Development of advanced air conditioncontroller for urban buses

PATRIK THEDE

Master’s Degree ProjectStockholm, Sweden September 2012

XR-EE-RT 2012:023

Abstract

The Advanced Air Condition Controller (AACC) function pre-sented in this master thesis reduces fuel consumption for theAir Condition (AC) compressor in urban buses. This is doneby compressing cooling refrigerant at locations where the ACcompressor can use freely available energy in the vehicle. TheAC system could for example compress cooling medium in adownhill or when a deceleration is done. In this master the-sis three different AACC functions have been developed. Thefirst utilize Look Ahead (LA) information. The second utilizeinformation about the current slope of the vehicle. The thirdAACC function is a combination of the two others. The AACCfunction that utilize LA information can decreased fuel con-sumption with 0.25%. The AACC function that utilize slopeinformation can decrease fuel consumption with 0.15%. TheAACC function that combines the best of the two other func-tions can decrease fuel consumption with 0.37%. For compar-ison, sometimes it is said that 1% fuel reduction is equivalentto one year of engine development. The decrease in fuel con-sumption can be done without major impacts on temperatureclimate inside the bus body. The simulation have been per-formed on a recorded bus route in Sodertalje.

Acknowledgements

I would like to thank Michael Skarped and Jonas Ekerlid for giving me the opportunity to perform mymaster thesis at Scania RBEB. I would like to express my deepest gratitude towards my supervisor JohanTholen. Johan have supported me throughout my master thesis, by listening to different suggestions andgiving valuable feedback on suggestions. I thank Henrik Sandberg at KTH for supervising my masterthesis, by giving wise inputs and feedback on my work. Without Fredrik Asell at Omni, measurementsin buses would have been nearly impossible. Thanks to Ludvig Wendelius at RBNT, for helping me outwith the Dymola model.

I would also like to thank all people at the RBEB department for being such wonderful colleges! FinallyI thank my friends and family for their great support!

Contents

1 Introduction 1

2 Background 22.1 AACC algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2 Look Ahead . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.3 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.4 Goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.5 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.6 Delimiters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.7 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

3 System description 53.1 Current system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.2 A brief explanation of an AC system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3 AC unit and AC compressor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73.4 ACC algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.5 Requirement specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.5.1 AC unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.5.2 AC compressor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4 Pre-study for AACC 114.1 Implementation possibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114.2 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124.3 Control strategies that could improve fuel consumption . . . . . . . . . . . . . . . . . . . 124.4 Requirement specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.4.1 Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.4.2 Advanced air condition controller function . . . . . . . . . . . . . . . . . . . . . . . 14

4.5 Discussion of pre-study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5 Modelling 165.1 Thermodynamic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5.1.1 Inside/Outside temperature effects on the internal energy . . . . . . . . . . . . . . 175.1.2 AC effects on internal energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195.1.3 Effects of extra energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205.1.4 Complete system model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

5.2 Adjusting and validation of thermodynamic model . . . . . . . . . . . . . . . . . . . . . . 215.3 ACC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215.4 Prediction of temperature inside the bus body . . . . . . . . . . . . . . . . . . . . . . . . . 225.5 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

6 GAC signal 236.1 Online and offline with LA data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

6.1.1 Vehicle model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236.1.2 Input signals to the AACC function . . . . . . . . . . . . . . . . . . . . . . . . . . 246.1.3 How the GAC signal is generated . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256.1.4 AACC online and offline function with LA data . . . . . . . . . . . . . . . . . . . . 29

6.2 Online AACC function without LA data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306.2.1 Input signals to the AACC function . . . . . . . . . . . . . . . . . . . . . . . . . . 316.2.2 How the GAC signal is generated . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316.2.3 AACC function without LA data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

7 Results 327.1 AACC online and offline with LA data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337.2 AACC online without LA data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377.3 Combination of AACC with LA information and AACC without LA . . . . . . . . . . . . 39

8 Sensitivity Analysis 408.1 Rolling resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408.2 Air resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418.3 Mass of the vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428.4 GPS inaccuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428.5 Poffset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438.6 Effect of best case PAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438.7 Oslo to Haugesund . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

9 Conclusion 469.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469.2 Final conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479.3 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

A Appendix 49A.1 Parameter values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

List of abbreviations

AC Air ConditionAACC Advanced Air Condition ControllerACC Air Condition ControllerAPS Air Processing SystemBCS Bus chassi Communication SystemBNS Bus Node SystemCOO Coordinator systemCRS Compression Refrigeration SystemECU Electrical Control unitGAC Guide Air ConditionHW HardwareLA Look AheadPID Proportional-Integral-DerivativeSW Software

List of symbols

Abus Surface area of the bus bodyAglass Area of glass existing on the busAroof Area of bus roofAv Front area of the vehicleACon/off Number of times the AC compressor have been turned on and offCd Air resistance coefficientcp Specific heat capacityCr Rolling resistance coefficientFair Air resistance forceFgrav Gravitational forceFroll Rolling resistance forceFtract Vehicle traction forceg Gravitational constantLAlength LA information horizonmair Mass of air inside the bus bodymairfan

Mass of air that is blown inside the bus body, through the evaporator each secondMv Vehicle massPbus Power existing in the vehiclePAC Power needed to use the AC compressorPoffset Power offset, used as a trade off parameterPsun How much power the sun affects each square meterPnet Net power after PAC and Poffset have been added to Pbus

QinsidebusChange in energy inside the bus body

Qinsidebus/ambient Power that is transferred between the outside of the bus to the inside of the bus

Qextra Power added by the sun to the inside of the bus bodyRroof Reflection rate of the roofρair Air densityTambient Temperature outside the bus bodyTglass Throughput of the glassTinsidebus

Temperature inside the bus bodyTinsidemargin

How many degrees that the inside temperature may deviate from thereference temperature when the AACC function is used.

4TinevapTemperature difference between air blow inside the bus bodyby the evaporator and existing air inside the bus body

4Tinsidebus/ambient Temperature difference between the inside of the bus body and the outside of the bus bodyTref Reference temperature for inside the bus bodyv Average velocity of the vehiclevair Air velocity

Chapter 1

Introduction

Scania buses consist of a chassis and a bus body. The chassis are manufactured by Scania and the busbody is manufactured and assembled by several different bus body manufacturers. Because the bus chassisand the bus body have different manufacturers; there exist an interface where the bus body manufac-turer can connect to the bus chassis systems. Bus body manufacturers connect to the electrical interfacethrough the Electrical Control Unit (ECU) that act as the interface between bus chassis systems and busbody systems. The ECU that function as the interface is the Bus Chassi Communication System (BCS).

In today’s system, BCS provides several unique signals that bus body manufacturers can use. In thefuture there could exist a signal that provides information about when there could exist ”free” energyto be used through the CAN bus. In today’s bus systems, several ECU’s are connected throughout anetwork in order to communicate with each other. The network is built with several ECU’s such as theBCS, the vehicle network is called CAN bus. In buses, the Air Condition (AC) system is one of thesystems that use a noticeable amount of torque from the motor in order to compress cooling medium.The utilisation of torque is equivalent to fuel consumption. Therefore it would be desirable if the BCScould with help from an algorithm could guide the AC system to compress cooling medium when it isoptimal in sense of fuel consumption. The AC system could for example compress cooling medium in adownhill or when a deceleration is done; the AC system could also avoid compressing cooling mediumwhen the bus need all power for take-off.

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Chapter 2

Background

In trucks and buses, a lot of resources are spent in order to decrease fuel consumption. Since a truckcan drive around 200 000 km each year, even a small reduction in fuel consumption could be economic.According to [4] the fuel consumption can increase with 12-17% when the AC system is used in mid-sizedvehicles. According to a Canadian study made on hybrid buses, an AC compressor have similar impact onbuses[8]. It would be desirable to decrease the fuel consumption of the AC compressor and maintainingthe temperature climate inside the bus body.

In this chapter, the Advance Air Condition Controller (AACC) algorithm and the Look Ahead (LA)concept are first introduced. LA is the concept which two of the AACC algorithms is based upon. Theoverall purpose and the goals for this thesis will follow. The approach of how the goals were accomplishand delimiters for this thesis will be described. The last section of this chapter is about related work tothis thesis.

2.1 AACC algorithm

The AACC algorithm have been developed to decrease fuel consumption for the AC compressor in urbanbuses. The AACC algorithm uses a guide AC (GAC) signal to make decisions regarding the AC compres-sor. The GAC signal is based on different kinds of information, depending on which AACC algorithmthat is used. If the GAC signal and the AACC algorithm was to be implemented as an AACC functionin a bus, then the function would probably be implemented inside the BCS.

2.2 Look Ahead

If all the systems in the bus would know exactly what route the bus would take and where the buswould stop, fuel consumption could be further minimised. The current LA system that exist providestopology information from the road ahead of the vehicle. LA information is for example used to choosethe most optimal gear for minimal fuel consumption. In a near future the LA system could be extendedwith information about bus stops along the bus route and traffic lights. This extra information could beutilised in other systems such as the Air Condition Controller (ACC).

2.3 Purpose

The purpose of this work is to create a guiding signal that is used in the controller of the AC system inbuses, which if used in the controller of the AC system generates a fuel saving.

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2.4 Goal

According to the work plan, the following goals should be fulfilled:

• Design a requirement specification for the bus manufacturer Omni’s AC system, where limitationsare clarified. The requirement specification shall be detailed enough, so that it can be used whenmodelling the AC system.

• Design a requirement specification for AACC where the limitations of the controller are clearlystated. The requirement specification shall be detailed enough, so that it can be used when designingthe AACC.

• Design a mathematical model of the climate system, where the climate system include all systemsthat affects the climate in the bus body.

• Design a ”guide AC system” signal that guides the AC system to compress cooling medium, whenthere exist ”free” energy to utilise.

• Design a mathematical model of the AACC, where the controller utilises information providedthroughout ”guide AC system” signal.

• Evaluate the guiding signal affect when simulation on predefined driving cycles such as SORT 1 &31. In order to show how the signal effect fuel consumption and the climate in the bus body.

• Evaluate the performance when the guiding signal is used in the AACC.

• If there is time, implement the AACC in a bus and evaluate towards the requirement specification.

2.5 Approach

In order to accomplish the stated goals the following approach was used:

Pre-studyA pre-study was performed in order to gather information about possible ways to accomplish thegoals. The information was gathered from technical articles and previous thesis works.

