DETERMINATION OF MAXIMUM POSSIBLE FUEL 
ECONOMY OF HEV FOR KNOWN DRIVE CYCLE: 
GA BASED APPROACH 
A dissertation submitted to the 
Department of Electrical Engineering, University of Moratuwa 
In partial fulfillment of the requirements for the 
Degree of Master of Science 
By 
RAN DOM B AGE SUDATH WIMALENDRA 
Supervised by Dr. Lanka Udawatta 
LIBRARY UNIVERSITY OF M0HAT8WA. SRI LANK* 
MORATUWA 
Department of Electrical Engineering , -j x S q C ] 
University of Moratuwa 
£ 2 L 1 . 5 ( 0 4 ^ 5 
University of Moratuwa 
"TV* 
92962 
January 2009 
a 
n o r i 9 o i-L ^ O U 
DECLARATION 
I certify that this dissertation does not incorporate without acknowledgement any material 
previously submitted for a degree or diploma in any University to the best of my 
knowledge and believe it does not contain any material previously published, written or 
orally communicated by another person or myself except where due reference is made in 
the text. I also hereby give consent for my dissertation if accepted, to be made available 
for photocopying and for interlibrary loans, and for the title and summary to be made 
available to outside organizations. 
R.S.Wimalendra 
Date : 31/01/2009 
We/I endorse the declaration by the candidate. 
Dr. Lanka Udawatta 
Supervisor 
Date : 3 I - OI 7- o o 
ii 
CONTENTS 
PAGE 
DECLARATION ii 
• 
CONTENTS iii 
ABSTRACT vi 
ACKNOWLEDGEMENT vii 
LIST OF FIGURES viii 
LIST OF TABLES ix 
CHAPTER - 1 
1 Introduction 01 
1.1 HEV Evolution 01 
1.2 Motivation for HEV 02 
1.3 Literature Review 02 
1.4 Objectives 04 
CHAPTER-2 
2 Hybrid Electric Vehicles 06 
2.1 The need of HEV 06 
2.1.1 Environmental Concern 07 
2.1.2 Energy Consumption 07 
2.2. Clarification of HEV 08 
2.2.1. Series Hybrid Vehicles 08 
2.2.2 Parallel Hybrid vehicles 09 
2.2.3 Series - Parellel Hybrid vehicle 10 
2.3 Characteristics of Hybrid Systems 11 
2.4. HEV components 12 
2.4.1 Electric Motors 12 
2.4.2 Battery 13 
2.4.3 Transmission 14 
2.5 Energy Management Systems of HEVs 14 
iii 
C H A P T E R - 3 
3 Modeling and Simulation of HEV 16 
3.1 Modeling of Drivetrain 16 
3.2. Modeling of Engine 20 
3.3 Modeling of Motor 21 
3.4 Modeling of Energy Storage System 21 
3.5 Specifications of Selected Vehicle 22 
3.6 Advanced Vehicle Simulation Tools 26 
3.6.1 ADVISOR 27 
3.6.2 Dymola 27 
3.6.3 SAT 28 
CHAPTER-4 
4 Drive Cycles 29 
4.1 Transient Drive Cycle 30 
4.2 Model Drive Cycle 30 
4.3 Drive Cycles Used in The Study 30 
4.3.1 NEDC 30 
4.3.2 CDC 31 
CHAPTER-5 
5 Overview of Genetic Algorithm 33 
5.1 Introduction & Background 33 
5.2 Overview 34 
5.3 Coding 35 
5.4 Genetic Operators 36 
5.4.1 Selection 36 
5.4.2 Crossover 37 
5.4.3 Mutation 38 
iv 
C H A P T E R - 3 
6 Optimization using GA 40 
6.1 Power Split in HEV 40 
6.2 Optimization problem formulation 41 
6.2.1 Domain and Constrain 41 
6.2.2 Population and Individuals 43 
6.2.3 Chromosome 43 
6.2.4 Fitness Function 45 
CHAPTER-7 
7 Results & Analysis 49 
CHAPTER-8 
8 Conclusion and Remarks 57 
References 59 
Appendix A 63 
v 
ABSTRACT 
Hybrid electric vehicles (HEVs) have great potential as new alternative means of 
transportation. The specific benefits of HEVs, compared to conventional vehicles, include 
improved fuel economy and reduced emissions. Hybrid systems using a combination of 
an internal combustion engine and Electric motor (EM) have the potential of improving 
fuel economy by operating Internal Combustion Engine (ICE) in the optimum operating 
range and by making use of regenerative braking during deceleration This paper 
described a methodological approach to find out the maximum fuel economy that can be 
achieved by a hybrid vehicle with parallel configuration for a known drive cycle. A 
backward looking hybrid vehicle model is used for computation of fuel consumption. The 
optimization process represents a constrained nonlinear and time-varying problem that is 
not easily solved. Here GA approach was used to find out optimum power split between 
two power sources over a driving cycles that make maximum possible overall fuel 
economy for the given drive cycle by the vehicle. In this approach using parallel Hybrid 
