Status, challenges, and promises of data‐driven battery lifetime prediction under cyber‐physical system context

• Published in: IET cyber-physical systems Vol. 9; no. 3; pp. 207 - 217
• Main Authors: Liu, Yang, Chen, Sihui, Li, Peiyi, Wan, Jiayu, Li, Xin
• Format: Journal Article
• Language: English
• Published: Southampton John Wiley & Sons, Inc 01.09.2024; Wiley
• Subjects: Accuracy; Algorithms; Batteries; battery management systems; Context; cyber‐physical systems; Data acquisition; Data collection; Decision making; Electric vehicles; Energy consumption; Energy storage; Kalman filters; learning (artificial intelligence); Lifetime; Machine learning; Prediction models; Rechargeable batteries
• ISSN: 2398-3396; 2398-3396
• DOI: 10.1049/cps2.12086

Energy storage is playing an increasingly important role in the modern world as sustainability is becoming a critical issue. Within this domain, rechargeable battery is gaining significant popularity as it has been adopted to serve as the power supplier in a broad range of application scenarios, such as cyber‐physical system (CPS), due to multiple advantages. On the other hand, battery inspection and management solutions have been constructed based on the CPS architecture in order to guarantee the quality, reliability and safety of rechargeable batteries. In specific, lifetime prediction is extensively studied in recent research as it can help assess the quality and health status to facilitate the manufacturing and maintenance. Due to the aforementioned importance, the authors aim to conduct a comprehensive survey on the data‐driven techniques for battery lifetime prediction, including their current status, challenges and promises. In contrast to existing literature, the battery lifetime prediction methods are studied under CPS context in this survey. Hence, the authors focus on the algorithms for lifetime prediction as well as the engineering frameworks that enable the data acquisition and deployment of prediction models in CPS systems. Through this survey, the authors intend to investigate both academic and practical values in the domain of battery lifetime prediction to benefit both researchers and practitioners. The authors aim to conduct a comprehensive survey on the data‐driven techniques for battery lifetime prediction, including their current status, challenges and promises. In particular, the authors focus on the algorithms for lifetime prediction as well as the engineering frameworks that enable the data acquisition and deployment of prediction models in CPS systems. Through this survey, the authors intend to investigate both academic and practical values in the domain of battery lifetime prediction to benefit both researchers and practitioners.