Mapping of current systemIn order to complete the stated goals a clear system overview was needed. By gathering informationfrom Scania employees and reading documentation that have some connection to the AC system. Asystem identification was performed in a real bus body, where the purpose was to map the currentcontrol of the AC system and also understand how the AC unit affects temperature climate insidethe bus body. This stage resulted in a distinct requirement specification and mathematical model.

Mathematical modelling and simulationBy creating a model that reflects how the climate in the bus body is affected, different controlstrategies that utilises a guiding signal could be evaluated. With a sufficient model, much time canbe saved when evaluating different control strategies, since the process that is controlled is slow.This phase was iterated several times.

Developing AACC algorithmDevelopment of the AACC algorithm was an iterative phase, where different solutions were testedin the mathematical model. This stage resulted in a complete AACC algorithm.

ImplementationImplementation of the AACC in order to test the function in a real environment was optional.

1SORT - Standardised on road tests:http://www.uitp.org/knowledge/projects-details.cfm?id=439

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2.6 Delimiters

This master thesis only involves designing and evaluation of a controller. Implementation of an AACCalgorithm is optional. The AACC algorithm should utilise a guiding signal to determine when it is optimalto compress cooling medium on the bus model Omnilink. Modelling and implementation will only beperformed on the test bus Kalle. The test bus has the following configuration:

Table 2.1: Test bus configuration

Name KalleBus model Scania OmnilinkBus configuration K310 UB6×2LBCompressor Bitzer 4PFC(Y) 558 cm3

AC unit passenger Aerosphere World 32 kW

2.7 Related work

There exist several articles on the subject ”intelligent energy management of vehicle AC systems”. Someof these articles utilises LA in their algorithms in order to decrease fuel consumption while other articlespresent a more intelligent control algorithm for the AC.

All found articles on the subject [1]-[4], only discuss AC systems on smaller vehicles such as cars. Theauthors also have full control of the AC system. This thesis discuss the AC system on a large bus wherethe proposed algorithm does not have direct control of the entire AC system but can only suggest to theACC whether it is optimal for the fuel consumption to use the AC compressor or not.

In [2], three different methods are presented to control the AC system. All presented methods usePID controllers. The first presented method was a manually tuned PID controller, the second one wastuned with Chien-Hrones-Reswick tuning method and the last one was tuned by a neural network tuningmethod. In simulation the writers achieve an energy reduction by 12% for the second method comparingto the first and 13.6% for the third method compared to the first. The article [2], shows the possibilityof saving fuel with an optimised control algorithm.

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Chapter 3

System description

In this chapter, the complete system in this thesis is described. All parts of the system may not benecessary to model in order to make a thermodynamic model and a functional AACC function. But anoverview of the system could give a greater insight of the complexity of the AC system. In the first sectionis a brief description of all systems that in some way support the AC system. The basic of an AC systemwill be described, followed by a description of the AC unit and the ACC algorithm. The last section inthis chapter is a requirement specification for the AC unit and the AC compressor.

3.1 Current system

The AC system in buses depends indirect and direct on several systems such as the Coordinator sys-tem(COO), the Bus Chassis System(BCS), the Bus Node System(BNS), the Air Condition Controller(ACC)and the AC components. An overview of the system can be seen in Fig 3.1.

COO

The COO unit could be described as a gateway between different CAN buses that exist in the vehiclenetwork. The COO handles all centralized functions that doesn’t obviously belong to any other ECU. Amajor part of the I/O of the actuators and sensors are handled by the COO.

BCS

The BCS enables the bus body manufacturer to communicate with chassis systems on the CAN bus. Arough explanation is that the BCS act as a gateway between the systems that are below the passengersfeet. This ECU is involved in several functions that are unique for bus manufactures e.g. kneeling andhill hold. An AACC function could for example be placed inside the BCS if it would be recognised as abus unique function

BNS

The BNS is developed by bus body manufactures and communicates with the BCS. It could be said thatthe BNS acts as the COO but only for the bus body unique functions, for example opening doors. Arough explanation is that the BNS acts as a gateway and controls everything that is above the passengersfeet.

ACC

The ACC unit that is often used in the Omnis bus is the Viper 2000, which is developed by the companyMCC(UWE). The ACC has the overall control perspective of the temperature inside the bus. The ACCcontrols the heating and cooling of passenger and driver compartments. The defroster and roof fans arealso controlled by the ACC, as well as the flaps used for recirculation of air. The control unit also checks

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that the safety conditions are fulfilled. If not fulfilled, the ACC can disconnect the AC compressor fromthe power train.

COO - BCS - BNS - ACC - AC components

Figure 3.1: An overview of the system

3.2 A brief explanation of an AC system

An AC system used for decreasing temperature inside of a bus Tinsidebushas the same functionality as

a refrigerator. The Compression Refrigeration System (CRS) consist of four basic components: com-pressor, condenser, evaporator and a throttling valve. A block diagram of the CRS can be observed infigure 3.2. The CRS contains a heat exchanger which is normally some type of cooling refrigerant withlow evaporation point. The refrigerant that is normally used inside AC system is the R134a, which havea boiling point at approximate −15◦C.

A refrigerant cycle is started inside the evaporator. In the evaporator, evaporates the refrigerant at a lowpressure and low temperature and thus absorbing heat from the surroundings. The refrigerant vapouris then removed by the compressor and compressed to a higher pressure and thus a higher temperature.The refrigerant is then condensed at the condenser and thus rejecting heat to the surroundings. Thepressure is then regulated by the throttling device to an optimal pressure level for the evaporator andthen is the refrigerant cycle complete.

suction vapor line

compressor

hot gas lineliquid line

throttling device

air cooled conenser

air cooled evaporator

injection line

Figure 3.2: A block diagram of the compression refrigeration system, see [9]

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3.3 AC unit and AC compressor

The air conditioning components contains an AC unit and an AC compressor. The AC unit on Omni’sbuses are manufactured by Spheros and normally have a cooling capacity of 32 kW or 39 kW for thepassengers. A 39 kW Spheros unit can be observed in figure 3.3. Often there exist an additional coolingunit close to the driver in order to establish a desired climate close to the driver. The AC unit containsall the parts that the is needed for a functionally AC unit e.g. evaporator, condenser etc.

Figure 3.3: The AC unit from Spheros is mounted on the roof of the bus

The AC compressor used in Omni buses is manufactured by Bitzer and is a mechanical compressor withfour cylinders and have a cylinder volume of either 558 cm3 or 647 cm3 deepening on the model. Theoperational speed of the compressor is between 500-3500 rpm and the compressor is optimised to delivermaximum compression at low engine speed. The compressor is almost directly connected to the powertrain through a magnetic clutch and a belt to the motor.

Figure 3.4: AC compressor with a belt connection to the power train

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3.4 ACC algorithm

The ACC controller is a fuzzy controller[6] with a hysteresis control strategy for regulating the tempera-ture inside the bus Tinsidebus

. To decrease Tinsidebusthe ACC can change the fan speed and use the AC

unit. The ACC decides whether air from inside the bus body should be recirculated through the evap-orator or if a mix of recirculated air and fresh air should go through the evaporator. The ACC alwayschooses the air that has the lowest temperature. The ACC checks that all safety conditions are fulfilledin order to avoid e.g. icing on the defroster. The ACC handles both increasing and decreasing of Tinsidebus

.

The ACC could be seen as a state machine that moves between 10 different states depending on Tinsidebus,

the reference temperature Tref for the ACC and Tinsidebus. A schematic figure of the hysteresis control

strategy can be observed in figure 3.5. The 5 states on the right side are used when Tinsidebus> 0 and

the 3 states on the left side are used when Tinsidebus< 0. The 2 states in the middle are used when

Tinsidebus<< 0. Transition between states depends on the temperature difference between Tinsidebus

andTref ; Tinsidebus

also effect transitions. The initial state that the ACC starts in depends on the differencebetween Tinsidebus

and Tref .

State 8

AC on

State 9

AC on

State 10

AC off

State 6

AC off

State 7

AC off

State 5

AC on

State 4

AC on

State 3

AC off

State 2

AC offTransition

State 1

AC off

Figure 3.5: A schematic figure of the ACC algorithm

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3.5 Requirement specification

3.5.1 AC unit

The AC unit normally used in Omni buses have a cooling effect of 32 kW and in rare cases 39 kW. The32 kW AC unit that the test bus Kalle was equipped with, can in optimal conditions cool the inlet airwith approximate 10◦C. According to logged data from a climate chamber test, the AC unit on Kalle de-creased the temperature of the air that went through the evaporator with approximate 8◦C independentof ambient temperature and engine speed.

This will thus limit the range where the AACC function can operate, since the ambient temperature andTref must be in a area where the AC compressor will normally be turned on and off by the ACC. Trefcan vary between 18◦C and 26◦C. This will limit the area where the AACC can operate to a temperatureoutside the bus body of Tambient = 19− 33◦C, depending on Tref .

Tinsidebusis calculated based on how many degrees the evaporator decreases the air that is blown inside

the bus body and the amount of air. The amount of air that is blown through the evaporator is dependenton the size and amount of fans that the AC unit has, this varies between different models.

There exist 6 fans on the 32 kW AC unit and the fans have 3 different speed levels. Because air isconstantly added to the bus body by the 6 fans, a higher pressure compared to the outside of the busbody will exist inside the bus. This higher pressure will act as a resistance towards the fans and reducethe amount of air that can be added to the bus body by the fans.

The recirculation flap changes the relationship between how much fresh air and recirculated air that goesthrough the evaporator. The ACC will always choose the coolest air but the current ACC configurationcan only choose between having 100% recirculation or 50% recirculation. Due to higher pressure insidethe bus body and insufficient insulation at the recirculation flap the relationship between fresh air andrecirculated air is close to 80% when 50% recirculation is chosen.

The recirculation flap will limit the performance of the AC unit if Tinsidebus> Tambient, which can occur

if the vehicle have been standing in the sun for some time. Because the AC unit cannot only use air fromoutside the bus to cool the interior of the bus body.

The air temperature after the evaporator decreases exponentially and the AC unit reach full coolingcapacity after approximate 120 s. The air temperature at the evaporator outlet have decreased to 80%of the full cooling capacity after 60 s.

Summary of requirements for the AC unit:

• The AC unit can only cool the air entering the evaporator with approximate 8◦C.

• The recirculation flap can only choose between 100% recirculated air and 50% recirculated air.

• The recirculation flap gives a relationship of 80% recirculated air and 20% fresh air, when it shouldgive 50% recirculated air and 50% fresh air.

• Full cooling effect from the evaporator takes about 120 s.

• Engine must have been turned on for more than 1 minute before the AC unit can be started.

