2% 183 527 1,819 6,435 3,906 5.4% 0.3% 1,757,211 2.5% 848,779

Bahrain 2.5 0.6 616 14.9% 181 485 1,608 5,687 3,413 17.7% 1.7% 1,552,911 5.6% 750,097

Malta 2.5 0.6 250 8.1% 74 194 638 2,255 1,350 44.4% 3.1% 615,923 20.5% 297,507

Hong Kong 3.4 0.8 635 30.4% 187 416 1,212 4,288 2,473 4.4% 0.2% 1,171,077 - 565,661

Barbados 4.4 1.0 195 12.2% 58 112 286 1,013 558 40.8% 6.2% 276,730 7.8% 133,668

Netherlands 4.8 1.1 14,214 10.2% 4,184 7,812 19,213 67,968 36,758 28.9% 2.5% 18,560,671 8.5% 8,965,297

Lebanon 4.9 1.1 4,205 60.3% 1,238 2,289 5,570 19,702 10,606 51.6% 7.1% 5,380,364 22.1% 2,598,858

South Korea 5.1 1.2 38,271 36.0% 11,264 20,462 48,705 172,296 91,864 13.4% 2.9% 47,050,768 7.1% 22,726,770

Taiwan 5.1 1.2 12,712 30.7% 3,742 6,791 16,145 57,112 30,435 9.2% 1.6% 15,596,210 5.6% 7,533,383

Guam 5.2 1.2 209 20.0% 62 111 259 917 486 21.4% 6.0% 250,467 - 120,982

Belgium 5.4 1.3 11,313 7.3% 3,330 5,903 13,623 48,193 25,337 22.9% 3.2% 13,160,576 10.1% 6,356,908

Japan 5.4 1.3 134,172 11.0% 39,490 69,553 159,183 563,118 294,891 21.4% 4.0% 153,776,941 12.2% 74,278,344

Liechtenstein 5.6 1.3 58 15.2% 17 30 67 237 123 - 2.5% 64,599 - 31,203

Luxembourg 6.0 1.4 857 29.6% 252 425 915 3,237 1,645 59.1% 2.0% 884,001 7.3% 426,996

Israel 6.5 1.5 6,297 33.9% 1,853 3,040 6,285 22,235 11,056 14.3% 3.0% 6,071,927 7.0% 2,932,902

U.K. 6.6 1.5 73,730 18.7% 21,701 35,378 72,420 256,189 126,690 29.0% 3.6% 69,960,303 250.8% 33,792,683

Italy 6.6 1.5 89,313 18.3% 26,287 42,794 87,403 309,193 152,709 43.3% 5.4% 84,434,873 17.0% 40,784,285

Germany 6.6 1.6 104,876 16.3% 30,868 50,069 101,670 359,661 177,054 23.7% 3.6% 98,216,595 10.9% 47,441,222

Switzerland 7.1 1.7 11,300 15.8% 3,326 5,269 10,290 36,401 17,516 20.0% 3.0% 9,940,324 18.4% 4,801,440

Kuwait 7.1 1.7 5,002 75.7% 1,472 2,327 4,526 16,010 7,685 10.3% 2.2% 4,371,946 2.3% 2,111,766

Mauritius 7.4 1.7 549 25.6% 162 252 479 1,695 802 26.1% 2.9% 462,822 7.8% 223,555

Qatar 7.6 1.8 3,065 31.2% 902 1,395 2,612 9,242 4,332 9.7% 1.2% 2,523,687 3.4% 1,219,008

Trinidad and Tobago 8.0 1.9 1,282 15.4% 377 572 1,033 3,653 1,671 18.7% 3.6% 997,534 2.2% 481,835

Nauru 8.8 2.1 5 16.0% 1 2 3 12 5 16.7% 3.9% 3,378 3.8% 1,632

Poland 9.0 2.1 67,294 15.9% 19,807 28,866 47,970 169,697 73,054 41.7% 7.5% 46,341,208 12.3% 22,384,033

France 9.4 2.2 116,915 11.4% 34,411 49,539 80,104 283,371 119,316 20.6% 5.4% 77,383,281 15.1% 37,378,179

Cyprus 9.4 2.2 1,961 9.8% 577 831 1,341 4,745 1,996 41.9% 8.6% 1,295,892 14.6% 625,950

Czech Republic 9.5 2.2 16,210 12.4% 4,771 6,842 10,964 38,784 16,208 18.0% 6.0% 10,591,153 7.8% 5,115,808

San Marino 9.6 2.2 13 4.4% 4 5 9 31 13 - 0.8% 8,354 - 4,035

Denmark 9.7 2.3 8,779 11.9% 2,584 3,690 5,854 20,708 8,581 24.2% 4.1% 5,655,093 9.6% 2,731,560

92

Portugal 9.9 2.3 18,532 22.4% 5,455 7,740 12,102 42,810 17,513 32.8% 7.8% 11,690,517 15.9% 5,646,830

Austria 9.9 2.3 16,578 13.3% 4,879 6,908 10,744 38,006 15,475 22.7% 5.0% 10,378,799 12.2% 5,013,235

Spain 10.5 2.5 95,201 13.9% 28,020 39,076 58,541 207,090 81,453 29.3% 7.0% 56,552,400 16.1% 27,316,310

Slovenia 10.7 2.5 3,768 8.6% 1,109 1,538 2,271 8,032 3,115 22.0% 7.1% 2,193,489 11.2% 1,059,513

Saint Lucia 10.9 2.6 112 9.2% 33 45 66 235 90 31.2% 6.5% 64,054 5.8% 30,940

Jamaica 11.1 2.6 1,948 8.8% 573 787 1,129 3,996 1,506 34.0% 9.4% 1,091,104 9.7% 527,032

Antigua and Barbuda 11.2 2.6 79 6.8% 23 32 46 162 61 62.2% 4.5% 44,159 4.2% 21,330

Saint Kitts and Nevis 11.2 2.6 47 12.2% 14 19 27 95 36 30.7% 4.1% 25,938 7.2% 12,529

Greece 11.6 2.7 22,570 19.3% 6,643 9,016 12,566 44,454 16,229 30.8% 9.4% 12,139,601 11.1% 5,863,749

Brunei 12.0 2.8 875 28.9% 258 346 469 1,658 586 19.1% 3.1% 452,823 2.2% 218,726

Slovakia 12.1 2.8 7,951 20.4% 2,340 3,140 4,235 14,983 5,267 21.2% 5.8% 4,091,510 9.4% 1,976,308

