FUEL ECONOMY OF A HYBRID ELECTRIC VEHICLE WITH SHORT TERM VELOCITY PREDICTIONS : 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 E.M.C.P. EDIRISINGHE LIBRARY U: • :BSiTY OF MORAT&vUfc tRI LANKA MORATUWA Supervised by : Dr. Lanka Udawatta Department of Electrical Engineering University of Moratuwa, Sri Lanka -Cs _ / / oq T U January 2009 University of Moratuwa 7 / 2 / 92961 9 2 C G 1 DECLARATION The work submitted in this dissertation is the result of my own investigation, except where otherwise stated. It has not already been accepted for any degree, and is also not being concurrently submitted for any other degree. Date : 30th January 2009. I endorse the declaration by the candidate. Dr. Lanka Udawatta. CONTENTS Declaration Abstract Dedication Acknowledgement List of Figures List of Tables List of Abbreviations 1. Introduction 1.1 Literature Surveyor of Previous Work 1.2 Objectives of the Research 1.3 Hybrid Electric Vehicles 1.4 Intelligent Vehicles 1.5 ADVISOR Software 2. HEV Classifications 2.1 Parallel HEVs 2.2 Series HEVs 2.3 Parallel - Series ( Dual) HEVs 2.4 Basic HEV Components 2.4.1 Electric Motor 2.4.2 Energy Storage System 2.4.3 Power Splitter 2.5 Characteristics of Hybrid Systems 2.6 Advantages & Disadvantages of HEVs 3. Drive Cycles 3.1 New European Drive Cycle ( NEDC ) 3.2 Colombo Drive Cycle ( CDC ) 4. HEV Model used for Simulations 4.1 Specifications of the Selected HEV 4.2 Calculation of required power 4.3 Engine Model 4.3.1 Operating Regions 4.4 Battery Model 5. Genetic Algorithms 2S 5.1 Basics of GA 28 5.1.1 Individuals 30 5.1.2 Population 31 5.1.3 Obj ective & Fitness Functions 31 5.1.4 Selection 31 5.1.4.1 Roulette Wheel Selection 31 5.1,4.2Stochastic Universal Sampling 32 5.1.5 Crossover 33 5.1.6 Mutation 35 5.1.7 Termination of the GA 36 5.2 Inherent features of GA 36 6. GA Based Approach 6.1 Problem mapped in GA Domain 6.1.1 Obj ective Function 6.1.2 Chromosome 6.2 GA Parameters 6.3 Optimization Process 7. Results and Analysis 4 2 7.1 Results for NEDC 4 2 7.1.1 Velocity profile and relevant power demand 42 7.1.2 Operating points of ICE 43 7.1.3 EM Contribution 45 7.1.4 SOC Variation 46 7.2 Results for CDC 7.2.1 Velocity profile and relevant power 7.2.2 Operating points of ICE 7.2.3 EM Contribution 7.2.4 SOC Variation 7.3 Analysis of Results 8. Conclusions 8.1 Conclusions, Remarks and Discussion 8.2 Recommendations for Future Research References Appendix A Appendix B Published Research Papers Codlings of MATLAB Programs Abstract The increasing of fuel price and environmental concerns, researches were pushed to think about more fuel-efficient and less emission vehicles. As a result of this great enthusiasm, researchers were able to introduce Hybrid technology to the field of automobile. In hybrid electric power trains, an internal combustion engine (ICE) together with an electric motor (EM) is used as two energy sources. Use of an electrical motor in place of the ICE during different stages of driving results a definite saving in fuel usage. Researches did not satisfy with this saving and these endless efforts gave the birth to the concept of intelligent vehicles or telematics - enabled Hybrid Electric Vehicles (HEV). These vehicles may use a sensor network to obtain the information about the degree of traffic flow in the environment which they are operating, and subsequently adjust their drive cycle to get the better improvement in fuel economy based on these information. In this thesis, a conventional vehicle and a HEV with different amount of traffic flow information are compared in terms of fuel economy over two different drive cycles. First simulation results for conventional vehicle was compared with simulation results for an HEV without traffic flow information and HEV with available of traffic flow information for 4 seconds & 8 seconds ahead of current time, over New European Drive Cycle (NEDC). Thus estimated the same for a Sri Lankan Drive Cycle named Colombo Drive Cycle (CDC). Results show that with increase of traffic flow information, the fuel economy of the HEV is increased. Finally two drive cycles were compared and the comparison shows that the improvement in fuel saving is very significant for CDC. Acknowledgement First I would like to thank Dr. Lanka Udawatta for guiding me successfully in completing this research within the time frame. As the research supervisor, he directed me to find all necessary literature and to do the research work up to the standards. I would like to extend my heart gratitude to Prof. Saman Halgamuge and Mr.Sunil Adikari, School of Engineering, University