基于AFEKF的锂离子电池SOC估算方法  

SOC estimation method based on AFEKF for lithium ion battery

作　　者：刘光军 吴思齐 张恒[1] 邓洲 LIU Guangjun;WU Siqi;ZHANG Heng;DENG Zhou(Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System,Hubei University of Technology,Wuhan 430068,Hubei,China)

机构地区：[1]湖北工业大学太阳能高效利用及储能运行控制湖北省重点实验室,湖北武汉430068

出　　处：《沈阳工业大学学报》2024年第3期318-323,共6页Journal of Shenyang University of Technology

基　　金：国家自然科学基金项目(61903129);湖北工业大学产业研究院项目(XYYJ2022C01)。

摘　　要：针对利用扩展卡尔曼滤波算法估算锂电池荷电状态时,由于历史数据影响易产生累积误差的问题,提出了一种基于自适应渐消扩展卡尔曼的SOC估算方法。选用Thevenin等效模型并用递推最小二乘法进行电池参数辨识,通过将自适应渐消因子引入EKF算法中,抑制历史数据对当前状态估算的影响,完成锂电池SOC估算。结果表明:AFEKF算法在递推20次时可有效收敛,具有较好鲁棒性,估算SOC的平均误差为1.03%,误差均方根为1.21%,平均运行时间为1.476 s,可以较好地模拟电池的动静态特性。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 

分 类 号：TM912[电气工程—电力电子与电力传动]