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Modeling and Control of a Hybrid Electric Drivetrain for Optimum Fuel Economy, Performance and Driveability

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Modeling and Control of a Hybrid Electric Drivetrain for Optimum Fuel Economy, Performance and Driveability MODELING AND CONTROL OF A HYBRID ELECTRIC DRIVETRAIN FOR OPTIMUM FUEL ECONOMY, PERFORMANCE AND DRIVEABILITY DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Xi Wei, M.S. * * * * * The Ohio State University 2004 Dissertation Committee: Approved by Professor Giorgio Rizzoni, Adviser _________________________________ Professor Vadim Utkin, Co-Adviser _________________________________ Advisers Professor Stephen Yurkovich Graduate Program in Mechanical Engineering Copyright by Xi Wei 2004 ABSTRACT Automotive manufacturers have been striving for decades to produce vehicles which satisfy customers’ requirements at minimum cost. Many of their concerns are on fuel economy, road performance and driveability. Improving fuel economy is both a political concern of alleviating dependency on foreign fuel and a customer preference of reducing vehicle operating cost. Consumers also expect vehicles to provide satisfactory performance with desirable driving comfort. Improvements on all these aspects may contribute to lower emissions as well if the vehicle is designed and controlled properly. A hybrid electric vehicle (HEV) is one of the most promising alternatives to a conventional engine-powered vehicle which satisfies increasing customer requirements mentioned above. However, how much the hybrid vehicle is better than the conventional one depends heavily on its control strategy. The involvement of the electric machine for HEV traction offers the possibility to provide the total tractive force in different ways. Investigations indicate that how to allocate the total tractive force between the engine and the electric machine has significant influences on vehicle fuel economy, performance and driveability. Therefore, designing an optimal control strategy which considers all three criteria is of great interest. Model based control design requires control oriented models and the complexity of these models are determined by their applications. The control oriented models need to be sufficient to evaluate the control criteria and easy enough for control strategy development. Vehicle fuel economy and performance are related with power allocation in the steady state while human perceptible driveability issues are in the frequency range of a few hertz. Since the control strategy is developed in two steps (finding the solution for the best fuel economy first and then taking driveability into account), two models, i.e., ii the quasi-static model and the low-frequency dynamic model are built for each step in the control design. Simulation results demonstrate that these two models are effective to capture the main behaviors of the vehicle and to evaluate fuel economy, performance and driveability respectively. Defining objective metrics for vehicle fuel economy, performance and driveability is also very important. Evaluations in both simulations and real vehicles require objective and quantitative measures. Subjective and descriptive metrics cannot be easily implemented in simulations and evaluations vary with changing time or evaluators. Vehicle fuel economy is estimated under various city, highway and some other user- defined driving schedules. Performance criteria consist of acceleration/deceleration performance, gradeability and towing capability. Driveability measures deal with pedal responsiveness, operating smoothness and driving comfort, which include interior noise level, jerk, tip-in/tip-out response, Maximum Transient Vibration Value (MTVV), acceleration Root Mean Square (RMS) value and Vibration Dose Value (VDV). Numerical references and some interpretations for these metrics are also presented in this dissertation. The optimal control solution is then found hierarchically with the help of Pontryagin’s minimum principle based on models, i.e., solving vehicle fuel economy optimization first and then taking driveability into consideration. Meeting power requests always has the highest priority which guarantees good vehicle performance. Fuel economy optimization contains three steps: finding the optimal solution for known constant power requests, for known time-varying power requests and for unknown time- varying power requests with short-term predictions. An innovative interpretation of the minimum principle is applied when minimizing fuel consumption for the vehicle with constant battery parameters and fixed CVT ratio under constant power requests. This so- called sliding optimal control which switches between two control values has been theoretically proven to be the optimal solution. The control strategy developed with the minimum principle is compared with a simple heuristic one and