Grenada 12.6 3.0 55 4.9% 16 21 28 99 34 26.7% 4.6% 27,108 1.4% 13,094

Hungary 12.6 3.0 14,209 7.1% 4,182 5,553 7,261 25,688 8,691 34.2% 6.4% 7,014,842 10.4% 3,388,355

Seychelles 12.7 3.0 72 14.1% 21 28 37 129 44 - 3.3% 35,353 4.6% 17,077

Ireland 13.1 3.1 10,524 10.9% 3,098 4,076 5,183 18,333 5,977 29.2% 4.9% 5,006,512 8.2% 2,418,278

Maldives 13.2 3.1 45 51.5% 13 18 22 79 25 18.8% 1.1% 21,437 1.8% 10,355

United Arab Emirates 13.2 3.1 12,698 311.2% 3,737 4,911 6,216 21,990 7,125 8.0% 2.3% 6,004,974 0.1% 2,900,562

Malaysia 13.3 3.1 49,255 34.1% 14,497 18,990 23,791 84,161 26,883 25.8% 7.0% 22,982,854 8.1% 11,101,328

Croatia 14.5 3.4 7,730 26.3% 2,275 2,925 3,441 12,172 3,531 48.4% 9.8% 3,323,835 12.1% 1,605,500

U.S. 14.5 3.4 1,262,903 19.2% 371,707 477,833 561,938 1,987,878 576,401 18.7% 8.0% 542,852,359 142.5% 262,212,099

Dominican Republic 14.6 3.4 6,618 33.6% 1,948 2,499 2,921 10,333 2,965 31.9% 5.1% 2,821,838 9.0% 1,363,022

El Salvador 14.7 3.5 2,821 25.9% 830 1,064 1,238 4,380 1,247 29.5% 6.1% 1,196,132 9.4% 577,763

Thailand 14.8 3.5 69,057 38.4% 20,325 26,007 30,085 106,426 30,009 26.1% 7.1% 29,063,002 8.4% 14,038,201

Serbia 14.9 3.5 10,395 23.5% 3,059 3,908 4,495 15,901 4,438 17.9% 12.2% 4,342,254 7.2% 2,097,424

Romania 15.1 3.5 30,494 36.2% 8,975 11,437 13,036 46,117 12,668 31.4% 8.4% 12,593,590 9.8% 6,083,038

Turkey 15.2 3.6 101,533 23.8% 29,884 38,037 43,171 152,718 41,626 25.8% 7.3% 41,704,503 10.9% 20,144,382

Tonga 15.2 3.6 94 13.8% 28 35 40 141 38 105.5% 20.5% 38,514 10.1% 18,603

Bulgaria 15.3 3.6 14,185 35.3% 4,175 5,304 5,978 21,147 5,690 19.4% 12.1% 5,774,908 9.4% 2,789,434

Lithuania 15.4 3.6 8,156 9.7% 2,401 3,046 3,420 12,100 3,232 102.2% 11.3% 3,304,201 15.3% 1,596,017

Sri Lanka 15.7 3.7 8,244 7.2% 2,427 3,067 3,390 11,991 3,108 40.4% 4.0% 3,274,400 7.9% 1,581,622

Kiribati 17.1 4.0 95 14.2% 28 35 36 127 28 568.2% 62.7% 34,580 43.2% 16,703

Moldova 17.7 4.2 3,723 39.8% 1,096 1,352 1,358 4,805 999 35.6% 23.4% 1,312,157 10.4% 633,806

China 17.8 4.2 1,047,508 23.5% 308,310 379,923 379,188 1,341,395 273,974 9.3% 6.0% 366,309,862 3.8% 176,937,386

Mexico 18.2 4.3 213,832 56.4% 62,937 77,254 75,808 268,175 52,177 36.9% 10.7% 73,233,659 10.7% 35,373,801

Ukraine 18.8 4.4 61,497 36.3% 18,100 22,074 21,039 74,427 13,214 15.5% 20.4% 20,324,684 3.5% 9,817,362

Belarus 18.9 4.4 21,519 22.7% 6,334 7,723 7,356 26,021 4,609 33.7% 14.2% 7,105,843 7.3% 3,432,311

Costa Rica 18.9 4.4 5,403 13.8% 1,590 1,938 1,842 6,518 1,147 26.6% 8.1% 1,779,876 11.7% 859,728

Bosnia and Herzegovina 19.5 4.6 5,262 23.0% 1,549 1,878 1,744 6,168 998 17.1% 14.8% 1,684,497 6.2% 813,657

Dominica 19.5 4.6 77 5.1% 23 28 26 90 15 45.5% 10.4% 24,640 10.7% 11,902

Estonia 20.2 4.8 4,191 7.2% 1,233 1,486 1,335 4,724 670 16.4% 12.2% 1,290,061 6.0% 623,134

Jordan 20.4 4.8 8,688 120.6% 2,557 3,075 2,740 9,692 1,320 28.4% 11.5% 2,646,619 10.3% 1,278,387

Montenegro 20.5 4.8 1,315 16.9% 387 465 415 1,467 199 23.2% 14.7% 400,527 11.4% 193,465

Azerbaijan 21.2 5.0 7,793 14.7% 2,294 2,741 2,369 8,379 976 14.6% 4.9% 2,288,210 3.9% 1,105,266

Albania 21.4 5.0 2,561 14.2% 754 900 772 2,730 305 28.4% 8.7% 745,422 10.5% 360,059

Sweden 22.0 5.2 37,387 6.5% 11,004 13,077 10,979 38,838 3,778 9.8% 8.8% 10,605,966 17.1% 5,122,963

New Zealand 22.3 5.3 23,987 25.5% 7,060 8,368 6,927 24,504 2,149 22.9% 15.8% 6,691,456 9.4% 3,232,151

Comoros 22.5 5.3 199 22.6% 59 69 57 202 17 407.7% 17.9% 55,142 19.7% 26,635

93

Armenia 23.6 5.5 2,394 30.7% 705 828 655 2,317 129 13.2% 10.3% 632,741 8.9% 305,631

Guatemala 23.8 5.6 8,988 78.2% 2,645 3,104 2,430 8,595 416 41.5% 7.8% 2,347,202 6.8% 1,133,761

Iran 24.0 5.6 127,685 59.7% 37,581 44,060 34,308 121,364 5,415 18.6% 10.0% 33,142,338 4.7% 16,008,629

Philippines 24.2 5.7 24,661 11.3% 7,258 8,500 6,574 23,255 923 12.4% 3.6% 6,350,591 4.3% 3,067,504

Norway 24.2 5.7 25,108 26.7% 7,390 8,651 6,677 23,621 903 7.1% 7.7% 6,450,426 12.6% 3,115,727

Latvia 24.5 5.8 5,082 7.1% 1,496 1,748 1,337 4,730 149 32.4% 11.0% 1,291,610 10.0% 623,882

Finland 24.5 5.8 24,797 5.5% 7,298 8,530 6,523 23,075 723 13.1% 12.0% 6,301,250 8.1% 3,043,671

Indonesia 24.6 5.8 147,168 29.6% 43,316 50,593 38,533 136,310 3,870 25.5% 5.6% 37,223,826 4.6% 17,980,096

Tunisia 25.6 6.0 12,120 62.4% 3,567 4,143 3,047 10,780 23 28.6% 10.3% 2,943,736 8.4% 1,421,903

94

Table 16. Countries where the yearly savings from an ERS based transportation system are positive using the future battery cost (Conductive Road Case).