• The AC unit cannot be started if the system has been in heating mode for the last 10 min.

• Battery voltage must be greater than 21V, for the AC unit to be started.

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3.5.2 AC compressor

According to compressor specifications, a Bitzer AC compressor must be guaranteed a runtime of min-imum 2-3 minutes each time the compressor is turned on. This is due to wear that will occur if thecompressor is turned on and off more often. The compressor should not be switched on and off morethan 8 times each hour in order to avoid additional wear.

The magnetic clutch that connects the compressor to the power line cannot be switched on and off formore often than 1 time each minute, due to wear. The BCS or the BNS have a safety function thatdisconnects the compressor from the power line if the engine speed is higher than a predefined value setby the bus body manufacturer. This is accomplished by disconnecting the magnetic clutch. This alsohave to be seen as an on and off switch for the AC unit, which is limited to 8 each hour.

Summary of requirements for the AC compressor:

• The AC compressor must be guaranteed a runtime of 2-3 minutes when turned on.

• The magnetic clutch cannot be switched on and off more than 1 time each minute.

• The BCS or the BNS have a safety function that will disconnect the AC compressor if the enginespeed is higher than a specific value.

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Chapter 4

Pre-study for AACC

This report aims to investigate and develop a function that if used will save fuel while maintaining a de-sired temperature climate inside the bus body. There are no limitations for what types of hardware (HW)that can be used or what signals can be utilised by the function. Although an underlying aim is to de-velop an AACC function that can be used with available HW and available signals that exist in currentbus systems. A working AACC function that use available HW and signals could be implemented as asoftware (SW) update and would thus not lead to any increase in cost of additional HW.

In this chapter different implementation possibilities will be discussed, the robustness aspects of the AACCfunction and different control strategies that could lead to lesser fuel consumption. The requirementspecifications for the GAC signal and the AACC function will be described, and a discussion of thepre-study will end this chapter.

4.1 Implementation possibility

The AACC function could be implemented in at least two different ECU’s. Either inside the ACC orinside the BCS as an extra function. Because the ACC is bus body manufacturer dependent and theidea would be to sell the AACC function as a function to bus body manufacturer, the AACC function isthought of as implemented inside the BCS.

In order to affect the fuel consumption, the AC compressor must either be turned on or off by the AACCfunction. To maintain the temperature climate inside the bus body the AC compressor must also followthe control signal from the ACC. This would lead to a conflict between the ACC and the AACC function,this could lead to undesired effects in the system. If one is chosen as the master, the slave functionalitycould be affected as the master’s functionality has higher priority. This could be possible by letting theAACC function take full control of the AC compressor. The AACC function would also have to take thebus climate into consideration when choosing to turn the AC compressor on or off. This would thus turnthe AACC function in to an ACC with an implemented AACC function.

One way of affecting the AC compressor without creating a conflict between different functions and nothaving to design an AACC function that works like an ACC with implemented AACC function is tomanipulate Tref . The AACC function would then manipulate Tref and acquire a desired on and offpattern, which would lead to a decrease in fuel consumption.

Since this type of implementation only requires a manipulation of Tref and can probably be done insideone of the ECU. This method is suggested as a cheap way to implement the AACC function.

There exist some risks of adding uncontrolled oscillations to the climate system since the AAC may notbe designed for fast changes in the reference signal. This would need to be further investigated if theAACC function were to be implemented.

11

4.2 Robustness

One underlying idea with the AACC function is that it should not increase the fuel consumption, itshould only suggest to turn on or off the compressor if the function is absolute sure that it will not leadto an increase in fuel consumption. This could be accomplished by rather giving a few good suggestionsthen a lot average suggestions.

The robustness and success rate of the function will highly depend on what type of information that isavailable for the function. If for example the available information is based on LA data, then the outcomefrom a recommendation has a high likelihood to lead to a decrease in fuel consumption. Suggestionsbased on LA information could be made with less security, but suggestions based on information that isonly valid now or have been valid before is less reliable. The usage of LA information makes it possibleto make decisions based on future knowledge. LA information could almost be compared to buying alottery ticket and knowing the outcome before buying the ticket and based on that knowledge makingthe decision to buy the ticket.

4.3 Control strategies that could improve fuel consumption

In this section, investigated control strategies concerning saving energy while using the AC compressorare listed. The general concept is to compress refrigerant when there exist ”free” energy, the second ideais to avoid compressing refrigerant when the cost of compressing is too high e.g. travelling up a hill. Thelast idea is to store energy when the cost is low.

Situations when the energy is ”free” could for example be when the bus driver does not use the acceleratorpedal or when the bus is travelling down a hill. In some of today’s trucks system is some LA informationused in order to foresee what gear should be chosen in order to minimise fuel consumption.

Below follows some of the proposed control strategies for minimising fuel consumption.

Braking and retarder

This control strategy use the concept of that there should exist ”free” energy when the driver or theother functions want to decrease the vehicles velocity. This information would come from signals on theCAN bus, indicating if the driver or other functions is using the brake, retarder or both at the sametime. Positive aspects of this strategy is that it is restrictive; since it is guaranteed that there alwaysexist ”free” energy when the driver wants to decrease the velocity.

Negative aspect is the limited time when the vehicle actually is breaking and can compress cooling mediumfor free.

Acceleration pedal

This control strategy is based on the idea that there often exist ”cheap” energy or ”free” energy whenthe acceleration pedal is not depressed. This strategy is similar to the braking strategy presented above,but use the position of the acceleration pedal as input. Positive aspect of this strategy is probably thenumber of occurrence, when the acceleration pedal is not pressed down. Negative aspects are that theremay not exist ”free” or ”cheap” energy when the driver has the acceleration pedal in upright position,because the driver may not intend to decrease the velocity of the vehicle.

This strategy is currently used, when air is compressing for the Air Processing System (APS).

12

Look Ahead

The concept for this control strategy is to utilise information that would normally be available in a nearfuture for the bus driver and bus systems. This future knowledge could for example be topology infor-mation, distance to upcoming bus stop, distance to upcoming traffic light on the route and the overallbus route.

Topology information could inform bus systems in advance that the bus is approaching a downhill. Ifa predefined velocity should be maintained during travel down the hill, a negative acceleration may beneeded. The brake or retarder could probably be used to maintain the predefined velocity. In this casecould the bus system inform other systems that there will exist ”free” energy, which could be used tocompress refrigerant.

If the bus is approaching a bus stop on the bus route, then there could exist “free” energy. The challengeis to decide whether the bus really have to stop at an upcoming bus stop i.e. if there exist “free” energy.If any passenger press the stop button when approaching a bus stop then there exist “free” energy. Butthere may exist ”free” energy even if the stop button has not been pressed, because there could be apassenger waiting at the bus stop.

When approaching traffic lights, the same strategy as when approaching bus stop can be implemented.With the exception that there is some probability that the bus have to stop and ”free” energy can be usedto compress refrigerant. In order to increase the take off performance after a bus stop or a traffic lightthe AACC function should recommend turning off the AC compressor if it is turned on or not starting itfor some time after a take off.

If the entire bus route is known from start, and there exist a control strategy for when to compressrefrigerant. Then the optimal situations for compressing refrigerant could be decided right from the startor during a run.

Compress refrigerant when standing still at a bus stop

There might exist ”cheap” energy when the bus stands still at a bus stop with idle engine speed, sincethe AC compressor has a high efficiency at low engine speed.

Store energy

In thermodynamic systems, one way of storing energy is to either to increase or decrease the temperatureof the system. This could be done when there exist ”free” or ”cheap” energy. If the ACC is trying to keepa desired temperature inside the bus body that is lower than the ambient temperature of the bus body,energy could be stored by decreasing the temperature below the desired temperature. This will resultin a lower air temperature than desired but this temperature buffer can be used when energy is expensive.

Do not compress cooling refrigerant in an uphill

One way of saving fuel could simply be to avoid to compress refrigerant in an uphill by either recommendthe ACC to not start the AC compressor or by turning off the compressor in uphill’s.

13

4.4 Requirement specifications

4.4.1 Signals

The GAC signal is an imaginary signal that would be generated inside the BCS if the AACC algorithmis implemented. In this report there exist no limitation for what type of information that the GAC signalcan be based on. Depending on different algorithms, different type of information is used. The GACsignal can for example be based upon LA information which provides information about future topologyof the bus route. An AACC function can be based on LA information, but also other types of information.

Signals that exist in the CAN bus that is of interest for the GAC signal are the following:

• Retarder activated.

• Accelerator pedal position.

• Bus stop brake activated.

• Break pedal position.

• Engine speed.

• Door open.

• Tambient.

• Tinside.

• Tref .

4.4.2 Advanced air condition controller function

As mentioned in section 3.5.1, the operating range of a potential AACC function is between Tambient =19 − 33◦C depending on Tref . The GAC signal is generated in the imaginary AACC function. TheGAC signal may also be used inside the AACC function depending on how the GAC signal and AACCalgorithm is implemented. A block diagram of the AACC function can be seen in figure 4.1.

The GAC signal should only suggest to the ACC where optimal locations to compress refrigerant inregard to minimise the fuel consumption are located. The GAC signal and the AACC algorithm couldalso recommend where to avoid compressing cooling refrigerant to increase performance. The GAC sig-nal should rather give a lesser fuel savings then risking misleading the ACC and generating a fuel increase.

The challenge for the GAC signal and the AACC algorithm is to handle the HW limitations for the ACcompressor and not increase any fuel consumption. While maintaining a comfortable temperature climateinside the bus body.

The GAC signal and the AACC algorithm will hence be described as the AACC function.

Summary of requirements for the AACC:

• The AACC algorithm can only operate in the region of Tambient = 19− 33◦C.

• Should not increase fuel consumption.

• Should not affect abs(Tinsidebus− Tref ) more then a predefined Tinsidemargin .

14

AACC function

GAC

signal generatorAACC

algorithm

Figure 4.1: A block diagram of the AACC function

4.5 Discussion of pre-study

In order to design the AACC function as an ACC independent function, it must be implemented insidethe BCS. The GAC signal could also be given as an input signal to the ACC but then there would be anincreased implementation cost for the manufacturer of ACC and the GAC signal might not be utiliseddue to the increase in development costs. If the implementation is done by manipulation of Tref , thenthe implementation can be done almost independent on ACC manufacturer.

To ensure that the fuel consumption does not increase, the information used for making decisions mustbe highly reliable. LA information is highly reliable data that can be used to guarantee that the fuelconsumption does not increase.

