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

Liu，Yang1; Chen，Sihui2; Li，Peiyi3; Wan，Jiayu3; Li，Xin1

2024

10.1049/cps2.12086

IET Cyber-Physical Systems: Theory and Applications   影响因子和分区

2398-3396

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.

battery management systems cyber-physical systems learning (artificial intelligence)

ESCI ; EI

英语

其他

2-s2.0-85184169291

Scopus

1.Data Science Research Center,Duke Kunshan University,Kunshan,China
2.Department of Mechanical and Energy Engineering,Southern University of Science and Technology,Shenzhen,China
3.Global Institute of Future Technology,Shanghai Jiao Tong University,Shanghai,China

Liu，Yang,Chen，Sihui,Li，Peiyi,et al. Status, challenges, and promises of data-driven battery lifetime prediction under cyber-physical system context[J]. IET Cyber-Physical Systems: Theory and Applications,2024.

Liu，Yang,Chen，Sihui,Li，Peiyi,Wan，Jiayu,&Li，Xin.(2024).Status, challenges, and promises of data-driven battery lifetime prediction under cyber-physical system context.IET Cyber-Physical Systems: Theory and Applications.

Liu，Yang,et al."Status, challenges, and promises of data-driven battery lifetime prediction under cyber-physical system context".IET Cyber-Physical Systems: Theory and Applications (2024).