Hev Fuel Optimization Using Interval Back Propagation Based Dynamic Programming

To my Parents Whose guidance, care, love, and support has proven extremely valuable to me ACKNOWLEDGEMENTS First and foremost I would like to thank my advisor, Dr. Nader Sadegh, for giving me the opportunity to carry out this research. I feel privileged to work alongside him for the last two years. He has been a guiding light, supporting me at all times and giving me the right advice. His words of encouragement thought the duration of the research spurred me onto this work. In addition to helping with my research, the discussions I had with Dr. Sadegh have helped me develop a deep interest control theory and has motivated me to delve further in this field. their valuable time to serve on my thesis committee and provided their valuable feedback. Finally, I owe my thanks to Mrs. Johnson Glenda of the mechanical engineering department, who has helped guide me through the administrative process of completing my MS thesis degree in a timely manner. SUMMARY In this thesis: • The primary powertrain components of a power split hybrid electric vehicle are modeled. In particular, the dynamic model of the energy storage element (i.e., traction battery) is exactly linearized through an input transformation method to take advantage of the proposed optimal control algorithm. • A new dynamic programming approach called interval back propagation is introduced. This involves quantization of the energy storage states (i.e., states of charge) into a set of computed intervals. • A closed form globally optimal solution is obtained for the optimal input under certain conditions. • The procedure used for real time implementation of the algorithm is elucidated • The fuel economy results are compared with those from standard rule based techniques to confirm improvement. A lipschitz continuous and nondecreasing cost function is formulated in order to minimize the net amount of consumed fuel. The globally optimal solution is obtained using a dynamic programming routine that produces the optimal input based on the current state of charge and the future power demand. it is shown that the global optimal control solution can be expressed in closed form for a time invariant and convex incremental cost function utilizing the interval back propagation approach. The global optimality of both time varying and invariant solutions are rigorously proved. The optimal closed form solution is further shown to be applicable to the xi time varying case provided that the time variations of …