On-line estimation of state-of-charge of Li-ion batteries in electric vehicle using the resampling particle filter |
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| Institution: | 1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;2. Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, United States;1. College of Electrical Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310027, PR China;2. State Key Laboratory of Industrial Control Technology, Zhejiang University, 38 Zheda Road, Hangzhou 310027, PR China;3. School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW, 2308, Australia;4. Zhejiang Province Marine Renewable Energy Electrical Equipment and System Technology Research Laboratory, Zhejiang University, 38 Zheda Road, Hangzhou 310027, PR China;1. School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, Sichuan Province611731, PR China;2. School of Science, Hubei University for Nationalities, Enshi, Hubei Province445000, PR China;3. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan Province611731, PR China;1. School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Drive, Atlanta, GA 30332, United States;2. Department of Civil Engineering, Kansas State University, 2132 Fiedler Hall, Manhattan, KS 66503, United States |
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| Abstract: | Accurate battery state-of-charge (SOC) estimation is important for ensuring reliable operation of electric vehicle (EV). Since a nonlinear feature exists in the battery system and particle filter (PF) performs well in solving nonlinear or non-Gaussian problems, this paper proposes a new PF-based method for estimating SOC. Firstly, the relationships between the battery characteristics and SOC are analyzed, then the suitable battery model is developed and the unknown parameters in the battery model are on-line identified using the recursive least square with forgetting factors. The proposed battery model is considered as the state space model of PF and then SOC is estimated. All experimental data are collected from the running EVs in Beijing. The experimental errors of SOC estimation based on PF are less than 0.05 V, which confirms the good estimation performance. Moreover, the contrastive results of three nonlinear filters show PF has the same computational complexity as extend Kalman filter (EKF) and unscented Kalman filter (UKF) for low dimensional state vector, but PF have significantly better estimation accuracy in SOC estimation. |
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| Keywords: | Electric vehicle State-of-charge Battery model Parameters identification Particle filter |
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