using mobility information to perform feasibility studies ... 2015... · using mobility information...
TRANSCRIPT
Using mobility information to perform
feasibility studies for the introduction of electric vehicles in taxi fleets
Jesús Fraile Ardanuy
ETSI de Telecomunicación
Universidad Politécnica de Madrid
Hasselt, July 13th 2015
Who am I?
Jesús Fraile-Ardanuy
Associate Professor
Technical University of Madrid
Outline
• Introduction
• Fundamentals of Electric Vehicles (EVs)
• Big Data and EVs
• Data Mobility description
• Results
• Other lines of work
• Conclusions
INTRODUCTION
World Population
World population forecast
• Rates of population growth are currently highest in the less developed regions.
• If current trends continue:
– Africa’s share will rise to 20%
– Asia’s population will decrease slightly to 57% of the world total in 2050.
– Europe’s share will drop below Latin America’s.
http://www.theguardian.com/world/2011/jan/14/population-explosion-seven-billion
Urban and rural population
• Globally, more people live
in urban areas than in
rural areas.
– In 2007, the global urban
population exceed the
global rural one.
• Level of urbanization varies greatly across regions.
http://esa.un.org/unpd/wup/Highlights/WUP2014-Highlights.pdf
Urban-rural population
http://image.guardian.co.uk/sys-files/Guardian/documents/2007/06/27/URBAN_WORLD_2806.pdf
• Africa and Asia are urbanizing more rapidly than other
regions in the world.
Urban and rural population
http://www.populationlabs.com/world_population.asp
Urban population problems and challenges
• There are many problems associated with the rapid urban population
growth:
– Unplanned housing
http://www.geo.tv/article-112485-Traffic-jam-in-Karachi-residents-forced-to-open-fast-on-roads-
http://urbanpoverty.intellecap.com/?p=552
http://www.china-mike.com/facts-about-china/facts-pollution-environment-energy/
– Water and energy
– Urban waste
– Stress on the infraestructure
– Basic services: education and health care.
– Pollution
Air pollution in Urban areas
http://aqicn.org/map/world/
Problems
• The biggest threat to clean air these
days is traffic emissions.
• Cars are responsible for 73% of
urban air pollutants.
• Petrol and diesel-engined vehicles
emit a variety of pollutants,
principally carbon monoxide (CO),
oxides of nitrogen (NOx), volatile
organic compounds (VOCs) and
particulate matter (PM10).
• The pollutants have linked to
chronic health problems like asthma,
lung cancer, emphysema, and heart
disease.
http://uk-air.defra.gov.uk/air-pollution/effects
http://newhamgreenparty.com/2015/03/15/tackling-air-pollution/
http://newhamgreenparty.com/2015/03/15/tackling-air-pollution/
The reduction of pollutant emissions and improving air quality
in urban areas are fundamental aspects to be solved in the
following years
Solution?
Strategies to EV deployment in urban areas
• Strategies:
– Promoting public
transportation, bicycling
and walking in the cities,
reducing the number of
vehicles in the streets.http://studyinuk.universiablogs.net/2013/11/05/take-a-walk-to-the-campus/image0017-584x234/
http://bellovelo.blogspot.com.es/2010/02/great-bike-friendly-cities.html
– Promoting the transition from ICE EVs.
http://www.wired.com/tag/tesla-model-s/
EV promotion
• Governments have been promoting EVs
through different initiatives:
https://en.wikipedia.org/wiki/Government_incentives_for_plug-in_electric_vehicles
http://www.greenwisebusiness.co.uk/news/transport-for-
london-issues-67m-tender-for-green-vehicles-
1320.aspx#.VZPvHfntlBc
http://www.plugincars.com/public-charging-why-its-time-think-plugging-127217.html
– Subsides to purchase EVs.
– Creation of ultra-low emissions zones (ULEZ) in
city centers.– High occupancy vehicle (HOV) lane access
– Tax exemptions and other fiscal incentives
– Priority parking
– Insurance discounts
– Deployment of charging points
ELECTRIC VEHICLES
But…What is an electric vehicle?
