Review on degradation mechanism and health state estimation methods of lithium-ion batteries

IF 7.4 2区 工程技术 Q1 ENGINEERING, CIVIL
Yongtao Liu , Chuanpan Liu , Yongjie Liu , Feiran Sun , Jie Qiao , Ting Xu
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引用次数: 2

Abstract

State of health (SOH) estimation is important for a lithium-ion battery (LIB) health state management system, and accurate estimation of SOH is influenced by the degree of degradation of the LIB. However, considering the complex electrochemical reactions within Li electrons and the influence of many external factors on internal reactions, it is difficult to accurately estimate the SOH based on the surface state characteristics of the battery (including current, voltage, and temperature). Thus, in this study, the knowledge graph method is employed to analyze keyword co-occurrences and citations in the literature on LIB degradation and SOH estimation to determine research hotspots. Based on the research trends, findings regarding the internal and external degradation mechanisms and influencing factors of (LIBs) are reorganized, and chemical and physical degradation processes, including solid electrolyte interface (SEI) layer formation, fracture, Li plating, and dendrite formation, are systematically introduced based on the modeling perspective. The interrelationships between these degradation factors and their effects on capacity and power decay as well as their correlation with SOH estimation are evaluated. Additionally, a comparative analysis of existing SOH estimation methods is presented, and the applicable scenarios and technical problems of each method are summarized. The key issues such as model simplification, estimation methods based on random data, and second-life SOH are also analyzed and discussed. The results show that the estimation results of methods mixing multiple models tend to be more accurate. Finally, the development trend of SOH estimation methods under complex degradation conditions and usage scenarios is analytically discussed.

锂离子电池降解机理及健康状态评价方法综述
健康状态(SOH)估计对于锂离子电池(LIB)健康状态管理系统来说是重要的,并且SOH的准确估计受到LIB退化程度的影响。然而,考虑到锂电子内部复杂的电化学反应以及许多外部因素对内部反应的影响,很难根据电池的表面状态特性(包括电流、电压和温度)准确估计SOH。因此,在本研究中,采用知识图方法分析了LIB退化和SOH估计文献中的关键词共现和引用,以确定研究热点。基于研究趋势,对LIBs的内外降解机制和影响因素进行了重组,并从建模的角度系统地介绍了化学和物理降解过程,包括固体电解质界面(SEI)层的形成、断裂、Li电镀和枝晶的形成。评估了这些退化因子之间的相互关系及其对容量和功率衰减的影响,以及它们与SOH估计的相关性。此外,对现有的SOH估计方法进行了比较分析,总结了每种方法的适用场景和技术问题。对模型简化、基于随机数据的估计方法、二次生命SOH等关键问题进行了分析和讨论。结果表明,混合多个模型的方法的估计结果往往更准确。最后,分析讨论了复杂退化条件和使用场景下SOH估计方法的发展趋势。
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来源期刊
CiteScore
13.60
自引率
6.30%
发文量
402
审稿时长
15 weeks
期刊介绍: The Journal of Traffic and Transportation Engineering (English Edition) serves as a renowned academic platform facilitating the exchange and exploration of innovative ideas in the realm of transportation. Our journal aims to foster theoretical and experimental research in transportation and welcomes the submission of exceptional peer-reviewed papers on engineering, planning, management, and information technology. We are dedicated to expediting the peer review process and ensuring timely publication of top-notch research in this field.
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