基于模型的锂离子电池SOC估计方法综述

doi:10.19799/j.cnki.2095-4239.2023.0016

摘要/Abstract

摘要：

锂离子电池由于其高能量密度、高循环寿命等优点被广泛应用于电力储能和新能源汽车中。准确估计电池的荷电状态(state of charge，SOC)对提高电池使用寿命和利用效率具有重要意义。然而，锂电池是一个高度复杂、时变和非线性的电化学系统。因此，精度高的在线SOC估计方法对锂电池的实际应用非常重要。近年来，基于模型的SOC估计方法由于其闭环控制、易于实现等特点被广泛关注和研究。本文从模型分类、模型参数辨识算法、SOC估计算法以及SOC估计影响因素对基于模型的SOC估计方法进行综述，首先归纳总结了各种常见的锂离子电池模型，主要介绍了各种常见电化学模型和等效电路模型并进行对比分析；然后重点对模型建立方法和SOC状态估计算法进行梳理和对比，主要介绍了各种模型参数辨识方法及SOC估计方法并进行了对比分析；之后对影响基于模型的SOC估计方法精度的影响因素及解决方法进行分析和总结，主要从温度、老化以及电池组对电池SOC估计的影响进行分析；最后对未来的研究方向进行了讨论和展望。

关键词: 锂离子电池, 等效电路模型, 电化学模型, 荷电状态, 在线估算

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

中图分类号:

TM 912

谭必蓉, 杜建华, 叶祥虎, 曹馨, 瞿常. 基于模型的锂离子电池SOC估计方法综述[J]. 储能科学与技术, 2023, 12(6): 1995-2010.

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.

图/表 9

表1

常见锂离子电池等效电路模型对比"

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

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表2

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图7

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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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