• An EV is a vehicle that uses one (or more) electric motors
for propulsion, instead of an ICE.
http://treneando.com/2012/01/23/parla-apuesta-por-
el-tranvia-que-en-2011-supero-los-cinco-millones-de-
usuarios/
https://movimientoindignadosspanishrevolution.wor
dpress.com/el-ave-no-es-rentable-en-espana/
http://www.motoryracing.com/camiones/noticias/scania-siemens-
trabajan-camion-electrico-perfecto/
• An EV can be powered:
– Through a collector system by electricity
from off-vehicle
– Self-contained using a battery or generator
to convert fuel to electricity.
Electric vehicle classification
Motor/Generator
Battery Fuel
Transmission
Engine
Fuel
Transmission
Engine
Battery
Transmission
Motor/Generator
Battery ElectricHybridConventional
Electric Vehicles 101. Dan Lauber MIT
Hybrid EV
• Hybrid EV
– ICE+electric motor-generator
• Fueled by gasoline, diesel,
compressed natural gas or bio-fuels
– Small battery pack
– Recharged from regenerative braking
– Limited all-electric range (2-3 km)
– No support external charging (no plug-in)
– ICE engine more powerful than EV motor
– Types:
• Micro hybrid (stop & start)
• Mild hybrid (assisst to ICE)
• Full hybrid (Electric motor can drive the
car)
http://www.taringa.net/posts/autos-motos/18323659/Toyota-Prius.html
http://www.lexusofglendale.com/los-angeles-hybrid-lexus
Plug in Hybrid Electric Vehicle
• Plug in Hybrid EV
– ICE+electric motor-generator
– Larger battery pack
– Recharged from regenerative
braking and external charging
– Limited all-electric range (25-50 km)
– ICE engine>EV motorhttp://www.autoblog.com/2014/04/28/2015-bmw-i8-review-first-drive-video/
http://cocheselectricos365.com/mitsubishi-outlander-phev-plug-electric-vehicle-13247.htmlhttp://www.hibridosyelectricos.com/articulo/mercado/nuevo-bmw-x5-xdrive-40e-hibrido-
enchufable/20150316133159009036.html
Plug in Hybrid Electric Vehicle
• Extended-Plug in Hybrid EV
– A PHEV with bigger battery
– Driving ranges (60-100 km)
– All electric mode in day-by-day activities
– ICE engine<EV motor
– ICE engine is added to an EV motor to charge battery (no for
propulsion)
http://www.opel.es/vehiculos/coches-opel/vehiculos-de-pasajeros-opel/ampera/models/available-models.html
Degrees of hybridization
Source: http://www.hybridcenter.org/hybrid-center-how-hybrid-cars-work-under-the-hood.html
Efficiency
Micro Hybrid
Citroën C2
Mild Hybrid
Honda Insight
Full Hybrid
Toyota Prius
Ext-PHEV
Chevy Volt
Plug-in Hybrid
BMW i8
Pure Electric Vehicles
• BEV: Battery Electric Vehicle
– No ICE
– Different ranges depending on nominal battery capacity:
• iMiev (150 km)
• Leaf (170 km)
• Model S (350 km)
• E6 (400 km)
– No plan B if you are out of battery!
Benefits of EVs
• More efficient.
• Lower energy cost compared
to oil.
• Lower emissions (depending
on the country)
– But it is easier to control
emissions at few large locations
(power plants) than millions of
tailpipes
• Simpler transmissions. Fewer
moving parts.
• Noise reduction.
Current Challenges
• Limited range
– Large battery (weight/size)
• Long charge times
• High initial cost
• Battery life
• Consumer acceptance
• Grid Integration
Consumer acceptance
http://www.continental-corporation.com/www/download/pressportal_com_en/themes/initiatives/channel_mobility_study_en/ov_mobility_study2015_en/download_channel/mobistud2015_praesentation_en.pdf
Are EV boring?
Go to min 4
Understanding power systems
Thermal power
plant
Hydro
power plant
Wind
Energy
Transmission
Substation
System Operator
(SO) control center
Transmission
NETWORK
Distribution
substation
Residential
Customers (Low
Voltage)
Industrial
Customers
(Medium or
High
Voltage)
Generation
Energy flows in one direction, from
generation to consumer at the
lowest cost and at the highest
reliabilitySource: REE.es
Balance between generation and demand
30/08/2015
BIG DATA AND EVS
Big Data and EVs
• Big data applied to EVs can turn the information from
the vehicles into meaningful operational insights and
insights about the customer’s behavior.
