Review of SOC Estimation Methods for Power Battery Based on Equivalent Circuit Model

doi:10.11985/2021.01.013

Abstract

Abstract:

：Accurate modeling and estimation of state of charge (SOC) of power battery are of great significance for improving the utilization efficiency of battery and prolonging its service life. Among all kinds of SOC estimation methods, the one based on the battery equivalent circuit model has good accuracy and robustness, and the battery model has simple structure and small calculation amount. Therefore, its application prospect in the battery management system (BMS) is bright. Focusing on the SOC estimation methods based on the equivalent circuit model, the common battery equivalent circuit models at home and abroad are summarized, and then the existed SOC estimation methods are compared. The factors affecting the estimation accuracy of SOC and the countermeasures are also analyzed in detail. Finally, the research trend of SOC estimation in the future is discussed and prospected.

Key words: Power batteries, battery management system, state of charge (SOC) estimation, equivalent circuit model

CLC Number:

TM912

GAO Mingkun, XU Hailiang, WU Mingbo. Review of SOC Estimation Methods for Power Battery Based on Equivalent Circuit Model[J]. Journal of Electrical Engineering, 2021, 16(1): 90-102.

Figures/Tables 17

References 74

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模型	优点	缺点	精准性	应用性
Rint模型	模型结构简单	精度低	差	差
Thevenin模型	模型结构较简单考虑了电池动态特性	对电池实际特性的描述存在局限	较好	好
双极化模型	能准确模拟电池动态特性	模型结构复杂计算成本较高	好	好
PNGV 模型	考虑了电池动态特性;考虑了SOC变化对OCV的影响	模型结构较复杂	较好	较好
GNL模型	能准确模拟电池动态特性;考虑了自放电现象	模型结构太复杂计算成本高	好	较差

SOC估计方法		优点	缺点	鲁棒性
卡尔曼滤波法	扩展卡尔曼滤波法	克服了传统估计方法的缺点;适用于电流波动剧烈的工况	线性化处理产生误差	差
	无迹卡尔曼滤波法	不需要对非线性函数线性化处理,避免误差	异常扰动、初值不确定等因素可能导致系统发散	较差
	综合型卡尔曼滤波法	结合了不同卡尔曼滤波算法的优点,估计精度高	需要至少两个卡尔曼滤波算法计算复杂	一般
滑模观测器法		对参数变化和扰动不敏感	输出存在抖动	较好
H_∞滤波法		不要求精准的噪声统计特性,最小化最大估计误差	计算复杂	好