基于FFRLS-SRUKF算法的锂电池SOC估计研究  

Study on SOC Estimation of Lithium Battery Based on FFRLS-SRUKF Algorithm

作　　者：林群锋 高秀晶 黄红武 李斌 王艺菲 杨镓炜 LIN Qunfeng;GAO Xiujing;HUANG Hongwu;LI Bin;WANG Yifei;YANG Jiawei(Institute of Smart Marine and Engineering,Fujian University of Technology,Fuzhou 350118,China;Fujian Provincial Key Laboratory of Marine Smart Equipment,Fuzhou 350118,China;School of Electronic Electrial Engineering and Physics,Fujian University of Technology,Fuzhou 350118,China)

机构地区：[1]福建理工大学智慧海洋与工程研究院,福建福州350118 [2]海洋智能装备福建省高校重点实验室,福建福州350118 [3]福建理工大学电子电气与物理学院,福建福州350118

出　　处：《自动化仪表》2024年第10期99-104,共6页Process Automation Instrumentation

基　　金：福建省海洋经济发展专项基金资助项目(FUHJF-L-2022-16);福建省科技创新重点基金资助项目(2022G02008);福建省财政厅教育和科研专项基金资助项目(GY-Z22010)。

摘　　要：传统无迹卡尔曼滤波在荷电状态(SOC)估计时主要面临两个问题:一是电池模型参数固定导致SOC估计精度低;二是协方差矩阵出现非正定时导致SOC估计失败。提出了遗忘因子递推最小二乘(FFRLS)算法结合平方根无迹卡尔曼滤波(SRUKF)的SOC估计算法。首先,建立二阶阻容(RC)等效电路模型。其次,利用FFRLS算法对电路模型参数在线辨识并实时修正电池等效电路模型,在此基础上使用SRUKF算法估计SOC。最后,通过间歇恒流脉冲放电和动态应力测试工况对所提算法进行验证。试验结果表明,该算法的平均绝对值误差低于0.0115、均方根误差低于0.012。与SRUKF算法相比,FFRLS-SRUKF算法具有更好的SOC估计性能,为电池管理系统解决锂电池的不一致性提供了可靠依据。Conventional unscented Kalman filtering faces two main problems in state of charge(SOC)estimation:firstly,the low accuracy of SOC estimation due to the fixed parameters of the battery model;secondly,the failure of SOC estimation due to the occurrence of non-positive timing in the covariance matrix.A forgetting factor recursive least square(FFRLS)algorithm combined with square root unscented Kalman filter(SRUKF)is proposed for SOC estimation algorithm.Firstly,a second-order resistance capacitance(RC)equivalent circuit model is established.Secondly,the FFRLS algorithm is used to identify the circuit model parameters online and correct the battery equivalent circuit model in real time,based on which the SRUKF algorithm is used to estimate the SOC.Finally,the proposed algorithm is validated by intermittent constant-current pulse discharging and dynamic stress test conditions.The text results prove that the average absolute error of the algorithm is lower than 0.0115,and the rootmean-square error is lower than 0.012.Compared with the SRUKF algorithm,FFRLS-SRUKF algorithm has a better SOC estimation performance,which provides a reliable basis for the battery management system to solve the inconsistency of the lithium batteries.

关 键 词：锂电池 电池管理系统 荷电状态 等效电路模型 在线参数辨识 遗忘因子递推最小二乘算法 平方根无迹卡尔曼滤波 

分 类 号：TH701[机械工程—仪器科学与技术]