沈阳工业大学学报2024，Vol.46Issue(3)：318-323,6.DOI:10.7688/j.issn.1000-1646.2024.03.12
基于AFEKF的锂离子电池SOC估算方法
SOC estimation method based on AFEKF for lithium ion battery
摘要
Abstract
In order to solve the problem that the cumulative error is easy to occur because of the influence of historical data when estimating the charge state of lithium battery by using extended Kalman filtering algorithm,a SOC(state of charge)estimation method based on adaptive fading extended Kalman filtering was proposed.Thevenin equivalent model and recursive least square method were employed to identify battery parameters.By introducing adaptive fading factor into EKF algorithm,the influence of historical data on current state estimation was suppressed,and the SOC estimation of lithium battery was completed.The results show that AFEKF(adaptive fading extended Kalman filtering)algorithm can effectively converge when it is repeated for 20 times,and it has better robustness.The average error of SOC estimation is 1.03%,the root mean square error is 1.21%,and the average running time is 1.476 s,showing a good simulation for the dynamic and static characteristics of batteries.关键词
锂离子电池/荷电状态/卡尔曼滤波/SOC估算/估算方法/EKF算法/最小二乘法/自适应Key words
lithium ion battery/state of charge/Kalman filtering/SOC estimation/estimation method/EKF algorithm/least square method/self-adaption分类
信息技术与安全科学引用本文复制引用
刘光军,吴思齐,张恒,邓洲..基于AFEKF的锂离子电池SOC估算方法[J].沈阳工业大学学报,2024,46(3):318-323,6.基金项目
国家自然科学基金项目(61903129) （61903129）
湖北工业大学产业研究院项目(XYYJ2022C01). （XYYJ2022C01）
