Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management

Tao Jili received the B.Sc. and M.Sc. degrees from Central South University, Changsha, China, in 2001 and 2004, respectively, and the Ph.D. degree from Zhejiang University, Hangzhou, China, in 2007. She is currently an Associate Professor with the Institute of Ningbo Technology, Zhejiang University, Ningbo, China. Her current research interests include intelligent optimization, modeling, and its applications to electronic system design and control system design for HEV, chemical processes. Ridong Zhang received the Ph.D. degree in control science and engineering from Zhejiang University, Hangzhou, China, in 2007. From 2012 to 2016, he was a Visiting Professor with the Chemical and Biomolecular Engineering Department, The Hong Kong University of Science and Technology, Hong Kong. He is currently a Professor with the Institute of Information and Control, Hangzhou Dianzi University, Hangzhou. His current research interests include modeling and control for chemical nonlinear systems and HEV. Longhua Ma received the B.S. degree in industrial electrical automation from Lanzhou Jiaotong University,Lanzhou, China, in 1986, the M.S. degree and Ph.D. degree in control science and engineering from Zhejiang University, Hangzhou, China, in 1993 and 2002, respectively. He was an associate research fellow with National engineering research center for industrial automation, Zhejiang University, Hangzhou, China from 1993 to 2008.From 2008 to 2012, he was an associate professor with School of aeronautics and astronautics, Zhejiang University, Hangzhou, China. Currently, he is a professor with Ningbo Industrial Internet Institute, Ningbo, China. He has (co)author four books and published over 70 international journal and conference papers. His currently research interests include network security, new energy and electric vehicle energy management and control and inertial navigation theory and application.

• PrefaceAcknowledgments1. Introduction2. System modeling of lithiumeion battery, PEMFC, and supercapacitor in HEV3. Neural network modeling for SOH of lithium-ion battery and performance degradation prediction of fuel cell4.Optimal fuzzy energy management for fuel cell/supercapacitor systems using neural network-based driving pattern recognition5. Optimal fuzzy energy management system optimization based on NSGA-III-SD for lithium battery/supercapacitor HEV6. Q learning-based hybrid energy management strategy7. Improved DDPG hybrid energy management strategy based on LSH8. Further idea on meta EMS for HEVIndex