Li Mei, Xu Wenting, Zhang Shiwen, Liu Lina, Hussain Arif, Hu Enlai, Zhang Jing, Mao Zhiyu, Chen Zhongwei
College of Chemistry and Materials Science, Zhejiang Normal University, 688 Yingbin Avenue, Jinhua 321004, China.
Power Battery & System Research Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
Materials (Basel). 2025 Jan 2;18(1):145. doi: 10.3390/ma18010145.
Lithium-ion batteries are a key technology for addressing energy shortages and environmental pollution. Assessing their health is crucial for extending battery life. When estimating health status, it is often necessary to select a representative characteristic quantity known as a health indicator. Most current research focuses on health indicators associated with decreased capacity and increased internal resistance. However, due to the complex degradation mechanisms of lithium-ion batteries, the relationship between these mechanisms and health indicators has not been fully explored. This paper reviews a large number of literature sources. We discuss the application scenarios of different health factors, providing a reference for selecting appropriate health factors for state estimation. Additionally, the paper offers a brief overview of the models and machine learning algorithms used for health state estimation. We also delve into the application of health indicators in the health status assessment of battery management systems and emphasize the importance of integrating health factors with big data platforms for battery status analysis. Furthermore, the paper outlines the prospects for future development in this field.
锂离子电池是解决能源短缺和环境污染问题的一项关键技术。评估其健康状况对于延长电池寿命至关重要。在估计健康状态时，通常需要选择一个被称为健康指标的代表性特征量。当前大多数研究都集中在与容量下降和内阻增加相关的健康指标上。然而，由于锂离子电池复杂的退化机制，这些机制与健康指标之间的关系尚未得到充分探索。本文综述了大量文献来源。我们讨论了不同健康因素的应用场景，为状态估计选择合适的健康因素提供参考。此外，本文简要概述了用于健康状态估计的模型和机器学习算法。我们还深入探讨了健康指标在电池管理系统健康状态评估中的应用，并强调将健康因素与大数据平台集成以进行电池状态分析的重要性。此外，本文概述了该领域未来的发展前景。