Improving human experience
Personal level observation
Charging the EV has a significant cost since it was done
during peak load period. Consider changing this time to
night period.
EV monitoring application Action in the physical world
Population level observation
Source of Big Data in Power Systems
From BD in the management of EES. Louis Wehenkel
• Observational datasets– Meteo
• Wind, rain, clouding, temperature, etc.
• Measurable at any place and at any time
• Influences demands, offers, harzards, equipment ageing
• Simulated datasets
– Generated and used to replace or
forecast unavailable observational
quantities
– Economics
• Prices, bids, costs of consumers and producers
• Measurable for any actor and at any time
• Influence system technical and economic performance– Technical performance
• Failures, power flows, service disruptions, quality
Big data in EVs
Sources of Big Data in EVs
• Cars are generating lots of data every second:
– Acceleration/Braking/g forces
– Idle / Number of stops
– Electric Consumption/Battery state
– Ambient temperature
– HVAC temperature
– Tire preassures
– GPS traces
– Charging time periods
– Charging rated power
How to use these data?
Electric Vehicle
DATA
Drivers
Fleetmanagers
Retailers
DSOs
TSO
Generators
What Big Data can do for drivers?
• Improving driving efficiency
• Allowing to detect anomalies and problems in their own
vehicles
• Optimizing charging electricity costs
Improving driver behavior. Big Data for drivers
• Providing feedback to drivers on how they are currently
doing.
• Comparing to
personal historical
data
• Comparing to other
similar drivers
– Same mobility
patterns (same route
or area)
– Same type of EVs
Improving driver behavior. Big Data for drivers
• Providing feedback to drivers on how to do it better.
– Avoiding aggresive aceleration/braking events
– Time spent idling
Detecting problems in the Vehicles. Big Data for drivers
• Sharing and comparing different measurements will
allow to identify anomalous vehicle behavior.
– Anormal range reduction or higher average battery
temperature can lead to battery problems (accelerated ageing
of the battery)
– Diagnostics trouble codes
Optimizing charging electricity costs. Big Data for driver
• Optimizing charging costs, taking into account:
– Variable electricity price
– The personal daily schedule
• Determining posible charging locations
• Determining posible charging periods
What can Big Data do for fleets?
• Comprenhensive analysis of:
– Fuel/electricity economy reporting
• Measuring real-world consumption
from all fleet vehicles.
– Idle monitoring and management
• Reporting idle periods and allowing to
quantify savings and identify drivers
that may require additional route
adjustments
What can Big Data do for fleets?
• Driver behavior feedback
• Diagnostic trouble codes
• Distribution of charging times
• Vehicle location tracking
What can Big Data do for Elec. Retailers?
• EV Load forecasting
– Improving their offer bids and increasing their benefits
• Segmentation-driven marketing offers
• Special tariff designs
What can Big Data do for DSO?
• More effective monitoring and proactive maintenance
– Obtaining operation conditions for charging EVs on local
household distribution grid.
• Power losses
• Power quality (voltage and current profiles, unbalance and
harmonics)
– Modelling large scale (spatial-temporal) deployment of EVs and
quantifying the impacts on distribution operation conditions and
infrastructures.
What Bid Data can do for DSO?
• Investigating optimal EV
charging profiles that result
in maximal economic,
environmental benefits and
minimal operation
disturbance.
• Reducing or postponing
the need for network
reinforcement through
charging active demand
management.
What can Big Data do for TSO?
• Operation (short term)
– Predict network flow over the next minutes,
hours, days and weeks.
– Optimize the power system accordingly (tradeoff
between reliability-economy)
From BD in the management of EES. Louis Wehenkel
• Asset management (mid term)
– Understanding factors driving aging and failures of
components
– Undestand critically of components’ availability for
system operation
– Optimize the repairing and replacement of
equipment accordingly
• Investment (long term)
– Predict usage of the power system over the next
years
– Accordingly, take highly strategic important
decisions
What can Big Data do for Generators?
