基于BCRLS-ASREKF联合算法的动力锂电池SOC估计  

SOC Estimation of Lithium Power Battery Based on BCRLS-ASREKF Joint Algorithm

作　　者：李德俊 赵传婷 刘世林 孔敏 LI Dejun;ZHAO Chuanting;LIU Shilin;KONG Min(School of Electrical Engineering,Anhui Polytechnic University,Wuhu 241000,China;School of Electrical and Photoelectronic Engineering,West Anhui University,Lu'an 237400,China)

机构地区：[1]安徽工程大学电气工程学院,安徽芜湖241000 [2]皖西学院电气与光电工程学院,安徽六安237400

出　　处：《安徽工程大学学报》2022年第5期42-50,共9页Journal of Anhui Polytechnic University

基　　金：安徽省重点研究与开发计划基金资助项目(202004A05020014)。

摘　　要：荷电状态(SOC)估计是保障动力锂电池安全性和寿命的关键问题之一。为了提高SOC估计的精度,基于Sage-Husa自适应滤波原理,对BCRLS-SREKF算法进行改进,构造了BCRLS-ASREKF联合估计算法。通过实时估计过程噪声和观测噪声的协方差,并利用更新的噪声协方差直接计算系统状态协方差的平方根,抑制滤波发散,在增强数值计算稳定性的同时,能够达到提高滤波精度的目的。为了验证算法的有效性和先进性,采用北京公交动态应力测试工况(BBDST)实验数据开展了多种算法的对比分析。结果表明,所提算法的估计精度有显著提高,SOC估计平均绝对值误差(MAE)为0.093%,均方根误差(RMSE)为0.139%。State of charge(SOC)is one of the key problems to ensure the safety and life time of the Lithium power battery.In order to improve the accuracy of SOC estimation,this paper improves the BCRLS-SREKF joint estimation algorithm based on Sage-Husa adaptive filtering theory,named as BCRLS-ASREKF.To suppress the divergence of filtering,the algorithm estimates and corrects the process noise and the observation noise covariance in real time,and utilizes the updating of the noise covariance to calculate the square root of the covariance of the system state directly,thus the numerical stability and the precision of the filter are improved together to verify the effectiveness and the advancement of the algorithm,the contrastive analysis was carried out among several different algorithms based on the experimental data from Beijing Bus Dynamic Stress Test(BBDST).The results show that the estimation accuracy of the proposed algorithm is significantly enhanced,the mean absolute error(MAE)of the SOC estimate is 0.093%,and the root mean square error(RMSE)is 0.139%.

关 键 词：动力锂电池 荷电状态 Sage-Husa自适应滤波算法 自适应平方根扩展卡尔曼滤波 

分 类 号：TM911[电气工程—电力电子与电力传动]