Electric Vehicle (HEV) configuration optimization problem is formulated so as to 
minimize the overall fuel consumption. The whole set of Electric Motor power 
contribution along the drive cycle is then coded as the chromosomes. Variables are 
defined to find out optimum power contribution from EM and ICE. The objective 
function is defined to minimize weighted sum of the fuel economy and to keep the battery 
SOC within the desired range throughout the drive cycle. These results represent the 
maximum fuel economy any power management system of a Hybrid Electric Vehicle 
with the selected HEV configuration can ever achieve and does allow a benchmark to be 
set against which the fuel economy is measured. It is obvious that fuel economy varies 
with the driving cycle and hence the result obtained from this study is valid only for the 
selected drive cycle. The optimum fuel economy for the selected drive cycle is compared 
with that of conventional vehicle 
vi 
ACKNOWLEDGEMENT 
I would like to first acknowledge and express my sincere thank to my supervisor Dr 
Lanka Udawatta for the opportunity that he gave me to work on this highly promising and 
exciting research area. I am also grateful to Prof. Saman Halgamuge and Sunil Adikari, 
School of Engineering, University of Melbourne, Australia for providing the necessary 
research materials and information of HEVs for this study. 
I would like to thank all reviewers who have attended in the progress review presentations 
for their precious comments and guidance. 
Without the help and support given by my colleagues R Karunaratna and C.P.M. 
Edirisingha, who have also done researches on HEVs, I would not have been able to 
complete this research project in time and I am very thankful to them for their support. 
Finally, a special thank goes to my wife, two sons, the daughter and my mother for their 
moral support during the busy period. 
R.S.Wimalendra 
Department of Electrical Engineering, 
University of Moratuwa, 
31 s t January 2009. 
vii 
LIST OF FIGURES 
Figure Description Page 
Figure 2.1 Globe Oil Consumption Perspectives 0.7 
Figure 2.1: Series Hybrid Electric Vehicle 09 
Figure 2.2: Parallel Hybrid Electric Vehicle 10 
Figure 2.3: Series-Parallel Hybrid Electric Vehicle 11 
Figure 3.1 HEV Model Schematic Diagram 17 
Figure 3.2 Free body diagram of a vehicle 18 
Figure 3.3: Sample Drive Cycle 19 
Figure 3.4 Engine Model Schematic Diagram 20 
Figure 3.5 Motor Model Power Flow 21 
Figure 3.6 Energy Storage System Model 22 
Figure 3.7 Fuel consumption map of the ICE of tested HEV 24 
Figure 3.8 Engine fuel efficiency map 25 
Figure 3.9 Engine fuel efficiency contours 25 
Figure 3.10 Motor Efficiency Map 26 
Figure 4.1: New European Drive Cycle 31 
Figure 4.2: Colombo Drive Cycle 32 
Figure 5.1: Segment decoding 3 5 
Figure 5.2: Proportionate Selection Schemes 36 
Figure 5.3: One-point crossover 37 
Figure 5.4: Multi point Crossover 37 
Figure 5.5: Mutation Operator • 38 
Figure 6.1: Block Diagram of Energy Flow 40 
Figure 6.2: Block diagram of the parallel hybrid vehicle 41 
Figure 6.3: Example of EM contribution (Top), Chromosome (Bottom) 44 
Figure 6.4: Flow chart of calculation of Objective function 47 
Figure 6.4: Flow chart of GA 48 
Figure 6.5: Evolutionary Algorithm 48 
Figure 7.1: History of genetic algorithm optimization process for NEDC 51 
Figure 7.2: History of genetic algorithm optimization process for CDC 51 
Figure 7.3: Power demand to achieve the NEDC speed profile 52 
Figure 7.4: Power demand to achieve CDC 53 
viii 
Figure 7.5: Contribution from EM over NEDC 54 
Figure 7.6: Contribution from EM over CDC 54 
Figure 7.7: Battery SOC variation over NEDC 55 
Figure 7.8: Battery SOC variation over CDC 56 
LIST OF TABLES 
Table Description Page 
Table 2.1: Parameters of HEV Batteries 13 
Table 3.1: Vehicle model specifications 23 
Table 3.2: Engine Torque map 24 
Table 4.1: CDC parameters 32 
Table 6.1: Upper and Lower limits for decision variables 45 
Table 7.1: Fuel Economies for conventional and optimized HEV 50 
ix