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| Research on health status and remaining useful life prediction technology for retired lithium-ion power batteries |
| DOI：10.19783/j.cnki.pspc.240715 |
| Key Words:retired lithium-ion battery state of health estimation prediction of remaining useful life secondary utilization |
| Author Name | Affiliation | | CHEN Hongyu1 | 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 | | TAO Zhijun1,2 | 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 | | TAO Zhijun2 | 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 | | ZHU Yongli1,2 | 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 | | 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 |
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| 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. |
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