Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles

Preface.- Introduction.- Powertrain Modeling and Reinforcement Learning.- Prediction and Updating of Driving Information.- Evaluation of Intelligent Energy Management System.- Conclusion.- References.- Author's Biography.

Teng Liu received a B.S. degree in mathematics from Beijing Institute of Technology, Beijing, China, in 2011. He received his Ph.D. degree in automotive engineering from Beijing Institute of Technology (BIT), Beijing, in 2017. His Ph.D. dissertation, under the supervision of Prof. Fengchun Sun, was entitled ""Reinforcement Learning-Based Energy Management for Hybrid Electric Vehicles."" He worked as a research fellow in Vehicle Intelligence Pioneers Ltd. for one year. Now, he is a member of IEEE VTS, IEEE ITS, IEEE IES,IEEE TEC, and IEEE/CAA. Dr. Liu is now a postdoctoral fellow at the Department of Mechanical and Mechatronics Engineering, University of Waterloo, Ontario, Canada. Dr. Liu has more than eight years research and work experience in renewable vehicle and connected autonomous vehicle. His current research focuses on reinforcement learning (RL)-based energy management in hybrid electric vehicles, RL-based decision making for autonomous vehicles, and CPSS-based parallel driving. He has published over 40 SCI papers and 15 conference papers in these areas. He received the Merit Student of Beijing in 2011, the TeliXu Scholarship (Highest Honor) of Beijing Institute of Technology in 2015, ""Top 10"" in 2018 IEEE VTS Motor Vehicle Challenge, and sole outstanding winner in 2018 ABB Intelligent Technology Competition. Dr. Liu is a workshop co-chair in the 2018 IEEE Intelligent Vehicles Symposium (IV 2018) and has been a reviewer in multiple SCI journals, including IEEE Transactions on Industrial Electronics, IEEE Transactions on Intelligent Vehicles, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Industrial Informatics, and Advances in Mechanical Engineering.