Application of square root sigma point Kalman filter to SOC estimation of LiFePO4 battery pack

被引：0

作者：

Zhang J. ^{[1
]}

Tong W. ^{[1
]}

Qi H. ^{[1
]}

Zhang C. ^{[1
]}

机构：

[1] Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province (Electrical Engineering College of Yanshan University), Qinhuangdao, 066004, Hebei Province

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2016年 / 36卷 / 22期

基金：

中国国家自然科学基金;

关键词：

Equivalent model; LiFePO[!sub]4[!/sub] battery; SOC estimation; Square root sigma point Kalman filter (SRSPKF);

D O I：

10.13334/j.0258-8013.pcsee.160226

中图分类号：

学科分类号：

摘要：

State of charge (SOC) estimation technique is one of the most important functions of battery management system. The main purpose of this paper is to accurately estimate SOC of each cell in the series connected LiFePO4 battery pack. Firstly a comprehensive battery model was established based on Thevenin model and Ah counting model; and then a square root sigma point Kalman filter (SRSPKF) was adopted for SOC estimation, besides, a recursive least square (RLS) algorithm was also used to identify model parameters, in this way, model parameter identification and SOC estimation can be realized simultaneously by combined SPKF-RLS method. Theoretically speaking, by using SRSPKF, the system states were propagated in the form of square root of its variance, and thus the computation complexity of conventional SPKF can be significantly reduced. Experimental results show that, compared with SPKF, SRSPKF possesses a stronger error suppression capability of state estimation, and more accurate SOC estimation results can be obtained by SRSPKF. © 2016 Chin. Soc. for Elec. Eng.

引用

页码：6246 / 6253

页数：7

共 20 条

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