Research Status and Prospects of State of Health Estimation Methods for Lithium-ion Batteries

被引：0
作者：
Li, Zhuohao [1 ]
Shi, Qionglin [1 ]
Wang, Kangli [1 ,2 ]
Jiang, Kai [1 ,2 ,3 ]
机构：
[1] School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan
[2] State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan
[3] Engineering Research Center of Power Safety and Efficiency (Ministry of Education), Wuhan
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2024年 / 48卷 / 20期
关键词：
advanced sensing; battery aging; data-driven; lithium-ion batteries; state of health estimation;
D O I：
10.7500/AEPS20231221006
中图分类号：
学科分类号：
摘要：
As an important energy storage battery, lithium-ion batteries have gradually matured and been widely used in various industrial fields in recent years, effectively alleviating the pressure of energy transition and environmental pollution. To ensure the safe and efficient long-term service of lithium-ion batteries and reduce operation costs, it is especially important to accurately estimate the state of health (SOH) of batteries in real time. In this paper, the current development of SOH estimation methods for lithium-ion batteries is reviewed. Firstly, the aging mechanism of lithium-ion batteries and related concepts of SOH are introduced. Secondly, traditional SOH estimation methods, including test-based methods, model-based methods, data-driven methods, and hybrid methods, are introduced. Additionally, new SOH estimation methods based on advanced sensing technologies are presented, demonstrating the improvement processes of various methods. A brief overview of SOH estimation methods for lithium-ion battery modules in energy storage systems is also presented. The emerging advanced sensing methods involve perceiving internal information of batteries, offering broad prospects for applications. Then, the advantages, disadvantages and improvement perspectives of these methods are analyzed and compared to provide a reference for choosing the appropriate method when facing different problems. Finally, to promote the practical application of SOH estimation methods for lithium-ion batteries, the challenges faced by the field are presented and future research directions in the field are prospected. © 2024 Automation of Electric Power Systems Press. All rights reserved.
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页码：109 / 129
页数：20
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