A challenge with the AACC function is to identify when fuel consumption can be decreased by alteringthe usage of AC compressor, for example not using the AC compressor in positive slopes.

Due to limitations in the AC unit, strategies that only use information on braking, retarder and acceleratepedal is highly unreliable and could result in increased fuel consumption. The strategy that involves LAinformation is more likely to guarantee a decrease in fuel consumption. The LA strategy can both provideoffline and online functions.

As an alternative method to the LA strategy, the strategy to delay a potential turn on of the AC com-pressor in an uphill, is believed to be the simplest method to implement because it does not have to takethe limitations of the AC compressor into consideration which other solutions have to do.

15

Chapter 5

Modelling

In order to evaluate and verify the impact on Tinsidebuswhenever the AACC function utilises the GAC

signal, a basic thermodynamic model were developed to evaluate how Tinsidebusdepends on Tref and

Tambient. The thermodynamic model is based on the assumption that there exist perfect air mix and thatthe gas has ideal behaviour. As described in section 3.1, all ECU’s are not necessary for modelling therelationship between how Tinsidebus

is affected when using the AC unit and AC compressor. The ACCneeds to be modelled in order to predict how Tinsidebus

is affected when the AC unit cools air. The BNSand BCS, are modelled as signal inputs to the mathematical model. An overview of the thermodynamiccontrol system can be observed in figure 5.1.

Cool a

ir

AC components

AC

on

ACCBus

bodyTemp inside

GA

C

Com. BNS

AACC

Inside BCS

From COO

From BNS

Figure 5.1: An overview of the thermodynamic control system

16

5.1 Thermodynamic model

A basic thermodynamic model is based on the first and second law of thermodynamics, stating that thechange in internal energy is equal to the amount of heat added to the system and the work done by thesystem.

Qinsidebus= Qinsidebus/ambient + QAC + Qextra (5.1)

Qinsidebus= mair · cpair · Tinsidebus

(5.2)

Where Qinsidebusis the change of internal energy inside the bus body. Qinsidebus/ambient is the energy

that is transferred between the bus body and the exterior of the bus body. QAC is the decrease in energythat the AC unit is affecting the energy inside the bus body with and Qextra is extra energy sources, forexample the sun. A schematic figure of the basic thermodynamic model can be seen in figure 5.2. cpair

is the specific heat capacity of air and mair is the mass of air inside the bus body.

Qinside /ambient QAC

extraQ.

..

Tinside

Tambient

bus

Figure 5.2: Closed thermodynamic system

The thermodynamic system is modelled as a closed system. Meaning that no mass enters and exits thesystem. This is a simplification, because the AC unit adds additional air to the system. Because the ACunit adds air to the system a higher pressure will exist in the bus which leads to that air will exit thebus. To simplify the model, the thermodynamic system is seen as a closed system, i.e. there is no massthat enters or exits the system.

Depending on the temperature difference between Tambient and Tinsidebusdifferent scenarios occur. The

work that the system achieves could for example be the amount of energy that is used in order to heatthe cooler air that is blown inside the bus body from the evaporator, in order to accomplish thermal equi-librium. Qinside/ambient could either be seen as a heat source or a work source depending on difference

between Tambient and Tinsidebus. Qextra can always be seen as an additional heat source. An example of

an additional heat source is the sun.

5.1.1 Inside/Outside temperature effects on the internal energy

According to the second law of thermodynamic, heat tends to move from a system with higher temper-ature to a system with lower temperature. If external work is applied, energy can be transported froma system with lower temperature to a system with higher temperature. The bus body can be seen as asystem with either higher or lower temperature than Tambient.

The exterior of the bus body is seen as a system. The amount of energy that is transferred betweenthe interior of the bus body and the outside of the bus body is dependent on the temperature difference4Tinsidebus/ambient = (Tinsidebus

- Tambient), and the heat transfer coefficient know as the U value. The

17

U value describes how much energy that is transferred per unit area.

The U value for the bus body is calculated based on data, collected from a test where the bus was placedfor night storage inside a climate chamber. During night storage, the bus is often connected to a stationto recharge the battery and additional energy is added to keep Tinsidebus

at a comfortable temperature.This is done so that the bus driver that will use the bus in the morning does not have to freeze. Byextending the basic thermodynamic model seen in Eq.5.1, by adding the U value and removing QAC sinceno AC is used during night storage Eq.5.3 is derived.

Qinsidebus= Qinside/ambient + QAC + Qextra

mair · cpair· Tinsidebus

=Abus · 4Tinsidebus/ambient · λ

d+ Qextra

U =λ

d

U =(mair · cpair

· Tinsidebus)− Qextra

Abus · 4Tinsidebus/ambient

Tinsidebus=4Tinsidebus/ambient

τ+

Qextra

mair · cpair

(5.3)

τ =mair · cpair

Abus · U

In Eq. 5.3 mair is the air mass, cpair is the specific heat capacity of the material, Abus is the area of the

bus body and t is time. Qextra is the additional energy that is added to the bus during night storageto keep the interior of the bus body at a pleasant temperature during the night. τ is the systems timeconstant.

The temperature inside the bus body during the test can be observed in figure 5.3 as measured data.Tambient during the night storage test is approximately −1◦C. Because the interior of the bus body iscomposed of several different materials it is difficult to determine a specific heat coefficient for all mate-rials that is used inside the bus body. The mair and the cpair

in Eq. 5.3 are adjusted so that the timeconstant τ , fits the measured data seen in figure 5.3.

By adjusting the time constant in Eq.5.3 to measured data in figure 5.3, with the Matlab function pem.The MATLAB function pem can adjust parameters in a model to measured data. The U value wascalculated to be close to U = 6.0668[ W

m2·K ].

18

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

x 104

9

10

11

12

13

14

15

τ adjusted to measured data

Time [s]

Te

mp

era

ture

[° C

]

datacomp

; measured

m1; fit: 90.67%

Figure 5.3: τ in Eq.5.3 adjusted to fit measured data

By inserting the derived U value inside Eq.5.3 and removing the Qextra the relationship between theeffect of the temperature difference between the inside and the outside of the bus body is.

Qinside/ambient = Abus · 4Tinsidebus/ambient · U (5.4)

5.1.2 AC effects on internal energy

The AC unit decrease Tinsidebusby adding cooler air that mixes with the warmer air inside the bus body.

According to the second law of thermodynamic the difference in temperature between the air that theAC unit adds, and the air inside the bus body should move towards zero. To simplify the thermodynamicmodel, the exchange of energy between the warmer air and the cooler air is modelled as instantaneously.

Air from either the exterior or interior of the bus body is sucked in by fans through the recirculation flapand then pushed through the evaporator that cools the air. The air that goes through the evaporatoris approximately cooled by as much as 10◦C in optimal conditions. According to measured data froma climate chambers test, the air temperature is normally decreased by approximately 8◦C. A schematicfigure of the mounted AC unit on the roof can be seen in figure 5.4.

The change in temperature inside the bus body depends on how much cooler air that is blown insidethe bus by the fans. The fans have 3 different speeds levels which for this ACC is configured as 100%,66% and 33%. The amount of air that the fans add to the bus body were calculated by measured whatspeed the air entered the fan hole. By calculating the area of the hole, the amount of air blown inside thebus by the fans at a specific fan speed could be calculated. The measured air speed is associated withsome measurement error because of the difficulty to measure a uniform air speed in the fan hole. Theamount of energy that will be absorbed by the cooler air from the warmer air inside the bus body can bedetermined with Eq. 5.5.

QAC = mairfan· cpair

· (Tevap − Tinside) (5.5)

QAC = mairfan· cpair

· 4Tinevap

19

Evaporator Fan

Fresh air

Recirculated airInside bus

Figure 5.4: The AC unit mounted on the roof

Where mairfanis the mass of air that is added by the AC unit each second, cpair is the specific heat

constant. Tevap is the temperature of the air that exits the evaporator. The difference between Tinsidebus

and Tevap is normally 8◦C. Thus 4Tinevapis set to be a constant value of 8◦C. This is a simplification of

the AC unit’s effect but needed in order to simulate the system.

5.1.3 Effects of extra energy

In order to keep the thermodynamic model as simple as possible, only the sun have been included as aexternal source of energy. There exist several other sources extra energy sources that could have beenincluded in the model. Heat from passengers, heat from the engine room and heat from the road. Tosimplify the model, heat from the engine room and the road has been neglected. The reason passengerswere not included as an extra energy source is that there existed no reliable data from any test run withpassengers. In [5], the extra energy that a passenger would affect the system with is Qpassenger = 300W.Additional passengers would also add extra mass to the system and change the specific heat constant.Because of the extra mass that additional passengers would add and the change in the specific heat con-stant, it cannot be foreseen how passengers would affect the time constant τ in the thermodynamic model.

The impact from the extra energy that is omitted by the sun is dependent on the geographical location ofthe bus. In Sweden relevant data have been collected from the national weather association[7] regardinghow much power the sun effects each square meter. Some part of the energy is transmitted through theglass which have a throughput of approximately 25%. The rest of the energy from the sun is transmittedthrough the roof where the metal reflects up to 80% of the transmitted energy. The final Qextra equationcan be seen in Eq. 5.6.

Qextra = Aroof · Psun ·Rroof +Aglass · Psun · Tglass (5.6)

Where Rroof is the reflection constant of the roof material, Aroof is the area of the roof, Aglass is thearea of the glass and Tglass is the throughput of the glass,

5.1.4 Complete system model

By inserting Eq.5.2, Eq.5.4, Eq.5.5 and Eq.5.6 into Eq.5.1, a complete thermodynamic model for how thetemperature inside the bus body changes when the AC unit is used for a specific ambient temperatureand when extra energy from the sun is applied.

Tinsidebus=4Tinsidebus/ambient

τ+mairfan

· 4Tinevap

mair+

Qextra

mair · cpair

(5.7)

τ =mair · cpair

U ·Abus(5.8)

Where τ is the time constant of the thermodynamic model.

20

5.2 Adjusting and validation of thermodynamic model

By using data collected from a climate chamber test with the test bus Kalle, where the AC units coolingcapacity was tested. Parameters inside Eq.5.7 could be adjusted to fit the available data. Half of availabledata collected from the climate chamber test were used to adjust parameters test. All parameters exceptthe U value and Abuss were adjusted to fit available data. Adjusted values can be found in appendix. Afteradjusting parameters, the model in Eq. 5.7 had a MSE = 0.4992◦C and a MPE = 1.96%, when validatedagainst the rest of the data from the climate chamber test. The result of the adjusted thermodynamicmodel can be observed in figure 5.5.

