On-line estimation of state-of-charge of Li-ion batteries in electric vehicle using the resampling particle filter

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On-line estimation of state-of-charge of Li-ion batteries in electric vehicle using the resampling particle filter

Shao, Sai / Bi, Jun / Yang, Fan / Guan, Wei

Highlights • A specific battery model applying to the running electric vehicles is developed. • We identify the unknown parameters of battery model. • First SOC is estimated based on particle filter with the suitable battery model. • The comparisons of two SOC estimation methods are given. • All experimental data are collected from the running electric vehicles.

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 0.05V, 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.