• EV Load forecasting
– Improving their supply bids and increasing their benefits
• Integration of intermittent generation
• Combined generation bids:
– Wind energy + distributed storage capacity of EVs
EV AND TAXI FLEETS
Benefits of EV in taxi fleet
• Improvements in Air Quality
– EVs have zero tailpipe emissions.
– Highest GHG concentrations is found in areas with
high traffic rate (also high density of taxi trips).
– 20% of total generated electric energy in California
comes from renewables.
– Using cleaner energy
sources will reduce the
emissions asssociated
with powering EVs.
Benefits of EV in taxi fleet
• Reduced Carbon footprint
– Even after accounting for the energy-
production-level emissions associated
with Evs, electrification of taxis would
lower the fleet’s carbon emissions.
• Resiliency
– EV can also be designed to be usable
as mobile power storage units in the
event of an emergency
Benefits of EV in taxi fleet
• Visibility
• Price consistency
– Electricity prices are much less volatile.
• Energy security
– Reducing the country petroleum imports.
Economics of EV ownership
• Factors that determine the adoption of an EV:
– Vehicle purchase price
• Battery price is the key driver of purchase price
• Price is projected to decline over the time
– Maintenance and repairs
• Lower maintenance (savings)
– Battery replacement
• Battery is degraded over time, more quickly if is quick charged
(need to be replaced)
• Opportunity: battery reuse for static applications.
– Years vehicle in service
– Residual value
– Cost of electricity
• Taxi operators, to maximize
revenues and minimize downtime,
limiting time available for charging.
DATA INFORMATION
Data information
• General information:
– GPS traces 466 Vehicles of Yellow Taxi Cap.
– Collected: May-June 2008.
– Data provides:
• Lat-Lon
• Time stamp (Unix time)
• Ocupation
Data information
• It is assumed:
– We are focus on the taxi (no on the driver).
• Taxis can be driven by different drivers.
– Drivers have same skills (similar knowledge of the
city).
Consumption model
• EV consumption model
Input:
GPS track
Consider:
• Terrain elevation
• Auxiliary loads (lights/heating)
• Occupation (increasing mass
for vehicle occupied with
customer)
Output:
• Power
• Consumed energy
• SoC evolution
Consumption model
Consumption model
cosgMMRF dcarrr
2
2
1vACF da
singMMF dcarhc
aMMF dcarla 05.1lahcarrte FFFFF
• Equations:
vFP tete
Consumption model
• Forward driving:
gear
te
outmot
PP
_
mot
outmot
inmot
PP
_
_ auxinmotbat PPP _vFP tete
Consumption model
• Regenerative braking:
teratiogenregte PRP __ regtegearoutmot PP __ regtegearoutmot PP __ auxinmotbat PPP _
Consumption model
• Battery dynamics:
– Discharging process (moving forward)
– Charging process (regenerative braking)
Consumption model
• Results
0 2000 4000 6000 8000 10000 12000 14000 16000 180000
20
40
60
80
100
Time [s]
0 2000 4000 6000 8000 10000 12000 14000 16000 180000
20
40
60
80
100
State of Charge (%)
speed (kph)
Car Stopped
(Speed=0 kph & SoC=58%)
Battey SoC (%)
Consumption model validation
• We have tuned our model based on real test consumption:
– 111.4 km (69.2 mile)
– 3.9 miles/kWh
– 0.159 kWh/km
http://insideevs.com/real-world-test-2013-nissan-leaf-range-vs-2012-nissan-leaf-range/
Our consumption model: 0.165 kWh/km
Consumption model
• More complex Consumption models are available
http://vbn.aau.dk/files/55733132/Electric_Vehicles_Modelling_and_Simulations.pdf
Transmission
Electric MachineInverter (Power Electronics)Battery
Consumption model
http://vbn.aau.dk/files/55733132/Electric_Vehicles_Modelling_and_Simulations.pdf
RESULTS
Results
• Analyzing the spatio-temporal mobility of a single taxi vehicle.
Results
• Vehicle: 1
• Number of days: 24
• Number of movements: 49
• Number of stops (>30 min): 48
> 30 min
Results
>30 min
Empty
Occupied
Results
• How is the distribution of distance travelled
between two consecutive stops (> 30 min)?