Country Optimal grid-mesh size

Battery Size

Total Length of grid

% of Existing road network

Cost of grid per year

Car investment per year

Annual Electricity Cost

Annual Fuel Cost

Yearly Saving

% Of Electricity production

% Of yearly BNP

Total GHG Emission reduced

% of GHG Emission reduced

Total number of cars in these countries

% of Total Cars in the World

km kWh km MSEK/Year MSEK/Year MSEK/year MSEK/year MSEK/Year % % tons % %

World 33.0 7.8 3,553,536 11% 1,045,902 1,511,625 2,465,996 8,723,566 3,700,045 16.7% 1.7% 2,382,242,687 6.7% 1,150,686,451 90.73%

Monaco 1.0 0.2 4 5.1% 1 14 68 242 158 - 0.4% 66,002 - 31,881

Singapore 3.7 0.9 372 10.9% 110 453 1819 6435 4053 5.4% 0.2% 1,757,211 2.5% 848,779

Bahrain 4.1 1.0 368 8.9% 108 412 1608 5687 3559 17.7% 1.4% 1,552,911 5.6% 750,097

Malta 4.2 1.0 149 4.8% 44 164 638 2255 1410 44.4% 2.4% 615,923 20.5% 297,507

Hong Kong 5.7 1.3 380 18.2% 112 341 1212 4288 2624 4.4% 0.2% 1,171,077 - 565,661

Barbados 7.4 1.7 117 7.3% 34 88 286 1013 604 40.8% 4.5% 276,730 7.8% 133,668

Netherlands 8.0 1.9 8,495 6.1% 2500 6129 19213 67968 40125 28.9% 1.8% 18,560,671 8.5% 8,965,297

Lebanon 8.1 1.9 2,513 36.1% 740 1791 5570 19702 11602 51.6% 5.1% 5,380,364 22.1% 2,598,858

South Korea 8.5 2.0 22,871 21.5% 6732 15930 48705 172296 100929 13.4% 2.1% 47,050,768 7.1% 22,726,770

Taiwan 8.5 2.0 7,597 18.3% 2236 5285 16145 57112 33446 9.2% 1.1% 15,596,210 5.6% 7,533,383

Guam 8.7 2.0 125 12.0% 37 86 259 917 535 21.4% 4.3% 250,467 - 120,982

Belgium 9.0 2.1 6,761 4.4% 1990 4563 13623 48193 28017 22.9% 2.3% 13,160,576 10.1% 6,356,908

Japan 9.1 2.1 80,183 6.6% 23600 53663 159183 563118 326671 21.4% 2.8% 153,776,941 12.2% 74,278,344

Liechtenstein 9.3 2.2 34 9.1% 10 23 67 237 137 - 1.7% 64,599 - 31,203

Luxembourg 10.1 2.4 512 17.7% 151 324 915 3237 1848 59.1% 1.4% 884,001 7.3% 426,996

Israel 10.8 2.5 3,763 20.3% 1108 2295 6285 22235 12547 14.3% 2.1% 6,071,927 7.0% 2,932,902

U.K. 11.0 2.6 44,062 11.2% 12969 26646 72420 256189 144154 29.0% 2.5% 69,960,303 250.8% 33,792,683

Italy 11.0 2.6 53,375 10.9% 15710 32216 87403 309193 173864 43.3% 3.7% 84,434,873 17.0% 40,784,285

Germany 11.1 2.6 62,675 9.7% 18447 37648 101670 359661 201896 23.7% 2.5% 98,216,595 10.9% 47,441,222

Switzerland 11.8 2.8 6,753 9.5% 1988 3931 10290 36401 20192 20.0% 2.1% 9,940,324 18.4% 4,801,440

Kuwait 11.9 2.8 2,989 45.2% 880 1735 4526 16010 8870 10.3% 1.5% 4,371,946 2.3% 2,111,766

Mauritius 12.4 2.9 328 15.3% 97 187 479 1695 932 26.1% 2.0% 462,822 7.8% 223,555

Qatar 12.7 3.0 1,831 18.6% 539 1032 2612 9242 5058 9.7% 0.8% 2,523,687 3.4% 1,219,008

Trinidad and Tobago 13.4 3.2 766 9.2% 225 420 1033 3653 1974 18.7% 2.5% 997,534 2.2% 481,835

Nauru 14.8 3.5 3 9.6% 1 2 3 12 7 16.7% 2.6% 3,378 3.8% 1,632

Poland 15.1 3.6 40,216 9.5% 11837 20896 47970 169697 88994 41.7% 5.0% 46,341,208 12.3% 22,384,033

France 15.7 3.7 69,870 6.8% 20565 35693 80104 283371 147010 20.6% 3.6% 77,383,281 15.1% 37,378,179

Cyprus 15.8 3.7 1,172 5.9% 345 598 1341 4745 2461 41.9% 5.7% 1,295,892 14.6% 625,950

Czech Republic 15.9 3.8 9,687 7.4% 2851 4922 10964 38784 20047 18.0% 4.0% 10,591,153 7.8% 5,115,808

San Marino 16.0 3.8 8 2.6% 2 4 9 31 16 - 0.5% 8,354 - 4,035

Denmark 16.2 3.8 5,247 7.1% 1544 2650 5854 20708 10661 24.2% 2.8% 5,655,093 9.6% 2,731,560

Portugal 16.5 3.9 11,075 13.4% 3260 5545 12102 42810 21903 32.8% 5.2% 11,690,517 15.9% 5,646,830

Austria 16.6 3.9 9,907 8.0% 2916 4945 10744 38006 19402 22.7% 3.3% 10,378,799 12.2% 5,013,235

