Lithium-ion Battery SOC Estimation Based on Weighted Adaptive Recursive Extended Kalman Filter Joint Algorithm

被引:0
作者
Wang, Jianfeng [1 ]
Zhang, Zhaozhen [2 ]
机构
[1] Changan Univ, Shanxi Rd Traff Intelligent Detect & Equipment En, Xian, Peoples R China
[2] Changan Univ, Sch Automobile, Xian, Peoples R China
来源
2020 IEEE 8TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT) | 2020年
关键词
SOC; Extended Kalman filter; Recursive Least Squares; lithium-ion battery; CHARGE; MODEL; STATE;
D O I
10.1109/iccsnt50940.2020.9304993
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Accurate estimation of the lithium-ion battery SOC is critical to the battery management system (BMS). In order to accurately estimate the lithium-ion battery SOC, a second-order equivalent model of the lithium-ion battery is firstly established in this paper, and the lithium-ion battery's nonlinear relationship of SOC-OCV is obtained through the experiment. Then the online parameter identification method based on the least square method is used to estimate the parameters of the lithium-ion battery's online model, and the accurate estimation of lithium-ion battery SOC is achieved by combining weighted adaptive recursive least square method with extended Kalman filter. This paper compares estimation accuracy of the battery SOC based on the extended Kalman filter algorithm (EKF), the recursive least square method based on the forgetting factor (FRLS), and the weighted adaptive recursive extended Kalman filter joint algorithm (WAREKF) in the experiment. The experiment result shows that the estimation accuracy of the battery SOC based on WAREKF which is proposed in this paper is higher than that of EKF and FRLS, and its root mean square error (RMSE) is less than 1%.
引用
收藏
页码:11 / 15
页数:5
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