Results
• Electrification Rate: 63.27%
Results
• Best location for charging points
Results
• How long are they stopped? Histogram stop time
duration
Results
• When are they parked? Histogram stop initial time
Results
• Energy demanded during the recharging process:
297.21 kWh
Results
ELECTRIC TAXI CONVENTIONAL TAXI
Results
• Total energy demanded: 297.211 kWh
• Electricity Price: 23.3 cents/kWh
• Total distance: 1,325.5 miles
• Total distance: 2,132.8 km
• Total cost: $69.25
• Gasoline Price: $3.692/gallon
• Consumption: 16 miles/gallon
• Total distance: 1,325.5 miles
• Total distance: 2,132.8 km
• Total cost: $244.56
• Saving: $175.31
http://www.bls.gov/regions/west/news-release/averageenergyprices_sanfrancisco.htma
ELECTRIC TAXI CONVENTIONAL TAXI
Results
• Centroid: (Lat, Lon):
37°46'42.2"N
122°25'01.6"W
• Radius of gyration:
2.56 km (1.6 mil)
Pick up points
Results
• Centroid: (Lat, Lon):
37°46'27.5"N
122°24'59.8"W
• Radius of gyration:
3.18 km (1.97 mil)
Drop off points
Results
• When are taxis occupied?
Results
• Average speed:
16.13 km/h
Empty taxi
Results
• Average speed:
26.92 km/h
Occupied taxi
Results
• Average distance:
3.52 km
Empty taxi
• Average distance:
4.2 km
Occupied taxi
Results
Results
• Analyzing the spatio-temporal mobility of a taxi fleet.
Results
• Number of analyzed Vehicles: 466 Taxis.
• Average number of days analyzed: 23 days
• Average number of stops > 30 min: 60
Results
• Gyration radius distribution for empty and
occupied situation.
km 785.091.3 occupied
gyrr
km 4.151.3 empty
gyrr
Results
• Time duration of the stops.
– Max: 17 days (413.5 hours)
– Stop (>30 min) less than 24 hours: 99.36%
• Average time duration: 2 hours 34 minutes.
Results
• Starting time to recharge EV taxis:
Results
• Energy recharged during the stops: 170 MWh
• Electrificability rate: 65.3% of the total journeys
Battery Capacity:
24 kWh
Results
• California daily electricity demand
http://www.caiso.com/outlook/SystemStatus.html
Results
• Impact on the California daily electricity demand:
0.002% in the peak (14:00)
ADDITIONAL RESEARCH
• A mobile application for identifying the potential for EV
adoption in company fleets.
• The app records:
– Distance traveled.
– Average speed.
– It is posible to record energy consumption.
New developments at ETSIT-UPM
• The app provides the user with
information about the daily driving
distances can be cover using an
electric vehicle.
• A database with the technical
specifications of different EVs are used
to advice users.
Mobile app description
• Initial screen:
1. Start to register
2. Analyzing a track
3. Analyzing all tracks
4. Share the track
11
2
3
4
4
32
Mobile app description
• From the recorded data (single track), the app provide
to the user with the following information:
– Electrificability (yes/no)
– Speed
– Electricity cost
– Fuel cost
– Number of subtracks
– Total distance travelled
– Total saving
Mobile app description
• From the recorded data (all tracks), the app provide to
the user with the following information:
– Electrificability (percentage)
– Number of tracks
– Electricity cost
– Fuel cost
– Average distance per track
– Total distance travelled
– Total saving
Mobile app description
• Configuration screen:
Improvements
• Providing feedback to drivers on how they are currently
doing.
• Comparing to personal
historical data
• Comparing to other similar
drivers
– Same mobility patterns (same
route or area)
– Same type of EVs
CONCLUSIONS
The future
• New applications can modify traditional business.
The future
• Recent Paper:
– Greenblatt, J. B., Saxena, S. “Autonomous taxis could greatly reduce
greenhouse-gas emissions of US light-duty vehicles”, Nature Clim. Change,
2015/07/06/online http://dx.doi.org/10.1038/nclimate2685
• Self-driving 'taxibots' could replace 90% of cars
• Driverless cabs will dramatically ease congestion in major cities
• Even with only one passenger per ride, car number dropped by 77 %
• Swapping personal cars with self-driving cabs would free valuable
space.
Questions?