Spain 17.5 4.1 56,894 8.3% 16745 27801 58541 207090 104003 29.3% 4.6% 56,552,400 16.1% 27,316,310

Slovenia 17.9 4.2 2,252 5.1% 663 1092 2271 8032 4007 22.0% 4.7% 2,193,489 11.2% 1,059,513

Saint Lucia 18.2 4.3 67 5.5% 20 32 66 235 116 31.2% 4.3% 64,054 5.8% 30,940

Jamaica 18.6 4.4 1,164 5.3% 343 556 1129 3996 1967 34.0% 6.2% 1,091,104 9.7% 527,032

95

Antigua and Barbuda 18.7 4.4 47 4.0% 14 23 46 162 79 62.2% 3.0% 44,159 4.2% 21,330

Saint Kitts and Nevis 18.7 4.4 28 7.3% 8 13 27 95 47 30.7% 2.7% 25,938 7.2% 12,529

Greece 19.4 4.6 13,488 11.5% 3970 6343 12566 44454 21575 30.8% 6.2% 12,139,601 11.1% 5,863,749

Brunei 20.1 4.7 523 17.3% 154 242 469 1658 793 19.1% 2.0% 452,823 2.2% 218,726

Slovakia 20.2 4.8 4,752 12.2% 1399 2198 4235 14983 7151 21.2% 3.8% 4,091,510 9.4% 1,976,308

Grenada 21.0 4.9 33 2.9% 10 15 28 99 47 26.7% 3.0% 27,108 1.4% 13,094

Hungary 21.1 5.0 8,491 4.3% 2499 3871 7261 25688 12056 34.2% 4.2% 7,014,842 10.4% 3,388,355

Seychelles 21.2 5.0 43 8.5% 13 20 37 129 61 - 2.2% 35,353 4.6% 17,077

Ireland 21.9 5.2 6,290 6.5% 1851 2830 5183 18333 8470 29.2% 3.2% 5,006,512 8.2% 2,418,278

Maldives 22.0 5.2 27 30.8% 8 12 22 79 36 18.8% 0.7% 21,437 1.8% 10,355

United Arab Emirates 22.0 5.2 7,588 186.0% 2233 3407 6216 21990 10133 8.0% 1.5% 6,004,974 0.1% 2,900,562

Malaysia 22.3 5.3 29,435 20.4% 8664 13157 23791 84161 38550 25.8% 4.6% 22,982,854 8.1% 11,101,328

Croatia 24.2 5.7 4,620 15.7% 1360 2009 3441 12172 5362 48.4% 6.3% 3,323,835 12.1% 1,605,500

U.S. 24.2 5.7 754,729 11.5% 222137 328264 561938 1987878 875540 18.7% 5.2% 542,852,359 142.5% 262,212,099

Dominican Republic 24.4 5.7 3,955 20.1% 1164 1716 2921 10333 4533 31.9% 3.3% 2,821,838 9.0% 1,363,022

El Salvador 24.6 5.8 1,686 15.5% 496 730 1238 4380 1916 29.5% 3.9% 1,196,132 9.4% 577,763

Thailand 24.8 5.8 41,270 22.9% 12147 17829 30085 106426 46366 26.1% 4.6% 29,063,002 8.4% 14,038,201

Serbia 24.9 5.9 6,212 14.0% 1828 2677 4495 15901 6900 17.9% 7.9% 4,342,254 7.2% 2,097,424

Romania 25.2 5.9 18,224 21.6% 5364 7826 13036 46117 19891 31.4% 5.5% 12,593,590 9.8% 6,083,038

Turkey 25.4 6.0 60,678 14.2% 17859 26012 43171 152718 65676 25.8% 4.7% 41,704,503 10.9% 20,144,382

Tonga 25.5 6.0 56 8.3% 17 24 40 141 61 105.5% 13.2% 38,514 10.1% 18,603

Bulgaria 25.6 6.0 8,477 21.1% 2495 3624 5978 21147 9050 19.4% 7.8% 5,774,908 9.4% 2,789,434

Lithuania 25.7 6.1 4,874 5.8% 1435 2081 3420 12100 5164 102.2% 7.3% 3,304,201 15.3% 1,596,017

Sri Lanka 26.2 6.2 4,927 4.3% 1450 2090 3390 11991 5061 40.4% 2.6% 3,274,400 7.9% 1,581,622

Kiribati 28.6 6.7 57 8.5% 17 23 36 127 51 568.2% 40.2% 34,580 43.2% 16,703

Moldova 29.6 7.0 2,225 23.8% 655 911 1358 4805 1880 35.6% 15.0% 1,312,157 10.4% 633,806

China 29.8 7.0 626,006 14.0% 184250 255863 379188 1341395 522093 9.3% 3.8% 366,309,862 3.8% 176,937,386

Mexico 30.4 7.2 127,789 33.7% 37612 51929 75808 268175 102826 36.9% 6.9% 73,233,659 10.7% 35,373,801

Ukraine 31.5 7.4 36,751 21.7% 10817 14790 21039 74427 27781 15.5% 13.0% 20,324,684 3.5% 9,817,362

Belarus 31.6 7.4 12,860 13.6% 3785 5174 7356 26021 9706 33.7% 9.0% 7,105,843 7.3% 3,432,311

Costa Rica 31.6 7.4 3,229 8.3% 950 1298 1842 6518 2427 26.6% 5.2% 1,779,876 11.7% 859,728

Bosnia and Herzegovina 32.6 7.7 3,145 13.7% 926 1255 1744 6168 2244 17.1% 9.4% 1,684,497 6.2% 813,657

Dominica 32.6 7.7 46 3.0% 14 18 26 90 33 45.5% 6.6% 24,640 10.7% 11,902

Estonia 33.8 8.0 2,505 4.3% 737 989 1335 4724 1662 16.4% 7.7% 1,290,061 6.0% 623,134

Jordan 34.2 8.0 5,192 72.1% 1528 2046 2740 9692 3378 28.4% 7.3% 2,646,619 10.3% 1,278,387

Montenegro 34.2 8.1 786 10.1% 231 310 415 1467 511 23.2% 9.4% 400,527 11.4% 193,465

Azerbaijan 35.5 8.3 4,657 8.8% 1371 1818 2369 8379 2822 14.6% 3.1% 2,288,210 3.9% 1,105,266

Albania 35.8 8.4 1,531 8.5% 450 596 772 2730 911 28.4% 5.5% 745,422 10.5% 360,059

Sweden 36.7 8.6 22,343 3.9% 6576 8650 10979 38838 12634 9.8% 5.5% 10,605,966 17.1% 5,122,963

