基于自适应无迹卡尔曼滤波法的并联型电池系统SOC估计  

A state of charge estimation method based on adaptive unscented kalman filter for parallel-connected battery system

作　　者：吴昊 伍华祥 苗一凡 卞艳茹 陈冲 WU Hao;WU Huaxiang;MIAO Yifan;BIAN Yanru;CHEN Chong(School of Electrical Engineering,Yancheng Institute of Technology,Yancheng 224051,China;State Grid Jiangsu Electric Power Co.,Ltd.,Yancheng Power Supply Branch,Yancheng 224005,China)

机构地区：[1]盐城工学院机械工程学院,江苏盐城224051 [2]国网江苏省电力有限公司盐城供电分公司,江苏盐城224005

出　　处：《电气应用》2023年第5期107-112,共6页Electrotechnical Application

基　　金：江苏高校“青蓝工程”项目(2021-11);盐城工学院校级科研项目(xjr2021052);江苏省研究生实践创新计划资助项目。

摘　　要：荷电状态(SOC)是并联型电池系统安全运行和有效控制的关键状态参数之一。当电池系统噪声统计信息(如模型噪声和测量噪声)未知且时变,对于含多个不一致电池单体的并联型电池系统,其SOC的估计准确度往往受限。为此,首先介绍了一种基于模型参数校正器的并联型电池系统,以表征电池单体的不一致性。然后,提出了一种基于自适应无迹卡尔曼滤波法的并联型电池系统SOC估计算法,该算法是一种带噪声统计估计器的时变无迹卡尔曼滤波法。最后,通过与普通无迹卡尔曼滤波法和扩展卡尔曼滤波法的仿真及实验数据对比分析表明,所提出的SOC估计算法能有效、准确地进行并联型电池系统SOC估计。Due to state of charge(SOC)is a key state parameter for the safe operation and control of a parallelconnected battery system(PBS),it is essential to estimate accurately SOC of a lithium-ion PBS composed of thousands of inconsistent cells when the noise statistics are unknown and/or time-varying,such as model noises and measurement noises.To resolve the problems,an equivalent circuit model of the PBS is presented based on a model parameter regulator that can overcome the influence of cell-to-cell variation.An adaptive SOC estimation method based on adaptive unscented Kalman filter(AUKF),which is a time-varying unscented Kalman filter with a noise statistics estimator,is proposed for the PBS when the noise statistics are unknown.Compared with the unscented Kalman filter and extended Kalman filter,the accuracy and effectiveness of the presented SOC estimation method is validated by the simulation results and experimental data.

关 键 词：并联型电池系统 荷电状态 等效电路模型 自适应无迹卡尔曼滤波 噪声估计器 

分 类 号：TM912[电气工程—电力电子与电力传动] TN713[电子电信—电路与系统]