0 0.5 1 1.5 2 2.5 3 3.5 428

30

32

34

36

38

40

42

44

Time [h]

Te

mp

era

ture

[°C

]

Theromodynamic model with adjust parameters

Meassured

Simulated

Figure 5.5: Eq.5.7 simulated with adjusted parameters.

Since the adjusted model is validated against data that is similar to the data that the model have beenadjusted to. It is to be expected that the adjusted model should give good results.

5.3 ACC

The ACC controller described in section 3.4, were modelled in Stateflow as a state machine with discreetstates. It is modelled with 10 states where the first 5 states are states for Tinsidebus

> 0 and 3 statesare used for Tinsidebus

< 0. 2 states are used when Tinsidebus<< 0 and the rest are used when the

temperature is decreasing at a normal rate. All states have direct control if the AC unit should be turnedon or off, and what fan speed that should be used.

The setting for recirculation is for simplicity placed outside the Stateflow model and chooses the coolestair that is available for the system. Due to limitations in the ACC function, performance of the AC unitcan be limited, if for example Tambient < Tinsidebus

when the ACC is started. The ACC would only beable use 20% of the cooler outside air, since the recirculation setting is limited to 50% and in reality onlyuses 20% fresh air and 80% recirculated air. This example could occur if the bus has been standing inthe sun for some time before the AC is started.

21

5.4 Prediction of temperature inside the bus body

According to previous section the model was adjusted to fit half of the available data and verified againstthe rest of the data with good results. The model should be able to predict the temperature with anerror of ±0.5◦C.

The adjusted model was fed with data from a ”normal” run with the test bus Kalle. The bus was takenon a normal run on regular roads in Sweden on a summer day. The temperature inside the bus body andthe ambient temperature were logged. The thermodynamic model was combined with the ACC modeland Tinsidebus

was simulated and compared with measured data from the ordinary run. The result canbe seen in figure 5.6, the run have a MSE = 0.4696◦C and a MPE = 2.64%.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.816

18

20

22

24

26

28

30

Time [h]

Te

mp

era

tur

[°C

]

Measured T

inside

Simulated Tinside

Figure 5.6: Ordinary test run with Kalle with measured and simulated Tinsidebus

The oscillations that occur in the end of the measured data in figure 5.6 are measurement errors. Sincethe temperature inside the bus body cannot increase with almost 1◦C in less than 1 minute.

5.5 Software

For modelling and simulations MATLAB, Simulink and StateFlow have been used. In order to read datafrom CAN buses the software CANanlyzer has been used. To estimate fuel savings a Dymola model havebeen used.

22

Chapter 6

GAC signal

In this chapter is the GAC signal that is used by the AACC function that utilise LA data first described.The GAC signal that is used by the AACC function that does not utilise LA data is later described. TheAACC function that combines LA information and delays AC unit cycles is not described, since the GACsignal used by the AACC function is just a combination of the two other. The AACC function presentedin this thesis, is developed to investigate whether or not it is possible to decrease fuel consumption bycontrolling the AC compressor with other reference signals than Tref . All possible HW and signals havebeen available in order to achieve that purpose of this thesis. Although the ability to implement the func-tion has always been an optional goal. The main goal is thus to design an AACC function that achievesthe main purpose of the thesis, which is to decrease the fuel consumption and maintain a comfortabletemperature climate inside the bus body.

6.1 Online and offline with LA data

In order to design a function that does not risk increasing the fuel consumption, the decisions must bemade by using the most reliable data. For this type of function the most reliable data is the LA data thatcan give information about future events in advance. The first AACC function is based on LA data andthat the bus route is known, which is a valid assumption since this thesis is focused on city buses. Thefunction works both in offline and in online mode. In offline mode could the optimal points for turningon the AC compressor be calculated when the bus is turned on. In online mode could the optimal pointsbe calculated during a run.

6.1.1 Vehicle model

To foresee where optimal points for turning on the AC compressor should be located during a bus route,a vehicle model was used based on the longitudinal motion, described by Newton’s second law.

Mv · v = Ftract − Froll − Fgrav − Fair (6.1)

Where Mv is the mass of the bus, v is the acceleration, Ftract is the traction force, Froll is the resistancefrom the road to the wheels, Fgrav is the gravitational force acting on the vehicle and Fair is the airresistance. In order to simplify the vehicle model the velocity is seen as constant i.e. v = 0.

Ftract = Froll + Fgrav + Fair (6.2)

The forces acting on the bus can be seen in figure 6.1.

23

F air

Fgravity

Froll

Figure 6.1: Bus with forces

Rolling resistance

The rolling resistance is give by

Froll = Mv · Cr · g · cos(α) (6.3)

where Cr is the rolling resistance parameter that can vary between 0.006 and up to 0.01.

Gravitational force

The gravitational force is given by

Fgrav = Mv · g · sin(α) (6.4)

Air resistance

The air resistance is given by

Fair =1

2· Cd ·Av · ρair · (v − vair)2 (6.5)

Cd is air force constant, Av is the front area of the bus, ρair is the air pressure and v is the averagevelocity of the bus during a drive on the bus route.

Combined vehicle model

By inserting Eq.6.3, 6.4 and 6.5 into Eq.6.2 the vehicle model can be expressed as Eq.6.6.

Ftrac = Mv · Cr · g · cos(α) +Mv · g · sin(α) +1

2Cd ·Av · ρair · (v − vair)2 (6.6)

Values for parameters used in Eq.6.6 can be found in appendix.

6.1.2 Input signals to the AACC function

In this thesis there exist no limitations to what signals that can be used by the AACC function. Signalsthat might be used in future bus systems have also been allowed as inputs to AACC function. By usingthe LA concept and imagining what type of LA signals that could be desired to have in a near futureinside a bus, some imaginary signals have been invented.

24

The LA data that exist in bus or truck systems today is the topology of the road, this information isused to make optimal gear shifts and to disengage the clutch. The disadvantages with the LA topologydata is that the system does not know which way the driver will take and guesses that the driver will notchange the direction of the bus. The strategy works good on highway but has more difficulties in citytraffic, but the advantage of bus traffic is that the route of the bus is often predetermined. If the routeis known, LA data on topology can be used.

The LA signals that have been invented are information about where bus stops are located and wherethe traffic lights are located. This is to increase the take off performance of the bus at these critical points.

The AACC function also demands other types of information then the LA data in order to work. Belowfollow a summary of all signals used by the AACC function.

• Topology LA data.

• Traffic lights LA data.(imaginary signals)

• Bus stop LA data. (imaginary signals)

• GPS position.

• Mv.

• Tinsidebus

• Tref

• Slope information.

The LA topology information can be available in modern systems, the GPS position can be providedthrough an ECU. The mass of the vehicle can be estimated if it’s unknown. The only thing that is notavailable in modern systems is the route information and the imaginary signals.

6.1.3 How the GAC signal is generated

Background for GAC signal

In order to determine where the optimal locations are to use the AC compressor, one way is to calculatehow much power that is available and sees if there is enough ”free” energy to use the AC compressor. Thepower consumption of the AC compressor depends on the engine speed, which is not known before thebus is actually driving. The power consumption of the compressor can be determined by examining thespecifications on the AC compressor. The power consumption of the AC compressor at different enginespeeds can be observed in figure 6.2. This is based upon a worst case scenario for the power consumption.

An experienced driver or an automatic gearbox normally keeps engine speed between 800 rpm and 1800rpm, this is the rpm area where the engine is in ecomode. In ecomode is the fuel consumption lowcompared to other engine speeds. Because the engine speed is unknown throughout the route from thebeginning, the engine speed is assumed to lie inside the ecomode area i.e. 800-1800 rpm.

25

500 1000 1500 2000 2500 30002

4

6

8

10

12

14

16

PA

Ccom

pre

ssor[k

W]

RPM [revolutions/min]

Power consumption

Figure 6.2: Worst case power consumption of AC compressor depending on engine speed

Locate optimal locations to use AC compressor

The power consumption for the bus can be calculated with Eq.6.7,

Pbus = Ftrac · v (6.7)

Where v is the average velocity of the bus. If the result of Eq.6.7 is < 0 then there exist ”free” energythat could be used to run the AC compressor for free if there exist enough ”free” energy. By extendingthe Eq.6.7 and adding the power required to use the AC compressor and an offset to ensure that theresult is reliable; the optimal locations can be calculated for the route. The extended power equation canbe seen in Eq.6.8.

Pnet = Pbus + PAC + Poffset (6.8)

If the result of Pnet is still < 0 then this location is a point where the AC compressor can be used withoutconsuming any fuel. The PAC , is set as a constant value throughout the whole run. This is a simplificationsince the power consumption from the AC constantly changes depending on the engine speed. At sometime the power consumption expected by the function may be lower than the actual power consumption,because of this is the Poffset introduced to ensure that the function does not suggest points where theredoes not exist ”free” energy. The resulting Pnet is calculated throughout all points on the route.

Handling limitations of the AC compressor

Due to limitations of the AC compressor, the AC compressor must be guaranteed a run time of 2-3minutes which is defined as the minimum LAlength. This means that when the AACC function suggest

to the ACC to turn on the AC compressor, it must be guaranteed that∑120−180

t=0 Pnet(t) < 0. Sometimesthe sum of Pnet(t) is < 0, but the difference in altitude at the start point and endpoint of LAlength is > 0.This could lead to an increased fuel consumption if the AACC function suggest that the AC compressorshould be turned on at a local positive slope. Because the engine needs to deliver enough torque to drive

26

the bus forward and deliver enough torque so that the AC compressor can compress cooling refrigerant.The extra torque that needs to be delivered to the AC compressor in order to compress refrigerant couldlead to higher engine speed. This is almost equivalent to higher fuel consumption.

To avoid giving suggestions that could lead to increased fuel consumption, the AACC function investigateif there exist any altitude difference between the initial start point and the end point. The function alsocheck if there exist any local peaks close to the start point, so the slope of a close range is checked forlocal altitude maximums. In figure 6.3 the different criteria ranges that AACC function checks can be seen.