New Zealand 37.4 8.8 14,335 15.2% 4219 5527 6927 24504 7830 22.9% 10.0% 6,691,456 9.4% 3,232,151

Comoros 37.6 8.8 119 13.5% 35 46 57 202 64 407.7% 11.3% 55,142 19.7% 26,635

Armenia 39.4 9.3 1,431 18.4% 421 545 655 2317 696 13.2% 6.5% 632,741 8.9% 305,631

Guatemala 39.9 9.4 5,371 46.7% 1581 2040 2430 8595 2545 41.5% 4.9% 2,347,202 6.8% 1,133,761

Iran 40.1 9.4 76,306 35.7% 22459 28938 34308 121364 35660 18.6% 6.3% 33,142,338 4.7% 16,008,629

Philippines 40.5 9.5 14,738 6.8% 4338 5579 6574 23255 6764 12.4% 2.3% 6,350,591 4.3% 3,067,504

Norway 40.6 9.5 15,005 16.0% 4416 5677 6677 23621 6850 7.1% 4.9% 6,450,426 12.6% 3,115,727

Latvia 41.0 9.6 3,037 4.3% 894 1146 1337 4730 1353 32.4% 6.9% 1,291,610 10.0% 623,882

96

Finland 41.0 9.6 14,819 3.3% 4362 5593 6523 23075 6597 13.1% 7.5% 6,301,250 8.1% 3,043,671

Indonesia 41.2 9.7 87,950 17.7% 25886 33163 38533 136310 38729 25.5% 3.5% 37,223,826 4.6% 17,980,096

Tunisia 42.9 10.1 7,243 37.3% 2132 2707 3047 10780 2893 28.6% 6.5% 2,943,736 8.4% 1,421,903

Georgia 43.6 10.3 3,195 15.6% 940 1190 1322 4677 1224 18.9% 10.2% 1,277,085 9.8% 616,866

Fiji 43.8 10.3 835 24.3% 246 311 344 1218 317 58.8% 12.1% 332,609 15.2% 160,659

India 45.8 10.8 129,856 2.7% 38220 47886 51182 181057 43769 6.1% 1.8% 49,443,304 2.0% 23,882,428

South Africa 47.5 11.2 51,150 6.8% 15055 18726 19441 68773 15551 10.8% 7.9% 18,780,582 4.1% 9,071,520

Syria 47.8 11.2 7,687 12.8% 2263 2811 2904 10274 2296 10.7% 15.4% 2,805,524 2.9% 1,355,142

Panama 48.8 11.5 3,049 20.1% 898 1111 1129 3993 856 21.1% 4.2% 1,090,486 7.2% 526,734

Viet Nam 49.0 11.5 12,647 6.1% 3722 4602 4655 16468 3489 4.2% 2.6% 4,497,096 1.7% 2,172,217

Honduras 49.2 11.6 4,547 30.8% 1338 1653 1667 5898 1240 35.5% 12.4% 1,610,683 8.2% 778,003

Colombia 49.3 11.6 42,145 29.8% 12404 15319 15432 54591 11436 35.4% 7.1% 14,907,773 8.7% 7,200,850

Swaziland 50.0 11.8 689 19.2% 203 250 249 880 179 77.2% 7.1% 240,280 8.6% 116,062

Nigeria 51.4 12.1 35,408 18.3% 10422 12768 12423 43945 8333 69.1% 3.6% 12,000,613 3.7% 5,796,614

Brazil 51.9 12.2 321,831 18.4% 94724 115844 111830 395604 73207 27.4% 11.3% 108,032,183 9.8% 52,182,412

Chile 54.8 12.9 27,141 34.9% 7988 9676 8937 31616 5014 17.6% 7.1% 8,633,740 9.2% 4,170,326

Micronesia 55.2 13.0 25 10.6% 7 9 8 29 5 6.2% 9.1% 8,040 - 3,884

Morocco 55.5 13.1 16,072 27.5% 4730 5717 5223 18477 2806 31.5% 6.5% 5,045,674 5.5% 2,437,195

Samoa 56.3 13.3 100 4.3% 29 36 32 114 16 39.9% 10.7% 31,009 7.8% 14,978

Venezuela 56.6 13.3 31,153 32.4% 9169 11044 9929 35126 4983 10.8% 7.0% 9,592,168 3.4% 4,633,272

Argentina 57.9 13.6 94,593 40.9% 27841 33414 29506 104378 13617 30.4% 10.8% 28,503,703 7.8% 13,768,045

Saudi Arabia 57.9 13.6 74,270 33.5% 21860 26233 23156 81915 10667 11.3% 4.9% 22,369,535 4.4% 10,805,079

Haiti 59.7 14.1 923 21.6% 272 324 279 987 112 61.1% 5.3% 269,505 3.5% 130,178

Zimbabwe 59.8 14.1 12,931 13.3% 3806 4543 3901 13799 1550 76.4% 50.8% 3,768,357 18.6% 1,820,216

Pakistan 61.2 14.4 25,207 9.6% 7419 8824 7438 26313 2632 11.4% 3.0% 7,185,686 2.4% 3,470,877

Iraq 62.6 14.7 13,964 23.8% 4110 4870 4023 14233 1230 11.2% 2.9% 3,886,710 1.7% 1,877,384

Egypt 63.2 14.9 31,518 22.9% 9277 10977 9006 31858 2598 8.7% 3.5% 8,699,855 3.1% 4,202,261

Ecuador 63.3 14.9 8,746 20.7% 2574 3045 2493 8820 708 15.3% 5.3% 2,408,495 4.6% 1,163,367

Cuba 65.4 15.4 3,360 6.4% 989 1164 928 3282 201 7.9% 2.8% 896,286 1.6% 432,930

Uruguay 65.4 15.4 5,351 16.7% 1575 1854 1476 5222 317 20.8% 8.0% 1,426,038 4.3% 688,814

Bangladesh 67.0 15.8 3,887 11.3% 1144 1342 1047 3705 172 3.0% 0.7% 1,011,813 0.8% 488,733

Ghana 67.5 15.9 6,741 6.2% 1984 2324 1802 6376 265 23.6% 6.5% 1,741,108 6.2% 841,001

97

9.3 Appendix C – Comparison between a Pure Battery Electric Car fleet vs ERS and Electric Car

Combination

Table 17. A list with countries where the ERS plus battery combination is cheaper than a pure battery based passenger car fleet.

