电源学报2024，Vol.22Issue(4)：236-242,7.DOI:10.13234/j.issn.2095-2805.2024.4.236
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
摘要
Abstract
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Key words
Lithium-ion battery/second-order RC model/state-of-charge(SOC)/square-root double unscented Kalman filter(SR-DUKF)/battery management system(BMS)分类
信息技术与安全科学引用本文复制引用
贾先屹,王顺利,曹文,乔家璐..基于二阶RC等效电路和SR-DUKF算法的锂电池SOC估算研究[J].电源学报,2024,22(4):236-242,7.基金项目
国家自然科学基金资助项目(62173281,61801407) （62173281,61801407）
四川省科技厅重点研发项目(2018GZ0390,2019YFG0427)This work is supported by National Natural Science Foundation of China under the grant 62173281 and 61801407 （2018GZ0390,2019YFG0427）
Sichuan Science and Technology Program under the grant 2018GZ0390 and 2019YFG0427 （）
