Abstract
Accurate state-of-power (SOP) estimation is critical for building battery systems with optimized performance and longer life in electric vehicles and hybrid electric vehicles. This paper proposes a novel parameter identification method and its implementation on SOP prediction for lithium-ion batteries. The extremum seeking algorithm is developed for identifying the parameters of batteries on the basis of an electrical circuit model incorporating hysteresis effect. A rigorous convergence proof of the estimation algorithm is provided. In addition, based on the electrical circuit model with the identified parameters, a battery SOP prediction algorithm is derived, which considers both the voltage and current limitations of the battery. Simulation results for lithium-ion batteries based on real test data from urban dynamometer driving schedule (UDDS) are provided to validate the proposed parameter identification and SOP prediction methods. The proposed method is suitable for real operation of embedded battery management system due to its low complexity and numerical stability.
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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Recommended by Associate Editor Yongping Pan under the direction of Editor Young IL Lee. This work is supported by the National Natural Science Foundation of China under Grants No.51807179 and No.51777193, and The Key Research and Development Program of Zhejiang Provience under Grants No.2019C01149.
Chun Wei received his B.S. degree in electrical engineering from Beijing Jiao-tong University, Beijing, China, in 2009, an M.S. degree in electrical engineering from North China Electric Power University, Beijing, China, in 2012, and a Ph.D. degree in electrical engineering from the University of Nebraska-Lincoln, Lincoln, NE, USA, in 2016. He was a postdoctoral researcher in ABB Corporate Research Center, Raleigh, NC, USA in year 2017. He is currently an Associate Professor with College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China. His research interests include renewable energy generation systems, adaptive control, motor drives, and system identification.
Mouhacine Benosman is a Senior Research Scientist at Mitsubishi Electric Research Labs (MERL) in Cambridge, USA. Before joining MERL, he worked at Reims University, France, Strathclyde University, Scotland, and National University of Singapore. His research interests include modeling and control of flexible systems, nonlinear robust and fault tolerant control, multi-agent distributed control with applications to robotics and smart-grid systems, and learning and adaptive control for nonlinear systems. Mouhacine has published a monograph about learning-based adaptive control, more than 100 peer-reviewed journal articles and conference papers, and more than 20 patents in the field of mechatronics systems control. He is a senior member of the IEEE, Associate Editor of the Control System Society Conference Editorial Board, Associate Editor of the Journal of Optimization Theory and Applications, and Senior Editor of the International Journal of Adaptive Control and Signal Processing.
Taesic Kim received his M.S. degree in Electrical Engineering and his Ph.D. degree in Computer Engineering at the University of Nebraska-Lincoln, in 2012 and 2015, respectively. In 2009, He was with the New and Renewable Energy Research Group of Korea Electrotechnology Research Institute, Korea. He was also with Mitsubishi Electric Research Laboratories, Cambridge, MA, USA in 2013. Currently, He is an assistant professor in the Department of Electrical Engineering and Computer Science at the Texas A&M University-Kingsville. He research focuses on energy IoT, power electronics, cyber and physical security, blockchain, and intelligence algorithms for power and energy systems. He holds 2 U.S. patents and co-authored more than 40 papers in refereed journals and IEEE conference proceedings in the field of cyber-physical power and energy systems. He is a Cyber Physical Security Steering Committee for IEEE PELS and a Guest Associate Editor of the IEEE Journal of Emerging and Selective Topics in Power Electronics.
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Wei, C., Benosman, M. & Kim, T. Online Parameter Identification for State of Power Prediction of Lithium-ion Batteries in Electric Vehicles Using Extremum Seeking. Int. J. Control Autom. Syst. 17, 2906–2916 (2019). https://doi.org/10.1007/s12555-018-0506-y
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DOI: https://doi.org/10.1007/s12555-018-0506-y