Country Optimal grid-mesh size

Battery Size Total Length of grid

Total Investment Grid+Battery+ Pickup arm

Total ERS cost per year

Fullsize EVs + Fastchargers

Fullsize EVs + Fastchargers per year

ERS cost compared to full EV cost in %

Total number of cars in these countries

% of Total Cars in the World

km kWh km MSEK MSEK/year MSEK MSEK/Year % %

World 17.8 2.8 13,968,123 28,498,080 2,096,939 506,110,987 37,240,532 5.6% 1,117,917,387 99.99%

Monaco 0.4 0.1 11 173 13 12,723 936 1.4% 28,103

Singapore 1.3 0.2 1,078 5,840 430 338,730 24,924 1.7% 748,199

Bahrain 1.4 0.2 1,066 5,342 393 299,348 22,027 1.8% 661,211

Malta 1.5 0.2 433 2,135 157 118,729 8,736 1.8% 262,252

Hong Kong 2.0 0.3 1,100 4,502 331 225,743 16,611 2.0% 498,630

Barbados 2.5 0.4 338 1,190 88 53,344 3,925 2.2% 117,828

Netherlands 2.8 0.4 24,608 82,843 6,096 3,577,858 263,265 2.3% 7,902,909

Lebanon 2.8 0.4 7,279 24,247 1,784 1,037,149 76,315 2.3% 2,290,894

South Korea 2.9 0.5 66,254 216,202 15,908 9,069,766 667,369 2.4% 20,033,648

Taiwan 2.9 0.5 22,007 71,739 5,279 3,006,412 221,217 2.4% 6,640,677

Guam 3.0 0.5 362 1,166 86 48,281 3,553 2.4% 106,646

Belgium 3.1 0.5 19,585 62,159 4,574 2,536,905 186,670 2.5% 5,603,614

Japan 3.1 0.5 232,279 731,798 53,847 29,642,893 2,181,176 2.5% 65,476,361

Liechtenstein 3.2 0.5 100 311 23 12,452 916 2.5% 27,505

Luxembourg 3.5 0.6 1,483 4,444 327 170,405 12,539 2.6% 376,397

Israel 3.7 0.6 10,901 31,662 2,330 1,170,458 86,124 2.7% 2,585,353

U.K. 3.8 0.6 127,642 368,078 27,084 13,485,935 992,319 2.7% 29,788,250

Italy 3.8 0.6 154,619 445,140 32,754 16,276,133 1,197,626 2.7% 35,951,347

Germany 3.8 0.6 181,562 520,527 38,301 18,932,774 1,393,107 2.7% 41,819,438

Switzerland 4.1 0.7 19,563 54,582 4,016 1,916,152 140,994 2.8% 4,232,470

Kuwait 4.1 0.7 8,659 24,094 1,773 842,760 62,012 2.9% 1,861,522

Mauritius 4.3 0.7 951 2,606 192 89,216 6,565 2.9% 197,064

Qatar 4.4 0.7 5,305 14,399 1,060 486,480 35,796 3.0% 1,074,555

Trinidad and Tobago 4.6 0.7 2,219 5,887 433 192,290 14,149 3.1% 424,738

Nauru 5.1 0.8 8 21 2 651 48 3.3% 1,438

Poland 5.2 0.8 116,500 294,933 21,702 8,932,987 657,305 3.3% 19,731,525

France 5.4 0.9 202,403 505,080 37,165 14,916,829 1,097,606 3.4% 32,948,864

Cyprus 5.4 0.9 3,395 8,467 623 249,803 18,381 3.4% 551,775

Czech Republic 5.5 0.9 28,063 69,706 5,129 2,041,609 150,225 3.4% 4,509,585

San Marino 5.5 0.9 22 55 4 1,610 118 3.4% 3,557

Denmark 5.6 0.9 15,199 37,562 2,764 1,090,107 80,212 3.4% 2,407,870

98

Portugal 5.7 0.9 32,083 78,713 5,792 2,253,529 165,819 3.5% 4,977,680

Austria 5.7 0.9 28,700 70,227 5,167 2,000,675 147,213 3.5% 4,419,167

Spain 6.1 1.0 164,813 396,149 29,149 10,901,353 802,141 3.6% 24,079,327

Slovenia 6.2 1.0 6,523 15,574 1,146 422,829 31,113 3.7% 933,961

Saint Lucia 6.3 1.0 193 459 34 12,347 909 3.7% 27,273

Jamaica 6.4 1.0 3,373 7,952 585 210,327 15,476 3.8% 464,579

Antigua and Barbuda 6.5 1.0 137 323 24 8,512 626 3.8% 18,802

Saint Kitts and Nevis 6.5 1.0 81 190 14 5,000 368 3.8% 11,044

Greece 6.7 1.1 39,073 90,948 6,692 2,340,097 172,188 3.9% 5,168,895

Brunei 7.0 1.1 1,515 3,484 256 87,289 6,423 4.0% 192,807

Slovakia 7.0 1.1 13,765 31,606 2,326 788,702 58,034 4.0% 1,742,116

Grenada 7.3 1.2 95 215 16 5,225 385 4.1% 11,542

Hungary 7.3 1.2 24,598 55,786 4,105 1,352,220 99,499 4.1% 2,986,835

Seychelles 7.3 1.2 124 282 21 6,815 501 4.1% 15,053

Ireland 7.6 1.2 18,220 40,877 3,008 965,083 71,012 4.2% 2,131,712

Maldives 7.6 1.2 78 176 13 4,132 304 4.3% 9,128

United Arab Emirates 7.6 1.2 21,983 49,236 3,623 1,157,552 85,175 4.3% 2,556,845

Malaysia 7.7 1.2 85,270 190,259 14,000 4,430,302 325,989 4.3% 9,785,821

Croatia 8.4 1.3 13,383 29,197 2,148 640,721 47,145 4.6% 1,415,249

U.S. 8.4 1.3 2,186,346 4,769,534 350,951 104,643,221 7,699,831 4.6% 231,139,965

Dominican Republic 8.4 1.3 11,457 24,939 1,835 543,953 40,025 4.6% 1,201,504

El Salvador 8.5 1.4 4,884 10,617 781 230,573 16,966 4.6% 509,298

Thailand 8.5 1.4 119,552 259,351 19,083 5,602,345 412,230 4.6% 12,374,675

Serbia 8.6 1.4 17,995 38,962 2,867 837,037 61,591 4.7% 1,848,879

Romania 8.7 1.4 52,791 113,960 8,385 2,427,610 178,628 4.7% 5,362,198

Turkey 8.8 1.4 175,775 378,914 27,881 8,039,190 591,538 4.7% 17,757,272

Tonga 8.8 1.4 163 351 26 7,424 546 4.7% 16,399

