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

被引:0
作者
Yongtao Liu [1 ]
Chuanpan Liu [1 ]
Yongjie Liu [1 ]
Feiran Sun [1 ]
Jie Qiao [1 ]
Ting Xu [2 ]
机构
[1] School of Automobile, Chang'an University
[2] College of Transportation Engineering, Chang'an University
关键词
D O I
暂无
中图分类号
U469.72 [电动汽车];
学科分类号
0807 ;
摘要
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.
引用
收藏
页码:578 / 610
页数:33
相关论文
共 50 条
[21]   A hybrid neural network model with attention mechanism for state of health estimation of lithium-ion batteries [J].
Tang, Aihua ;
Jiang, Yihan ;
Yu, Quanqing ;
Zhang, Zhigang .
JOURNAL OF ENERGY STORAGE, 2023, 68
[22]   Online state of health estimation of lithium-ion batteries through subspace system identification methods [J].
Camboim, Marcelo Miranda ;
Giesbrecht, Mateus .
JOURNAL OF ENERGY STORAGE, 2024, 85
[23]   State of Health Estimation of Lithium-Ion Batteries Based on Dual Charging State [J].
Lu D. ;
Chen Z. .
Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University, 2022, 56 (03) :342-352
[24]   State Of Health Estimation of Lithium-ion Batteries Based On Regression Techniques [J].
Azizi, Chaima ;
Ben Ali, Jaouher .
2017 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND DIAGNOSIS (ICCAD), 2017, :493-498
[25]   Wavelet Based Relative State of Health Estimation for Lithium-Ion Batteries [J].
Xu, Jun ;
Mei, Xuesong ;
Wang, Xiao ;
Zhao, Yunfei .
INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS, 2019, 158 :3101-3106
[26]   A state of health estimation method for full lifetime of lithium-ion batteries [J].
Zhou, Yafu ;
Sun, Xiaoxiao ;
Huang, Lijian ;
Lian, Jing .
Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology, 2021, 53 (01) :55-62
[27]   CTBANet: A new method for state of health estimation of lithium-ion batteries [J].
Zhu, Qinglin ;
Zeng, Xiangfeng ;
Wang, Zhangu ;
Zhao, Ziliang ;
Zhang, Lei ;
Wang, Junqiang .
JOURNAL OF ENERGY STORAGE, 2025, 117
[28]   Generative Adversarial Network for State of Health Estimation of Lithium-ion Batteries [J].
Ye, Zhuang ;
Yu, Jianbo ;
Yang, Pu ;
Yue, Shang ;
Zhou, Ruixu ;
Ma, Mingyan .
2023 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT, ICPHM, 2023, :99-104
[29]   State of health estimation of lithium-ion batteries based on the regional frequency [J].
Huang, Shaotang ;
Liu, Cuicui ;
Sun, Huiqin ;
Liao, Qiangqiang .
JOURNAL OF POWER SOURCES, 2022, 518
[30]   State of health estimation of lithium-ion batteries based on the regional triangle [J].
Zhang, Ya ;
Cai, Yongxiang ;
Liu, Wei ;
Dou, Zhenlan ;
Yao, Bin ;
Zhang, Bide ;
Liao, Qiangqiang ;
Fu, Zaiguo ;
Cheng, Zhiyuan .
JOURNAL OF ENERGY STORAGE, 2023, 69