LAlength

Alt

itude [

m]

[s]

Check P and diff. altitudenett

check for local maximum

Figure 6.3: What the function checks before suggesting points to turn on the AC compressor

Investigating impact of different inputs

Because the acceleration of the bus is only known when the bus is driving, the velocity is seen as constantthroughout the bus drive i.e. v = 0 in Eq.6.1. The velocity of the bus has a great impact on how bigthe value of Fair becomes, since it is a second power equation. Since both v and PAC throughout theroute have a big effect on where the optimal points are located and are unknown at the start of the busroute. Because v and PAC are set to constant values in Eq. 6.8, a variety of different values are tested.In order to know how different values affect the location of where to use the AC compressor for free. Thecalculations of Pnet(t) throughout the route are performed inside the GAC function. Pseudo code of howdifferent values are used to generate different results can be seen below.

v = vmin.....vmax [m/s]PAC = PACmin .....PACmax [W]for i=1, i≤ length of v, i++ do

for j=1, j≤ length of PAC , j++ doGAC(i) = GAC function(v(i),PAC(j));

end forend for

Different GAC signals will be generated depending on different v and PAC , as can be observed in figures 6.4and 6.5. By closely observing the figures, it can be noticed that at the time 2000 and 5000 exists a differentbetween the two GAC signals. The idea is to get a final GAC signal pattern that only recommend pointsthat will have good performance for all different v and PAC . This is accomplished by multiplying alldifferent GAC signals that are generated with different v and PAC . An example of the resulting GACsignal for a specific v and varying PAC can be observed in figure 6.6.

27

0 1000 2000 3000 4000 5000 6000 70000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

GAC signal calculated with vaverage

= 5.5556[m/s] and Pac

= 6052[W]

Time [s]

GA

C s

ignal

GAC = 1 = use AC compressor

Figure 6.4: Generated GAC signal

By multiplying all different GAC signal result for all different v. A final resulting GAC signal is computed;this GAC signal is based on all possible v and PAC combinations. The final GAC signal will thus giveinformation where it will good to use the AC compressor independent of what velocity the driver keepsand what the actual power consumption of the AC compressor is.

28

0 1000 2000 3000 4000 5000 6000 70000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

GAC signal calculated with vaverage

= 5.5556[m/s] and Pac

= 9064[W]

Time [s]

GA

C s

ign

al

GAC = 1 = use AC compressor

Figure 6.5: Generated GAC signal with different values

6.1.4 AACC online and offline function with LA data

If the AACC function is implemented as discussed in section 4.5, i.e. by manipulation the Tref andthus affecting the ACC and the AC compressor. Then the AACC function has some control of Tinsidebus

because the function can determine when the AC compressor should be turned on. The AACC functionmust then keep Tinsidebus

inside the bus body between some predefined temperature margin Tinsidemargin .The AACC function is not activated if Tinsidebus

exceeds Tinsidemargin . The AACC function must keepin mind how many times the AC compressor have been turned on and off during a period of an hour inorder to not exceed the limitations of the AC compressor. The AACC function has a linear cost functionin order make decisions whether to affect the reference signal to the ACC, deepening on number of timesthe AC compressor has been turned on and off ACon/off and GAC signal value.

AACC = w1 ·GAC + w2 ·ACon/off (6.9)

Values for weights in Eq.6.9, can be found in appendix. If the result of Eq.6.9 is greater than 0, thenTref is manipulated in order to turn on the AC compressor.

29

0 1000 2000 3000 4000 5000 6000 70000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Resulting GAC signal calculated with vaverage

= 5.5556 [m/s] and Pac

= 6052−10570 [W]

Time [s]

GA

C s

ign

al

GAC = 1 = use AC compressor

Figure 6.6: Resulting GAC signal for v with different PAC

6.2 Online AACC function without LA data

The online AACC function without LA data is designed to be simple and cheap to implement in a realvehicle. The online function is built on the strategy to delay the AC compressors on and off cycles, ratherthan starting the AC function at optimal locations. The online function can also extend the AC com-pressor cycle if conditions are good. The advantages of delaying and extending the turn on and turn offof the AC compressor is that none of the AC compressor limitations have to be taken into considerationbefore delaying a turn on. Since the online function only can use information about events that haveoccurred and events that are occurring. It cannot be guaranteed that the online function will be able tosave fuel, as can be done with the online AACC function that utilise LA data.

The online function without LA data tries to delay the usage of the AC compressor at locations thatcan increase the fuel consumption and tries to extend the period when the AC unit is used if externalconditions are good. The online function will only delay the start of the AC compressor i.e. if the ACcompressor is already turned on the outcome of a potential delay will be none.

30

6.2.1 Input signals to the AACC function

The aim for the online AACC function without LA data is that it should be simple and cheap to imple-ment. Because of this criteria, the function have been designed to utilise existing HW and signals insidea bus. Since the idea is to delay the AC compressors on and off cycles at locations that can increase thefuel consumption and extend the cycle at locations where the AC compressor can compress refrigerantfor free, slope information is thought to be used. In simulations made on the online function without LAdata the slope information is simulated from GPS data.

Signals that were used by the online AACC function without LA data:

• Slope information.

• Tref .

• Tinsidebus

• Information if the AC compressor is turned on.

6.2.2 How the GAC signal is generated

The GAC signal is generated by observing the current slope of the vehicle. If the vehicle is driving on apositive slope i.e. up a hill, the GAC signal then gives a value indicating to the AACC function to delaya potential start up of the AC compressor, if the compressor is not started. If the vehicle is driving ona downhill the GAC signal will indicate to the AACC function to extend the AC compressors on time,if the AC compressor is already on. Below follows pseudo code for the how the GAC signal is generatedfor the online AACC function without LA data.

if Slope > 0 thenGAC = Delay start off the AC compressor.if Slope <= 0 then

GAC = Extend AC on time.end if

end if

6.2.3 AACC function without LA data

The online AACC function without LA data is in some way similar to the function that does utiliseLA data, since it also affects the AC compressors on and off cycles. The same discussion mentioned insection 6.1.4 applies to the online function as well. Since the online function also affect Tinsidebus

bymanipulation of Tref . The online AACC function also has to take into account whether Tinsidebus

liesinside some predefined Tmargin. The function does not have to take into account any limitations exceptthe Tmargin and does not need any cost function to make valid decisions. Bellow follow pseudo code forhow the AACC function utilise the GAC signal.

ifACon == true AND Slope < 0 thenExtend the time for when the AC compressors is turned on.

end ifif abs(Tinsidebus

− Tref ) < Tmargin AND Slope > 0 thenDelay start of AC compressor.

end if

31

Chapter 7

Results

In this chapter are the results of the AACC function that utilise LA data and the function without pre-sented. The fuel consumption have been calculated by simulating a recorded bus route with a Dymolamodel. The recorded bus route is almost the same as bus route 751 O in Sodertalje. The route wasrecorded by sampling bus signals throughout a bus run, to be able to simulate an authentic city bus routein Dymola. The route has been extended by simulating several runs of the recorded route. The final roadthat is used in simulations is composed of 2 · 2 of the original run, where 2 runs are a run that is madeback and forth. The altitude of the route can be observed in figure 7.1. The Dymola model is basedon an average bus model with 360 hp. The decrease in fuel consumption corresponds to how much theaverage bus models fuel consumption is decreased with the function. The different PAC is used in thesimulations are the worst case power consumptions for the AC compressor.

The AACC function is simulated by first calculating optimal locations in MATLAB and then calculatingwhere the AC compressor is turned on in Simulink by simulating the thermodynamic model of the busbody. The AC compressors on and off cycles are fed into the Dymola model as requested AC compressortorque that occurs at pre calculated locations on the route.

Some of the results that is representative for the entire simulation set have been chosen for this chapterto illustrate how the function affects fuel consumption.

The Poffset value in Eq.6.8 changes the trade off between having highly reliable suggestions or havinga lot of average suggestions. A small Poffset could both lead to increase or a decrease of the fuel con-sumption and vice verse with a large value on Poffset. The Poffset value is chosen to be 11000 W sincethe Poffset is large enough to deliver reliable suggestions for optimal on and off locations for the ACcompressor.

First are results from 3 different scenarios where the AACC function with LA data is used presented,then is the result from the AACC function that does not use LA data presented. A combination of thetwo AACC functions are presented at last.

Results showing Tinsidebushave been simulated with Simulink, and can therefore be compared to each

other since the MSE and MPE of the thermodynamic model only apply if simulated temperatures arecompared to measured data. Fuel save results have been simulated with Dymola, these results have aprecision of ±0.01%.

32

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.80

10

20

30

40

50

60

70

Time [h]

Altitu

de

[m

]

Figure 7.1: Altitude of the combined bus route

7.1 AACC online and offline with LA data

AACC function use the AC compressor less time than the ACC

One way of saving fuel is to use the AC compressor less time than normally would be done if the ACcompressor was controlled by the ACC. The AACC function may in some cases use the AC compressorless time compared to the ACC. In this cases should fuel consumption decrease but the temperatureclimate inside the bus should not be affected too much. In presented results is Tambient = Tref + 3◦C, theAC compressor is used for 2261 seconds when no AACC function is used and 2229 seconds independentof PAC . The AC compressor runtime is independent of PAC since the runtime of the AACC function isbased on the GAC signal, which is independent of PAC and v.

The fuel consumption was decreased by 0.08-0.53% in average over all possible PAC . The temperatureinside the bus body, when the AC compressor is used less by the AACC function can be observed infigure 7.2.

The fuel consumption is decreased for all temperatures which were to be expected. The AACC functionhas a lower mean temperature compared to when no AACC function is used. The mean temperatureinside the bus body is 20.2206◦C, compared to 20.2071◦C when the AACC function was used.

33

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.819.5

20

20.5

21

21.5

22

22.5

Time [h]

Tem

pera

ture

[ ° C

]

T

Insidebus

no AACC

TInside

bus

with AACC

Figure 7.2: Simulated temperature inside the bus body when less AC is used by the AACC function

AACC function use the AC compressor more time than the ACC

If the general principle that the AACC function is based upon did not work. It would be difficult touse the AC compressor more time with the AACC function compared to the ACC and still save fuel.The AACC function may use the AC compressor more, if there exist enough locations where the ACcompressor can be used without consuming any extra fuel. In the result presented, Tambient = Tref + 2.The AACC function use the AC compressor for 1622 seconds compared to 1333 seconds used by the ACC.The AACC function is able to use the compressor for 289 seconds more than the ACC and still save fuel.

The fuel consumption is decreased by −0.32% up to 1.17%. The temperature inside the bus body whenthe AACC function use the AC compressor more can be observed in figure 7.3.The increase in fuel consumption occurs at the highest difference between Tambient and Tref . In thistemperature region, most of the simulation time is used to decrease Tinsidebus

to a temperature wherethe AC compressor can be turned on and off, where the AACC function can be used. This could be areason why a fuel increase occurs. The AACC function has a lower mean temperature inside the busbody compared to the mean temperature when no AACC function is used. The mean temperature insidethe bus body is 20.1248◦C when no AACC function is used and 20.0504◦C when the AACC function isused.