Bulgaria 8.8 1.4 24,558 52,818 3,886 1,113,203 81,911 4.7% 2,458,886

Lithuania 8.9 1.4 14,120 30,330 2,232 636,936 46,867 4.8% 1,406,889

Sri Lanka 9.1 1.4 14,273 30,505 2,245 631,191 46,444 4.8% 1,394,200

Kiribati 9.9 1.6 164 344 25 6,666 490 5.2% 14,724

Moldova 10.2 1.6 6,445 13,386 985 252,939 18,612 5.3% 558,700

China 10.3 1.6 1,813,452 3,759,435 276,626 70,611,913 5,195,748 5.3% 155,970,306

Mexico 10.5 1.7 370,188 763,816 56,203 14,116,925 1,038,748 5.4% 31,182,006

Ukraine 10.9 1.7 106,463 217,942 16,037 3,917,898 288,286 5.6% 8,654,005

Belarus 10.9 1.7 37,254 76,249 5,611 1,369,761 100,789 5.6% 3,025,582

Costa Rica 10.9 1.7 9,353 19,134 1,408 343,099 25,246 5.6% 757,850

Bosnia and Herzegovina 11.2 1.8 9,110 18,522 1,363 324,713 23,893 5.7% 717,238

Dominica 11.3 1.8 133 271 20 4,750 349 5.7% 10,491

Estonia 11.7 1.9 7,255 14,630 1,076 248,679 18,298 5.9% 549,292

Jordan 11.8 1.9 15,041 30,264 2,227 510,177 37,540 5.9% 1,126,898

Montenegro 11.8 1.9 2,277 4,582 337 77,208 5,681 5.9% 170,540

Azerbaijan 12.2 2.0 13,491 26,944 1,983 441,088 32,456 6.1% 974,292

Albania 12.4 2.0 4,434 8,840 650 143,692 10,573 6.2% 317,392

Sweden 12.7 2.0 64,725 128,399 9,448 2,044,465 150,435 6.3% 4,515,892

New Zealand 12.9 2.1 41,526 82,113 6,042 1,289,882 94,912 6.4% 2,849,141

Comoros 13.0 2.1 344 680 50 10,629 782 6.4% 23,479

99

Armenia 13.6 2.2 4,145 8,113 597 121,971 8,975 6.7% 269,414

Guatemala 13.8 2.2 15,560 30,393 2,236 452,460 33,293 6.7% 999,410

Iran 13.9 2.2 221,049 431,299 31,736 6,388,700 470,092 6.8% 14,111,607

Philippines 14.0 2.2 42,694 83,183 6,121 1,224,175 90,077 6.8% 2,704,005

Norway 14.0 2.2 43,467 84,654 6,229 1,243,420 91,493 6.8% 2,746,514

Latvia 14.2 2.3 8,797 17,101 1,258 248,978 18,320 6.9% 549,952

Finland 14.2 2.3 42,928 83,443 6,140 1,214,664 89,377 6.9% 2,682,996

Indonesia 14.2 2.3 254,778 494,825 36,410 7,175,470 527,984 6.9% 15,849,455

Tunisia 14.8 2.4 20,982 40,465 2,978 567,451 41,754 7.1% 1,253,407

Georgia 15.1 2.4 9,257 17,802 1,310 246,178 18,114 7.2% 543,767

Fiji 15.1 2.4 2,419 4,649 342 64,116 4,718 7.3% 141,621

India 15.8 2.5 376,175 717,678 52,808 9,530,965 701,305 7.5% 21,052,361

South Africa 16.4 2.6 148,174 281,063 20,681 3,620,249 266,384 7.8% 7,996,545

Syria 16.5 2.6 22,269 42,201 3,105 540,808 39,794 7.8% 1,194,558

Panama 16.8 2.7 8,834 16,688 1,228 210,208 15,467 7.9% 464,316

Viet Nam 16.9 2.7 36,637 69,152 5,088 866,885 63,787 8.0% 1,914,809

Honduras 17.0 2.7 13,171 24,846 1,828 310,484 22,846 8.0% 685,809

Colombia 17.0 2.7 122,089 230,257 16,943 2,873,705 211,452 8.0% 6,347,549

Swaziland 17.2 2.8 1,995 3,754 276 46,318 3,408 8.1% 102,308

Nigeria 17.8 2.8 102,572 192,222 14,144 2,313,304 170,217 8.3% 5,109,716

Brazil 17.9 2.9 932,301 1,744,696 128,378 20,824,881 1,532,331 8.4% 45,998,796

Chile 18.9 3.0 78,624 146,019 10,744 1,664,287 122,461 8.8% 3,676,142

Micronesia 19.0 3.0 74 137 10 1,550 114 8.8% 3,424

Morocco 19.2 3.1 46,558 86,311 6,351 972,632 71,568 8.9% 2,148,387

Samoa 19.4 3.1 290 537 40 5,978 440 9.0% 13,203

Venezuela 19.5 3.1 90,246 166,859 12,278 1,849,039 136,056 9.0% 4,084,229

Argentina 20.0 3.2 274,024 505,195 37,173 5,494,531 404,297 9.2% 12,136,532

Saudi Arabia 20.0 3.2 215,150 396,630 29,185 4,312,075 317,290 9.2% 9,524,677

Haiti 20.6 3.3 2,674 4,909 361 51,951 3,823 9.5% 114,752

Zimbabwe 20.7 3.3 37,460 68,762 5,060 726,409 53,450 9.5% 1,604,521

Pakistan 21.1 3.4 73,021 133,664 9,835 1,385,153 101,922 9.6% 3,059,578

Iraq 21.6 3.5 40,452 73,826 5,432 749,224 55,129 9.9% 1,654,914

Egypt 21.8 3.5 91,304 166,462 12,249 1,677,032 123,399 9.9% 3,704,293

Ecuador 21.9 3.5 25,335 46,176 3,398 464,275 34,162 9.9% 1,025,508

Cuba 22.6 3.6 9,734 17,673 1,300 172,773 12,713 10.2% 381,628

Uruguay 22.6 3.6 15,500 28,140 2,071 274,891 20,227 10.2% 607,190

Bangladesh 23.1 3.7 11,260 20,385 1,500 195,043 14,352 10.5% 430,818

Ghana 23.3 3.7 19,528 35,323 2,599 335,626 24,696 10.5% 741,343

Oman 24.9 4.0 24,844 44,603 3,282 399,362 29,386 11.2% 882,127

Bahamas 25.1 4.0 797 1,429 105 12,695 934 11.3% 28,041

Nicaragua 26.2 4.2 9,156 16,349 1,203 139,898 10,294 11.7% 309,012

Belize 26.8 4.3 1,703 3,035 223 25,480 1,875 11.9% 56,282

Russia 26.9 4.3 1,218,234 2,169,645 159,646 18,146,271 1,335,234 12.0% 40,082,181