of Melbourne, Australia for providing the necessary research materials and information of HEVs for this study. I should convey my gratitude to all the lectures of Electrical & Mechanical Engineering Departments of University of Moratuwa, who participated for the progress review presentations. Their valuable and fruitful comments helped me a lot to achieve the goals of this work. Then I would like to convey my sincere thanks to my three colleagues Miss. Thusharie Mundigala, Mr. Sudath Wimalendra & Mr. Sudarshana Karunarathne. They encouraged me in making this task a success from the very beginning second to the very last moment. My thanks are also due to Mrs. Hiranya Walpola for her kind support and patient in proof reading. Finally, I would like to thank everyone who supported me even in a single word to complete this research work successfully. v i i List of Figures Figure 2.1 : Block Diagram of Pre - Transmission Parallel HEV 8 Figure 2.2 : Block Diagram of Post - Transmission Parallel HEV 8 Figure 2.3 : Block Diagram of all wheel drive Parallel HEV 8 Figure 2.4 : Block Diagram Series HEV 10 Figure 3.1 : NEDC 16 Figure 3.2 : CDC 17 Figure 4.1 : Velocity Input 19 Figure 4.2 : HEV on the Road 20 Figure 4.3 : Engine Fuel Rate Map 23 Figure 4.4 : Engine Efficiency Map 24 Figure 4.5 : Engine Efficiency Contours 24 Figure 4.6 : Shape of the efficiency variation curve with torque for any speed 25 Figure 4.7 : ICE operated in Region 3 26 Figure 4.8 : ICE operated in Region 2 26 Figure 5.1 : Evolutionary algorithm mechanism 30 Figure 5.2 : Roulette Wheel Selection 32 Figure 5.3 : Stochastic Universal Sampling 33 Figure 5.4 : One-point crossover 34 Figure 5.5 : Multi-point crossover, m = 4 34 Figure 5.6 : Mutation Operator 35 Figure 6.1 : n second Time Slot 37 Figure 6.2 : Chromosome 38 Figure 6.3 : Optimized EM Power contribution for n second Time Slot 39 Figure 6.4 : Optimization Process 40 Figure 7.1 : NEDC 42 Figure 7.2 : Power demand for NEDC 42 Figure 7.3 : ICE Operating points for Conventional Vehicle - NEDC 43 Figure 7.4 : ICE Operating points for HEV Without Predictions - NEDC 43 v i i i Figure 7.5 : ICE Operating points for HEV With 4 Seconds Predictions - NEDC 44 Figure 7.6 : ICE Operating points for HEV With 8 Seconds Predictions - NEDC 44 Figure 7.7 : EM Contribution for HEV Without Predictions - NEDC 45 Figure 7.8 : EM Contribution for HEV With 4 Seconds Predictions - NEDC 45 Figure 7.9 : EM Contribution for HEV With 8 Seconds Predictions - NEDC 46 Figure 7.10 : SOC Variation for HEV Without Predictions - NEDC 46 Figure 7.11 : SOC Variation for HEV With 4 Seconds Predictions - NEDC 47 Figure 7.12: SOC Variation for HEV With 8 Seconds Predictions - NEDC 47 Figure 7.13 : CDC 48 Figure 7.14: Power demand for CDC 48 Figure 7.15 : ICE Operating points for Conventional Vehicle - CDC 49 Figure 7.16: ICE Operating points for HEV Without Predictions - CDC 49 Figure 7.17 : ICE Operating points for HEV With 4 Seconds Predictions - CDC 50 Figure 7.18: ICE Operating points for HEV With 8 Seconds Predictions - CDC 50 Figure 7.19 : EM Contribution for HEV Without Predictions - CDC 51 Figure 7.20 : EM Contribution for HEV With 4 Seconds Predictions - CDC 51 Figure 7.21 : EM Contribution for HEV With 8 Seconds Predictions - CDC 52 Figure 7.22 : SOC Variation for HEV Without Predictions - CDC 52 Figure 7.23 : SOC Variation for HEV With 4 Seconds Predictions - CDC 53 Figure 7.4 : SOC Variation for HEV With 8 Seconds Predictions - CDC 53 Figure 7.25 : Comparison of Fuel Usage 55 Figure 7.26 : Comparison of ICE Operating points for NEDC 56 Figure 7.27 : Comparison of ICE Operating points for NEDC with CDC 57 Figure 7.28 : Comparison of EM Power Contributions of NEDC 58 Figure 7.29 : Comparison of SOC Variation for NEDC with CDC 59 ix List of Tables Table 2.1 : Comparison of Hybrid Systems 10 Table 2.2 : Comparison of Batteries 12 Table 4.1 : Specifications of the selected HEV 18 Table 7.1 : Comparison of Fuel Usage 53 x List of Abbreviations ADVISOR ADvanced Vehicle SimulatOR CDC Colombo Drive Cycle DOE Department of Energy - United States of America EM Electric Motor ESS Energy Storage System FC Fuel Cells GA Genetic Algorithm GHG Greenhouse Gas HEV Hybrid Electric Vehicle ICE Internal Combustion Engine IEEE Institute of Electronic and Electrical Engineers IGBT Insulated Gate Bipolar Transistors NEDC New European Drive Cycle NREL National Renewable Energy Laboratory SCRAM Signal Coordination in Regional Areas of Melbourne SOC State of Charge ( of the battery ) SUS Stochastic Universal Sampling UN United Nations w.r.t With Respect To