基于二阶RC等效电路和SR-DUKF算法的锂电池SOC估算研究  

Research on Estimation of Lithium Battery SOC Based on Second-order RC Equivalent Circuit and SR-DUKF Algorithm

作　　者：贾先屹 王顺利[1,2] 曹文 乔家璐[1] JIA Xianyi;WANG Shunli;CAO Wen;QIAO Jialu(School of Information Engineering,Southwest University of Science and Technology,Mianyang 621010,China;CPSS)

机构地区：[1]西南科技大学信息工程学院,绵阳621010 [2]中国电源学会

出　　处：《电源学报》2024年第4期236-242,共7页Journal of Power Supply

基　　金：国家自然科学基金资助项目(62173281,61801407);四川省科技厅重点研发项目(2018GZ0390,2019YFG0427)。

摘　　要：锂离子电池的荷电状态是电池管理系统BMS(battery management system)运维的重要参数,对其准确估算关系到锂离子电池的实时监测和安全控制。传统无迹卡尔曼滤波UKF(unscented Kalman filter)算法在估算锂电池SOC时有使协方差矩阵负定的风险,存在估计精度不高的问题。为解决该算法的不足,以三元锂电池为研究对象,建立二阶RC等效电路模型对电池的工作特性进行表征,在传统的UKF算法基础上提出一种双UT变换的平方根无迹卡尔曼滤波SR-DUKF(square-root double unscented Kalman filter)算法,并结合多种工况对改进后的算法进行验证。实验结果表明,改进后的SR-DUKF算法通过二阶RC等效电路能够较好地对锂离子电池SOC进行估算,在HPPC、BBDST工况下的平均误差分别为0.59%、0.52%,2种工况下的收敛时间分别为60 s和110 s,验证了改进后SR-DUKF算法具有更高的估算精度、更好的收敛性及更优的鲁棒性。The state-of-charge( SOC) of lithium-ion battery is an important parameter for the operation and maintenance of a battery management system( BMS),and its accurate estimation is related to the real-time monitoring and safety control of lithium-ion battery.The traditional unscented Kalman filter( UKF) algorithm has the risk of making the covariance matrix negative when estimating the SOC of lithium battery,and the estimation accuracy is not optimal.To solve the shortcomings of this algorithm,a ternary lithium-ion battery is taken as the research object,and a second-order RC equivalent circuit model is established to describe the working characteristics of the battery.Based on the traditional UKF algorithm,a square-root double unscented Kalman filter( SR-DUKF) algorithm with double unscented transformation is proposed,and it is verified under multiple working conditions.Experimental results show that the improved SR-DUKF algorithm can better estimate the SOC of lithium-ion battery based on the second-order RC equivalent circuit.The average errors under HPPC and BBDST conditions are 0.59% and 0.52%,respectively,and the convergence times are 60 s and 110 s,respectively,which verifies that the improved SR-DUKF algorithm has a higher estimation accuracy,better convergence and better robustness.

关 键 词：锂离子电池 二阶RC模型 荷电状态 平方根双无迹卡尔曼滤波 电池管理系统 

分 类 号：TM912[电气工程—电力电子与电力传动]