Burundi 27.3 4.4 1,883 3,350 246 27,662 2,035 12.1% 61,101

Canada 27.6 4.4 659,790 1,172,136 86,248 9,586,498 705,391 12.2% 21,175,025

Uzbekistan 27.6 4.4 30,829 54,763 4,030 447,414 32,921 12.2% 988,264

Iceland 28.5 4.6 7,037 12,460 917 98,907 7,278 12.6% 218,469

100

Tajikistan 29.4 4.7 9,639 17,019 1,252 131,472 9,674 12.9% 290,401

Australia 29.5 4.7 521,591 920,707 67,747 7,091,674 521,818 13.0% 15,664,361

North Korea 29.5 4.7 8,169 14,419 1,061 110,982 8,166 13.0% 245,140

Benin 30.1 4.8 7,359 12,966 954 98,040 7,214 13.2% 216,555

Guinea-Bissau 30.1 4.8 1,869 3,292 242 24,870 1,830 13.2% 54,933

Kenya 31.7 5.1 35,864 62,881 4,627 452,569 33,301 13.9% 999,652

Algeria 32.2 5.2 147,809 258,812 19,044 1,836,902 135,162 14.1% 4,057,420

Cambodia 32.6 5.2 10,813 18,912 1,392 132,643 9,760 14.3% 292,986

Kyrgyzstan 32.9 5.3 11,665 20,390 1,500 142,069 10,454 14.4% 313,807

Yemen 33.2 5.3 31,810 55,559 4,088 383,800 28,241 14.5% 847,753

Peru 33.3 5.3 76,921 134,319 9,883 925,670 68,112 14.5% 2,044,655

Senegal 33.6 5.4 11,471 20,017 1,473 136,871 10,071 14.6% 302,327

Uganda 35.0 5.6 11,256 19,574 1,440 128,736 9,473 15.2% 284,356

Malawi 36.5 5.8 5,160 8,944 658 56,669 4,170 15.8% 125,172

Côte d'Ivoire 37.0 5.9 17,168 29,724 2,187 185,605 13,657 16.0% 409,971

Kazakhstan 37.2 6.0 145,045 251,032 18,471 1,560,528 114,826 16.1% 3,446,955

Afghanistan 37.4 6.0 34,849 60,290 4,436 372,882 27,437 16.2% 823,637

Gambia 37.6 6.0 539 932 69 5,741 422 16.2% 12,680

Turkmenistan 40.4 6.5 23,236 39,972 2,941 230,068 16,929 17.4% 508,184

Bhutan 41.5 6.6 1,849 3,175 234 17,836 1,312 17.8% 39,398

Madagascar 42.4 6.8 27,400 46,981 3,457 258,528 19,023 18.2% 571,046

Djibouti 43.0 6.9 1,079 1,848 136 10,052 740 18.4% 22,204

Libya 43.9 7.0 80,232 137,271 10,101 732,607 53,906 18.7% 1,618,210

Nepal 44.7 7.1 6,420 10,971 807 57,569 4,236 19.1% 127,160

Paraguay 46.9 7.5 16,957 28,893 2,126 144,935 10,665 19.9% 320,137

Burkina Faso 49.6 7.9 11,046 18,757 1,380 89,236 6,566 21.0% 197,108

Angola 50.5 8.1 49,384 83,773 6,164 391,726 28,824 21.4% 865,260

Cameroon 53.2 8.5 17,763 30,045 2,211 133,668 9,836 22.5% 295,251

Bolivia 54.2 8.7 39,981 67,559 4,971 295,482 21,742 22.9% 652,673

Laos 57.9 9.3 7,978 13,435 989 55,219 4,063 24.3% 121,971

Sudan 58.0 9.3 64,220 108,142 7,957 443,671 32,646 24.4% 979,998

Eritrea 58.7 9.4 3,442 5,793 426 23,493 1,729 24.7% 51,893

Myanmar 58.7 9.4 22,262 37,465 2,757 151,867 11,175 24.7% 335,450

Sierra Leone 60.3 9.6 2,376 3,993 294 15,785 1,161 25.3% 34,866

Botswana 60.9 9.7 18,603 31,250 2,299 122,281 8,998 25.6% 270,099

Zambia 65.2 10.4 22,806 38,191 2,810 140,102 10,309 27.3% 309,463

Mozambique 67.6 10.8 23,253 38,877 2,861 137,689 10,131 28.2% 304,132

Tanzania 67.9 10.9 26,107 43,643 3,211 154,080 11,337 28.3% 340,337

Equatorial Guinea 70.6 11.3 795 1,327 98 4,511 332 29.4% 9,965

Namibia 78.4 12.5 21,008 34,917 2,569 107,351 7,899 32.5% 237,120

Ethiopia 81.0 13.0 24,677 40,964 3,014 121,942 8,973 33.6% 269,351

Lesotho 84.0 13.4 723 1,199 88 3,449 254 34.8% 7,617

Togo 85.3 13.7 1,275 2,112 155 5,984 440 35.3% 13,217

Guinea 87.3 14.0 5,630 9,322 686 25,835 1,901 36.1% 57,064

Papua New Guinea 94.8 15.2 9,552 15,773 1,161 40,343 2,968 39.1% 89,110

Mali 98.2 15.7 24,847 40,986 3,016 101,321 7,455 40.5% 223,802

Liberia 118.2 18.9 1,630 2,676 197 5,526 407 48.4% 12,205

101

Mongolia 120.0 19.2 25,888 42,470 3,125 86,386 6,356 49.2% 190,813

Vanuatu 129.4 20.7 188 308 23 583 43 52.9% 1,287

Niger 132.4 21.2 19,132 31,315 2,304 57,869 4,258 54.1% 127,824

Suriname 140.2 22.4 2,226 3,639 268 6,359 468 57.2% 14,047

Gabon 144.7 23.2 3,560 5,816 428 9,851 725 59.0% 21,759

Chad 170.5 27.3 14,774 24,060 1,770 34,712 2,554 69.3% 76,673

Solomon Islands 177.4 28.4 316 513 38 712 52 72.1% 1,573

Somalia 194.6 31.1 6,446 10,475 771 13,264 976 79.0% 29,298

Guyana 232.3 37.2 1,695 2,747 202 2,922 215 94.0% 6,453