34

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.819.4

19.6

19.8

20

20.2

20.4

20.6

20.8

21

21.2

21.4

Time [h]

Tem

pera

ture

[ ° C

]

T

Insidebus

no AACC

TInside

bus

with AACC

Figure 7.3: Simulated temperature inside the bus body when the AC compressor is used more by theAACC function compared to the ACC

AACC function use the AC compressor as much as the ACC

At some ambient temperatures will the AACC function not be able save any fuel, this often occurs if thedifference between the ambient temperature and reference temperature is large. This is because it takessome time to decrease the temperature inside the bus body to a temperature where it is possible for theAACC function to be used. In the results presented, Tambient = Tref + 6. The AC compressor was usedfor 3983 seconds by the AACC function and the ACC. The AACC function could not decrease the fuelconsumption.

The temperature inside the bus body can be observed in figure 7.4.

The mean temperature is the same, for both the AACC function and the ACC.

35

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.819

20

21

22

23

24

25

26

Time [h]

Tem

pera

ture

[ ° C

]

T

Insidebus

no AACC

TInside

bus

with AACC

Figure 7.4: Simulated temperature inside the bus body when AC compressor is used the same time

Summary of results when AACC function with LA data is used

The performance of the AACC function that utilise LA data is highly dependent on the Poffset. A lowvalue on Poffset will increase the amount of locations where the function suggests that the AC compressorcan be used without using any additional fuel, at the cost of reliability. The potential fuel save is alsodependent on PAC , which can be derived as the torque needed by the AC compressor. The presentedfuel save results for all PAC can be observed in table 7.1.

Table 7.1: Results for different PAC , when Cr = 0.006, vair = 0, Mv = 12350 kg and Poffset = 11000 W.Fuel saving results presented is shown in %.

Tambient = Tref+ 1 2 3 4 5 6 7 8PAC = 6052W 0.06 1 0.23 0.13 0.23 0 0.24 -0.40PAC = 8840W 0.11 1.51 0.58 0.37 0 0 0.14 -0.43PAC = 10570W 0.27 0.99 0.78 0.05 0 0 0.11 -0.11Average fuel save 0.15 1.17 0.53 0.18 0.08 0 0.16 -0.31ACon time ACC [s] 349 1333 2261 2806 3434 3983 4721 4997ACon time AACC [s] 547 1617 2227 2790 3382 3983 4624 5054

As can be observed in table 7.1 and in figure 7.5, the performance of the function varies for differentTambient as well as for different PAC . The average fuel saves over all temperatures and all PAC is 0.25%.

36

1 2 3 4 5 6 7 8−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

1.2

Tambient

= Tref

+ [° C]

Fu

el sa

ve

[%

]

Figure 7.5: Fuel save results from table 7.1 over all temperatures and all PAC

7.2 AACC online without LA data

The AACC function that does not utilise LA data is difficult to tune, to not increase fuel consumption.This can be accomplished with the AACC function that utilise LA data. At some difference betweenTambient and Tref , the AACC function without LA data decreases the fuel consumption and at otherdifferences the AACC function without LA data increases the fuel consumption.

Two scenarios will be presented, the first one is when the AACC function increase the fuel consumptionand the second is when the AACC function decrease the fuel consumption. When the fuel consumption isincreased by the AACC function, the mean temperature inside the bus body is closer to Tref compared tothe ACC in almost all simulations. When the fuel consumption is decreased by the AACC function, themean temperature inside the bus body is further from Tref compared to the ACC for almost all simula-tions. The results of the online AACC function that does not utilise LA data can be observed in table 7.2.

Table 7.2: Results for different PAC , when Cr = 0.01, vair = 0 and Mv = 12350 kg. Fuel saving resultspresented are shown in %.

Tambient = Tref+ 1 2 3 4 5 6 7 8PAC = 6052W 0.07 -0.12 -0.40 0.8 0.03 0.54 -0.40 0.63PAC = 8840W 0.07 -0.09 -0.49 0.93 -0.11 0.59 -0.33 0.73PAC = 10570W 0.05 -0.08 -0.46 0.94 0.05 0.49 -0.5 0.72Average fuel save 0.06 -0.1 -0.45 0.89 -0.01 0.54 -0.41 0.69ACon time ACC [s] 349 1333 2261 2806 3434 3983 4721 4997ACon time AACC [s] 348 1377 1983 2743 3403 3920 4462 5177

37

As can be observed in table 7.2 and in figure 7.6 The fuel save varies for different Tambient, the average fuelsave when using the online AACC function that does not utilise LA data is 0.15% for all temperatures.

1 2 3 4 5 6 7 8−0.5

0

0.5

1

Tambient

= Tref

+ [° C]

Fuel save [%

]

Figure 7.6: Fuel save results from table 7.2 over all Tambient and all PAC

38

7.3 Combination of AACC with LA information and AACCwithout LA

A combination of the AACC function that utilise LA information and the AACC function that does notutilise LA information was done. The new combined AACC function delays usage of the AC compressorif the AC compressor is unused and if the vehicle is positioned in positive slope. But the combined AACCfunction will also start the AC compressor if there exist enough free energy. The average fuel saves overall Tambient and all PAC is 0.37% for Poffset = 11000 W. The result for all Tambient = Tref + (1− 8)◦ Ccan be observed in figure 7.7.

1 2 3 4 5 6 7 8−0.5

0

0.5

1

Tambient

= Tref

+ [° C]

Fuel save [%

]

Figure 7.7: Result of the combined AACC function

The combined AACC function has not been tested as much as the two other AACC functions. But hasbeen added to the report to show, that by adjusting the AACC function. A larger fuel save is possibleto achieve with the AACC function.

39

Chapter 8

Sensitivity Analysis

In this chapter is the sensitivity of the AACC functions investigated. Parameters that can affect theperformance of the function have been altered, to investigate the impact that different parameters have iftheir values are changed. The AACC function that utilise LA data have several parameters that impact onwhere the optimal locations for using the AC compressor should be located, which can be seen in Eq.6.6.By altering parameters that affect the rolling resistance, air resistance and the mass of the vehicle, therobustness of the function can be seen. The effect of the power offset in Eq.6.8 is also investigated. TheAACC function that use LA data is highly dependent on GPS position in order to know where to startcompressing refrigerant. If the GPS position is inaccurate, the performance of the AACC function couldbe affected. The effect of inaccurate GPS position is thus also investigated. At last is another road testedto investigate that the AACC function is not adjusted to just work on the recorded road in Sodertalje

8.1 Rolling resistance

The performance of the AACC function when varying the rolling resistance have been performed withtwo different values of Cr in Eq.6.3. Cr has been chosen to either be 0.006 or 0.01. The greater value willincrease the rolling resistance for the vehicle in the AACC function that use LA data. Thus alter optimallocations to use the AC compressor for free. The average fuel saves have been calculated for the differentPAC i.e. 6052, 8840 and 10540 W. Calculations have been performed with Tambient = Tref + (1− 8)◦C.The results can be observed in table 8.1.

Table 8.1: Average fuel saving for different values on the parameter Cr. Results are shown in %.

Tambient = Tref+ 1 2 3 4 5 6 7 8Cr = 0.006 0.14 1.17 0.53 0.17 0.08 0 0.17 -0.32Cr = 0.01 0.14 1.17 0.53 0.17 0.02 0 0.16 -0.22Diff 0 0 0 0 400 0 0.6 45

The rolling resistance parameter Cr does not seem to affect the performance of the AACC function forTambient that is close to Tref . At Tambient = Tref + 7◦C, the AACC function give a fuel increase. Thisis because the function cannot alter the usage of the AC compressor but until the end of the simulation,because of time it takes to decrease Tinsidebus

to a temperature level where the AC compressor can beturned on and off more often.

40

8.2 Air resistance

The air resistance test have been performed by varying the air velocity in Eq.6.5 and maintaining a spe-cific ambient air temperature. The ambient air temperature have been chosen to be 22◦C i.e. Tref + 3◦C,at this specific temperature does the AACC function shown good results for vair = 0m/s. The meanvelocity that city buses have is between 20 − 50km/h i.e. 5.5 − 13.9m/s, vair is chosen to lie between±12m/s. The average fuel saves for the different PAC i.e. 6052, 8840 and 10540 W have been calculated.The calculated average fuel save for different vair is presented in figure 8.1.

−15 −10 −5 0 5 10 150.42

0.44

0.46

0.48

0.5

0.52

0.54

0.56

Fu

el sa

ve

[%

]

Air velocity [m/s]

Figure 8.1: Fuel saving dependent on the air velocity

As can be observed in figure 8.1, a negative air velocity decreases the fuel saving. This is because the airresistance force in Eq.6.5 becomes larger and thus decreasing the amount of locations where the Pnet willbe < 0 i.e. where the AC compressor can be used for free. The air velocity has a large impact on howthe AACC function will perform. A solution to this problem could be to extend the calculations of theGAC signal and make the GAC signal independent of vair.

41

8.3 Mass of the vehicle

The mass of the vehicle affects Eq.6.4, a larger Mv would lead to a larger negative force in negativeslopes. This should thus increase the amount of locations where the AC compressor could be used with-out consuming any fuel. A large Mv should lead to a larger decrease in fuel consumption compared toa smaller MV . These mass tests have been performed by altering Mv between 12350-16350, kg, whichshould correspond to an empty bus and a bus full of passengers. PAC has also been altered between 6052,8840 and 10540 W. The sensitivity tests have been performed with Tambient = Tref + 3◦C. The result canbe observed in figure 8.2.

1.2 1.25 1.3 1.35 1.4 1.45 1.5 1.55 1.6 1.65

x 104

0.05

0.1

0.15

0.2

0.25

0.3

Mass [kg]

Fu

el S

ave

[%

]

Figure 8.2: Fuel saves depending on the mass of the vehicle

An increase in the mass decrease the fuel saves, compared to the mass of an empty bus. The average fuelsave for the all the different masses is 0.19% which is less than the fuel save of an empty bus.

8.4 GPS inaccuracy

The GPS position is crucial for the AACC function that utilise LA data. The GPS position of the vehicleis used for determining when the AC compressor should be turned on based on optimal points calculatedby the AACC function. If the GPS position is inaccurate this could result that the AC compressor isturned on at a position where the AC compressor could use more fuel than it normally should have done.In this test have the ambient air temperature been set to Tambient = Tref + 3◦C. The GPS inaccuracy isvaried between ±0− 57m. The average fuel saves for the different PAC i.e. 6052, 8840 and 10540 W havebeen calculated. The result can be observed in figure 8.3.

