| 引用本文: | 陈宏宇,陶志军,朱永利,等.退役动力锂离子电池健康状态及剩余使用寿命预测技术研究[J].电力系统保护与控制,2025,53(7):174-187.[点击复制] |
| CHEN Hongyu,TAO Zhijun,TAO Zhijun,et al.Research on health status and remaining useful life prediction technology for retired lithium-ion power batteries[J].Power System Protection and Control,2025,53(7):174-187[点击复制] |
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| 摘要: |
| 准确估计退役动力锂离子电池的健康状态和预测其剩余使用寿命，对保障其安全运行及促进梯次利用具有重要意义。传统的健康状态检测方法效率低，且缺乏有效评估退役电池价值的方法。基于数据驱动的人工智能方法在此领域有着独特的应用优势。从实际应用的角度出发，综述了近年来国内外在电池健康状态估计和剩余使用寿命预测方面的最新进展。首先介绍了锂电池健康状态估计以及剩余使用寿命预测方法，着重总结了基于数据驱动的剩余使用寿命预测方法的研究现状，对比了不同方法的优缺点。最后，针对当前研究中存在的关键问题提出了一些解决思路，并对未来的发展方向进行了展望。 |
| 关键词: 退役锂离子电池 健康状态估计 剩余使用寿命预测 梯次利用 |
| DOI：10.19783/j.cnki.pspc.240715 |
| 投稿时间：2024-06-10修订日期：2024-10-09 |
| 基金项目:广东省自然科学基金卓越青年团队项目资助(2023B1515040011) |
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| Research on health status and remaining useful life prediction technology for retired lithium-ion power batteries |
| CHEN Hongyu1,TAO Zhijun1,2,TAO Zhijun2,ZHU Yongli1,2,HU Renzong1,2 |
| (1. School of Materials Science and Engineering, South China University of Technology, Guangzhou 510641, China;
2. Guangdong Huajing New Energy Technology Co., Ltd., Foshan 528313, China) |
| Abstract: |
| Accurately estimating the state of health (SOH) and predicting the remaining useful life (RUL) of retired lithium-ion power batteries is of great significance for ensuring their operational safety and promoting secondary utilization. Traditional SOH detection methods are inefficient and lack effective ways for evaluating the value of retired batteries. Data-driven artificial intelligence methods offer unique advantages in this field. From a practical application perspective, this paper reviews the latest research progresses in SOH estimation and RUL prediction. First, the methods for estimating SOH and predicting RUL of lithium-ion batteries are introduced, with a focus on data-driven approaches. Then, the advantages and disadvantages of different methods are compared. Finally, key issues in current research are presented, potential solutions are proposed, and future development directions are discussed. |
| Key words: retired lithium-ion battery state of health estimation prediction of remaining useful life secondary utilization |
