Overview of SOC estimation methods for lithium-ion batteries based on model

doi:10.19799/j.cnki.2095-4239.2023.0016

Abstract

Abstract:

Lithium-ion batteries are extensively used in electric energy storage and new vehicles due to their high energy density and long cycle life. Accurate estimation of the battery's state of charge(SOC) is crucial for improving its service life and utilization efficiency. However, lithium batteries are a highly complex, time-varying, and nonlinear electrochemical system. Thus, an online SOC estimation method with high accuracy is vital for the practical application of lithium batteries. In recent years, model-based SOC estimation methods have gained widespread attention and research because of their closed-loop control and ease of implementation. This paper reviews model-based SOC estimation methods from the aspects of model classification, model parameter identification algorithm, SOC estimation algorithms, and factors influencing SOC estimation. First, various common lithium-ion battery models are summarized, primarily focusing on introducing and comparing common electrochemical and equivalent circuit models. Then, the model establishment methods and SOC state estimation algorithms are examined and compared; various model parameter identification methods and SOC estimation calculation methods are introduced and contrasted. After that, the influencing factors and solutions of the model-based SOC estimation method are analyzed and summarized, mainly addressing the impact of temperature, aging, and battery pack factors on battery SOC estimation. Finally, potential future research directions are discussed and explored.

Key words: lithium-ion batteries, equivalent circuit model, electrochemical model, state of charge, online estimation

CLC Number:

TM 912

Birong TAN, Jianhua DU, Xianghu YE, Xin CAO, Chang QU. Overview of SOC estimation methods for lithium-ion batteries based on model[J]. Energy Storage Science and Technology, 2023, 12(6): 1995-2010.

Figures/Tables 9

Table 1

Comparison of common equivalent circuit models of lithium ion batteries"

模型名称	状态方程	优点	缺点
Rint模型	U_t =U_OC-i_tR₀	结构简单	精度低，应用性差
Thevenin模型	$U t = U O C - i t R 0 - U 1 d U 1 d t = i t C 1 - U 1 R 1 C 1$	结构较简单，考虑了极化现象，应用性好	对电池特性描述存在局限
双极化模型	$U t = U O C - i t R 0 - U 1 - U 2 d U 1 d t = i t C 1 - U 1 R 1 C 1 d U 2 d t = i t C 2 - U 2 R 2 C 2$	能较好模拟电池动态特性，应用性较好	模型结构较复杂，计算成本较高
PNGV模型	$U t = U O C - i t R 0 - U 1 - U d d U 1 d t = i t C 1 - U 1 R 1 C 1 d U d d t = i t C d$	考虑了SOC值对开路电压的影响，考虑了电池动态特性	模型结构复杂，计算成本高，应用性一般
GNL模型	$U t = U O C - i t R 0 - U 1 - U 2 - U d d U d d t = i t C d + U O C R d C d - U d R d C d - U 1 R d C d - U 2 R d C d d U 1 d t = i t C 1 + U O C R d C 1 - U d R d C 1 - U 1 R d C 1 - U 2 R d C 1 - U 1 R 1 C 1 d U 2 d t = i t C 2 + U O C R d C 2 - U d R d C 2 - U 1 R d C 2 - U 2 R d C 2 - U 2 R 2 C 2$	能准确模拟电池动态特性，且考虑了自放电	模型结构过于复杂，应用性差

Table 1

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SOC估计方法	优点	缺点	鲁棒性
扩展卡尔曼滤波方法	克服了传统方法的缺点；适用于电流变化剧烈的场合	模型线性化过程产生误差	差
无迹卡尔曼滤波方法	不需要对模型线性化处理，没有模型线性化误差	在扰动和初值不确定等情况易发散	较差
容积卡尔曼滤波方法	不需要对模型线性化处理，没有模型线性化误差	计算量大	较差
HIF滤波方法	不需要知道噪声准确的统计特性	计算复杂	好
滑膜观测器	抗干扰能力强	输出存在抖动	较好

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