As can be observed in figure 8.3, the inaccuracy of the GPS position affect the performance of the AACCfunction. But the performance of the AACC function is not necessary decreased if the inaccuracy of theGPS is increased.

42

0 10 20 30 40 50 600.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

GPS inaccuracy [m]

Fuel save [%

]

Figure 8.3: Fuel saving dependent on the GPS inaccuracy.

8.5 Poffset

The Poffset that exist in Eq.6.8, exist to prevent the function from increasing the fuel consumption byincreasing the reliability of the function. This parameter could be seen as a trade off parameter. If thedesigner of the function desires a large fuel saving and is willing to compromise on reliability of the functionthen a small value on Poffset could be chosen. If the designer of the function desires the opposite situation,a large value on Poffset could be chosen. The sensitivity analysis have been performed by calculatingthe average fuel save for Poffset = 0 − 17000W over all PAC and over all Tambient = Tref + (1 − 8)◦C.Thus resulting in an average value fuel save for each Poffset. The result for all Poffset, can be observedin figure 8.4 .As can be observed in figure 8.4, a larger Poffset leads to a small average fuel save over all Tambient andPAC . At some Tambient does a smaller Poffset lead to a greater save, which can be seen in table 8.2. Theaverage fuel save is greater for the Poffset = 10570 W compared to Poffset = 0 W for this specific PAC .At Poffset = 6000 W a error has occurred when simulating, leading to incorrect fuel save value.

Table 8.2: Average fuel saving for different values on the parameter Poffset. Results are shown in %.

Tambient = Tref+ 1 2 3 4 5 6 7 8PAC = 10570W,Poffset = 0W -0.75 0.36 0.74 0.06 0.23 0 -0.03 -0.04PAC = 10570W,Poffset = 17000W 0.09 0.87 0.64 0.01 0 0 0.06 0

8.6 Effect of best case PAC

The power consumption used in the report is based upon the worst case of power consumption from theAC compressor. The power consumption is used inside Eq.6.8. If the power consumption from the bestcase is used inside Eq.6.8 the AACC function should act as if the Poffset were decreased. The fuel savingswill also be affected since a lesser torque is used by the AC compressor, which should lead to a decreasethe fuel saving. The result from using best case of the power consumption on the PAC can be observedin table 8.3.

43

0 2000 4000 6000 8000 10000 12000 14000 16000 18000−1.5

−1

−0.5

0

0.5

1

1.5

POffset

[W]

Fu

el sa

ve

[%

]

Figure 8.4: Fuel savings dependent on the Poffset value

Table 8.3: Average fuel savings for best case PAC , i.e. 3076, 4493 and 5374 W. Compared to worst casePAC , i.e. 6052, 8840 and 10570 W. Poffset = 11000W. Results are shown in %.

Tambient = Tref+ 1 2 3 4 5 6 7 8PAC best case 0.05 0.62 0.19 -0.01 0.15 0 0.08 -0.11PAC worst case 0.14 1.17 0.53 0.17 0.08 0 0.17 -0.32

As can be observed in table 8.3, the fuel saving is decreased when the best case power consumption isused. A decreased in PAC effects the AACC function in the same way as a decrease in Poffset and therequested AC compressor torque is also decreased. It is difficult to say what leads to a decrease in fuelsavings, it could for example be that the requested AC torque is decreased. The average fuel saves for allPAC over all Tambient with best case PAC is 0.12% compared to 0.25% for worst case PAC .

44

8.7 Oslo to Haugesund

The AACC function was tested on a different road to confirm, that the function is not adjusted to onlywork on the recorded bus route in Sodertalje. Simulations have been made on another recorded roadfrom Oslo to Haugesund in Norway. Due to lack of recorded city routes, this recorded road have beenchosen since it has a topology that is well suited for the AACC function. The bus travels mostly on ahighways, which is not the type of environment that the AACC function, have been designed to work in.The average fuel saves for all PAC over all Tambient with 3 different Poffset can be observed in table 8.4.

Table 8.4: Average fuel saves for different Poffset on the recorded road from Oslo to Haugesund. Resultsare shown in %.

Tambient = Tref+ 1 2 3 4 5 6 7 8PAC 8000 W 0.48 0.18 -0.23 -0.24 0.07 -0.50 -0.02 -0.09PAC 11000 W 0.33 0.05 -0.05 0.08 -0.19 0 0.20 -0.03PAC 12000 W 0.14 0.02 0.02 0 -0.25 0 0.20 -0.04

The average fuel savesx for Poffset = 8000, 12000 W is 0.01%. For Poffset = 11000 W is the averagefuel save 0.05%. The simulations have been done with the same tolerance but with much less data pointscompared to simulations done on the recorded road in Sodertalje, since there does not exist enoughmemory. The AACC function does as good performance on the road from Oslo to Haugesund as therecorded road in Sodertalje. The reason could be that the bus travel with much higher velocity on thehighway from Oslo to Haugesund compared to the when driving on the recorded bus run.

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Chapter 9

Conclusion

9.1 Discussion

This master thesis project have been focused on developing a guiding signal that if used by the controllerof AC system in buses, generates a decrease in fuel consumption. A signal that guides the AC controllerhave been developed and implemented in Simulink and Dymola through the AACC algorithm and theAACC function and have resulted in a decrease in fuel consumption, while maintaining the temperatureclimate inside the bus body. It has been proven throughout the report that it is possible to decreasefuel consumption by controlling the AC compressor with more input signals than the desired Tref . Theproposed GAC signal with proposed implementation of the AACC function is robust to alternations inparameters.

While modelling the ACC and the AACC function, an aim have been to fulfil requirements listed insection 3.5. Some requirements listed in section 3.5 can be implemented, while others are unnecessary toimplement. For example that the battery voltage must exceed 21 V.

Fuel savings have been calculated with the Dymola model and a recorded road, the road is based on arecorded bus route in Sodertalje. The recorded road was used instead of SORT cycles, since availableSORT cycles wasn’t optimal for testing the performance of the AACC function. The simulated road hasbeen extended to be 4 times longer than the recorded, which can be observed in figure 7.1 if looked uponwith sharp eyes.

If Tambient differs much from Tref , it takes some time until the ACC have decreased Tinsidebusto a level

where the AC compressor does not have to be on all the time. This is a reason why the AACC functionsometimes increase the fuel consumption when Tambient is much larger than Tref . The simulations couldhave been extended even further, which could have lead to more fuel being saved with the AACC functionat high Tambient. But that would have been the same as observing how the function behaved when thedifference between Tambient and Tref was smaller.

A unified fuel consumption is difficult to calculate since the fuel consumption depends on vair, Mv andhow Poffset is chosen. Mv will vary since it depends on the amount of passengers inside the bus. The

passengers will also affect Qextra and since extra mass is added, the time constant τ in the thermodynamicmodel will increase. The thermodynamic model seen in Eq.5.7 could be improved by adding a Qpassanger

but extra data would be needed to adjust τ in the model.

A city bus travels 120 000 km in average each year and have a fuel consumption of approximately 0.4-0.6l/km. The total fuel consumption for a year is thus 48000-72000 l/year. With a diesel price of 13.85kr/l1, the total fuel for a city bus is 664800 - 997200 kr/year. With a AACC function that utilise LA

1Best Swedish price on diesel on Price 2012-08-20

46

data the maximum average fuel saves is 0.29% with Poffset = 12000W. This could decrease the total fuelcost for a bus with 1900-2870 kr each year. The AACC function that does not utilise LA data have aaverage fuel save of 0.15%. This could lead to a decrease the fuel cost with 1000 - 1500 kr each year. Thedecrease in fuel cost is if the AACC function is used is highly dependent on the ambient temperature ofcountry where the city bus operates.The combination of the two AACC functions shows that the performance of the AACC function can beincreased even further, by altering how the AACC function should work.

9.2 Final conclusions

The AACC function decreases fuel consumption, but I believe that the AACC function that does notutilise any LA data is the only one that in a close future could be implemented for a reasonable cost.Since the implementation could be made with just a SW update and with available HW.

9.3 Future work

The performance of the AACC function is dependent on Poffset and on vair. If the AACC function thatutilise LA data is implemented the GAC signal should also test different vair, and make the GAC signalindependent of vair. The performance of the AACC function could probably be improved by designingthe AACC function that utilise LA information as a gain scheduled controller.

The combined controller shows that further improvements can be done by altering the AACC algorithmand the GAC signal.

The proposed way of implementing the AACC function is to manipulate one of the reference signals to theACC. This could lead to that the system start to oscillate, which would need to be further investigatedbefore implantation.

47

Bibliography

[1] Y. Farzaneh and A. A. Tootoonchi. Controlling automobile thermal comfort using optimized fuzzycontroller. Applied Thermal Engineering, 28(14-15):1906–1917, 2008. Cited By (since 1996): 13.

[2] H. Khayyam, A. Z. Kouzani, E. J. Hu, and S. Nahavandi. Coordinated energy management of vehicleair conditioning system. Applied Thermal Engineering, 31(5):750–764, 2011. Cited By (since 1996):2.

[3] Hamid Khayyam, Saeid Nahavandi, Eric Hu, Abbas Kouzani, Ashley Chonka, Jemal Abawajy, Vin-cenzo Marano, and Sam Davis. Intelligent energy management control of vehicle air conditioning vialook-ahead system. Applied Thermal Engineering, 31(16):3147 – 3160, 2011.

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[7] SMHI. Sun power june:http://www.smhi.se/klimatdata/meteorologi/stralning/1.3044, 08 2012.

[8] e Societe de transport de Montreal Societe de transport de l’Outaouais. Hybrid technology. Technicalreport, http://www.stm.info/english/en-bref/a-raptechhybride.pdf, 2009.

[9] Spheros. Refrigeration/air-conditioning traning manual, 07 2006.

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Appendix A

Appendix

A.1 Parameter values

Symbol Valuemair = adjusted to 2334 kg

cpair = adjusted to 1810 JK·kg

Abus = 159 m2

τ = 4377 s

U = 6.0668 Wm2·K

mairfan= 1.24,1.03 and 0.41 kg/s

4Tinevap= adjusted to 6.73 ◦C

Aroof = 35 m2

Psun = 180 W/m2

Rroof = 0.2Aglas = 32 m2

Tglas = 0.25Mv = 12350 kgg = 9.82 m/s2

Cd = 0.6Av = 6.25 m2

ρair = 1.3 kg/m3

w1 = 1w2 = -0.15

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