Research Progress on State-of-health Estimating Method for Lithium-ion Batteries

doi:10.11985/2024.01.004

Abstract

Abstract:

With the widespread application of lithium-ion batteries in electric vehicles, energy storage stations, it is the key to ensure the reliability and safety of the battery to accurately estimate the health state of the battery. The complex electrochemical reaction inside lithium-ion battery and the changeable external use conditions make it challenging to realize accurate health state estimation and life prediction. With the rapid development of technologies such as artificial intelligence and big data analysis, the methods of battery health assessment have been gradually diversified. First, the aging mechanism and SOH concept of batteries are introduced in this article. Next, different SOH estimation methods are categorized into four classes：experiment-based, model-based, data-driven, and hybrid methods. The characteristics of each method are analyzed in detail, and the corresponding advantages and limitations in practical applications are compared. Finally, the future trends of SOH estimation are prospected.

Key words: Lithium-ion battery, battery aging, health status, evaluation method

CLC Number:

TM912

JIN Jianxin, YU Ruxin, LIU Gang, XU Linbo, MA Yanqiang, WANG Haobin, HU Chen. Research Progress on State-of-health Estimating Method for Lithium-ion Batteries[J]. Journal of Electrical Engineering, 2024, 19(1): 33-48.

Figures/Tables 11

References 68

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模型名称	描述方程	参数	优点	缺点
Rint模型	V=E－IR₀	E为开路电压，R₀为欧姆内阻，V为端电压，I为负载电流	模型简单，易于参数辨识	无法反映电池动态特性，精度低，应用范围较小
Thevenin模型	V=E－IR₀－V_C $I=\frac{{{V}_{C}}}{{{R}_{1}}}+C\frac{d{{v}_{c}}}{dt}$	R₁和C为表述电池极化效应的电阻和电容	R_C回路用于模拟电池动态特性，考虑了欧姆极化和电化学极化，在实际工程应用中较多	因模型参数为常数，模型精度较大程度上受温度变化和电池老化的影响
二阶RC模型	$V=E-I{{R}_{0}}-{{V}_{C1}}-{{V}_{C2}}$$I=\frac{{{v}_{C1}}}{{{R}_{1}}}+{{c}_{1}}\frac{d{{V}_{C1}}}{dt}$	R₁和C₁分别为电化学阻抗和电化学电容；R₂和C₂分别为浓差阻抗和浓差电容	同时考虑了欧姆极化、电化学极化和浓差极化，在估计大倍率工况条件方面具有更高的精度	结构复杂、参数较多，计算较为复杂
PNGV模型	$V=E-I{{R}_{0}}-{{V}_{P}}-{{V}_{\mathrm{OC}}}$ ${{v}_{\mathrm{OC}}}=\frac{1}{{{C}_{\mathrm{OC}}}}$	C_OC为等效电容	模型考虑温度影响，可描述开路电压、容量变化及电池内部反应过程，对电池各种工况适用性好，精确度较好	串联电容的误差累积降低模型精确度，不能反映电池自放电问题
GNL模型	$V=E-{{V}_{b}}-{{V}_{\text{ep}}}-{{I}_{L}}{{R}_{0}}$ $V\text{=}{{I}_{S}}{{R}_{S}}$	R_S为自放电电阻	相比于PNGV模型考虑了电池自放电问题和负载电流随时间累计引起的开路电压变化问题；估算精度更高，适用性更广	相比于PNGV模型，计算更复杂，计算量更大

电池SOH估计方法		优点	缺点
实验法	安时积分法	估算非常准确	测试时间长，会导致容量衰减，仅适用于特定实验室条件
	欧姆内阻法	简单易行、快速	不太准确
	电化学阻抗谱法	估算准确，反映信息全	实施过程复杂、需要专业仪器、成本较高
	容量增量法	对电池类型不敏感，并能有效识别容量损失机制，精确获取电池内丰富的电化学反应信息	受电流大小、采样频率和噪声的影响
模型法	经验模型法	建模难度低，应用范围广	缺乏清晰的物理含义，精度低，不能考虑运行工况、受环境影响
	等效电路模型法	利用电路元件建模简单，可实现在线预测	工况复杂时不适用；精度低
	电化学模型法	预测精度高，可以给出电池退化的详细解释	模型参数多，建模困难、计算量大，鲁棒性差
	卡尔曼滤波及变体粒子滤波	适用于实时处理，预测精度高概率预测，表达不确定性能力强	方法复杂，计算量大，初始化过程复杂；退化模型的建立应准确
数据驱动法	人工神经网络	具有非线性特征和自学能力	需要大量数据训练，精度受训练数据和训练方法影响大，不适合小样本
	支持向量机	计算量小，预测精度高	长期预测效果不佳、适用于小样本
	模糊逻辑	可对复杂的非线性系统进行建模	自学习能力和适应能力较弱
	高斯过程回归	适用于小样本、高维的回归问题；适应性强	计算复杂度高、抗干扰性差
融合法	模型法与数据驱动法融合	精度更高、增加了模型可解释性	计算复杂度高
融合法	多种数据驱动法融合	精度更高、泛化能力更强	输入数据量大，计算复杂度高