Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles
Springer International Publishing (Verlag)
978-3-031-00375-2 (ISBN)
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems foundedin artificial intelligence and their real-time evaluation and application.
In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
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.
Preface.- Introduction.- Powertrain Modeling and Reinforcement Learning.- Prediction and Updating of Driving Information.- Evaluation of Intelligent Energy Management System.- Conclusion.- References.- Author's Biography.
| Erscheinungsdatum | 06.06.2022 |
|---|---|
| Reihe/Serie | Synthesis Lectures on Advances in Automotive Technology |
| Zusatzinfo | X, 90 p. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Gewicht | 207 g |
| Themenwelt | Technik ► Elektrotechnik / Energietechnik |
| Technik ► Fahrzeugbau / Schiffbau | |
| Technik ► Maschinenbau | |
| ISBN-10 | 3-031-00375-6 / 3031003756 |
| ISBN-13 | 978-3-031-00375-2 / 9783031003752 |
| Zustand | Neuware |
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