simulation results demonstrate an improvement on vehicle fuel economy. iii Dedicated to my parents and my husband iv ACKNOWLEDGMENTS I would like to express my sincere gratitude to my adviser Dr. Giorgio Rizzoni for leading me into the automotive field. I have been impressed by his beautiful sharp mind. His guidance and experience as well as his trust have provided a continuous inspiration for this dissertation. It is my great pleasure to have Dr. Vadim Utkin to be my co-adviser. His invaluable guidance and unique way of thinking has opened my eyes to a new world. I also enjoyed all the discussions and chatting between us. I would like to thank Dr. Lino Guzzella for all his advices and nice discussions. I am also very thankful to Dr. Yurkovich for reviewing this work. I would like to express my appreciation to my best friends Yubing Xie and Yanfei Liu for sharing my laughs and tears. Being friends of you is one of the most beautiful parts of my life in America. I also acknowledge all the fellow students at the Center for Automotive Research and Intelligent Transportation for their assistance in numerous problems. Finally I am very indebted to my parents and Feng for their constant support and encouragement. It is impossible to go through all the difficulties without your love. v VITA March 16, 1973 …………………………... Born – Shanghai, China. July 1995 …………………………………. B.S. Mechanical Engineering and Electrical Engineering, Dalian University of Technology, Dalian, China. July 1998 …………………………………. M.S. Mechanical Engineering, Tsinghua University, Beijing, China. 1998 – present ……………………………. Graduate Teaching and Research Associate, The Ohio State University, Columbus, OH. PUBLICATIONS Research publications 1. Wei X., Rizzoni G., 2004, “Modeling of Hybrid Electric Vehicles for Various Optimization Applications”, ASME Transaction, Journal of Dynamic Systems, Measurement, and Control. 2. Wei X., Guzzella L., Utkin V., Rizzoni G., 2004, “Model-based Fuel Optimal Control of Hybrid Electric Vehicle Using Variable Structure Control Systems”, ASME Transaction, Journal of Dynamic Systems, Measurement, and Control. 3. Wei X., Utkin V., Rizzoni G., 2004, “Sliding Optimal Control for Hybrid Electric Vehicle Energy Management Optimizations”, IFAC FISITA F2004F060. 4. Wei X., Rizzoni G., 2004, “Objective Metrics of Fuel Economy, Performance and Driveability”, SAE paper 2004-01-1338. vi 5. Wei X., Pisu P., Rizzoni G., Yurkovich S., 2003, “Dynamic Modeling of a Hybrid Electric Drivetrain for Fuel Economy, Performance and Driveability Evaluations”, Proceedings of ASME International Mechanical Engineering Congress and Exposition 2003, Washington D.C. 6. Wei X., Rizzoni G., 2001, “A Scalable Approach for Energy Converter Modeling and Supervisory Control Design”, Proceedings of ASME International Mechanical Engineering Congress and Exposition 2001, New York, NY. 7. Rizzoni G., Guezennec Y., Brahma A., Wei X., Miller T., 2000, “VP-SIM: A Unified Approach to Energy and Power Flow Modeling Simulation and Analysis of Hybrid Vehicles”, Proceedings of the 2000 Future Car Congress, Arlington, Virginia. FIELDS OF STUDY Major Field: Mechanical Engineering Specialization: system dynamics, modeling and control of automotive powertrains, hybrid electric vehicles, driveability objective metrics. vii TABLE OF CONTENTS Page Abstract…………………………………………………………………………… ii Dedication ………………………………………………………………………... iv Acknowledgments……………………………………………………………….... v Vita………………………………………………………………………………... vi List of Figures………………………………………………………………...…... xi List of Tables………………………………………………………………...…… xiv Abbreviates……………………………………………………………..……… xv CHAPTERS: CHAPTER 1 INTRODUCTION ........................................................................................ 1 1.1 Motivation and Problem Statement .................................................................... 1 1.2 Contributions of the Research............................................................................. 8 1.3 Organizations of the Dissertation........................................................................ 9 CHAPTER 2 LITERATURE REVIEW AND BACKGROUND .................................... 10 2.1 Modeling and Simulation.................................................................................. 10 2.2 Modeling of a Hybrid Electric Drivetrain......................................................... 14 2.3 Control .............................................................................................................. 17 2.3.1 Supervisory Control Strategies for Hybrid Electric Vehicles................... 18 2.3.2 Control Algorithms for Improving Performance and Driveability........... 28 2.3.3 Optimal Control Theory Overview .......................................................... 30 2.4 Summary..........................................................................................................
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