A Comprehensive Review of Key Technologies for Enhancing the Reliability of Lithium-Ion Power Batteries

Yue Ren, Chunhua Jin, Shu Fang, Li Yang, Zixuan Wu, Ziyang Wang, Rui Peng and Kaiye Gao ()
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Yue Ren: School of Economics and Management, Beijing Information Science & Technology University, Beijing 100192, China
Chunhua Jin: School of Economics and Management, Beijing Information Science & Technology University, Beijing 100192, China
Shu Fang: School of Economics and Management, Beijing University of Technology, Beijing 100124, China
Li Yang: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Zixuan Wu: Digital Committee, Xiamen Airlines, Xiamen 361006, China
Ziyang Wang: School of Economics and Management, Beijing Information Science & Technology University, Beijing 100192, China
Rui Peng: School of Economics and Management, Beijing University of Technology, Beijing 100124, China
Kaiye Gao: School of Economics and Management, Beijing Information Science & Technology University, Beijing 100192, China

Energies, 2023, vol. 16, issue 17, 1-38

Abstract: Fossil fuel usage has a great impact on the environment and global climate. Promoting new energy vehicles (NEVs) is essential for green and low-carbon transportation and supporting sustainable development. Lithium-ion power batteries (LIPBs) are crucial energy-storage components in NEVs, directly influencing their performance and safety. Therefore, exploring LIPB reliability technologies has become a vital research area. This paper aims to comprehensively summarize the progress in LIPB reliability research. First, we analyze existing reliability studies on LIPB components and common estimation methods. Second, we review the state-estimation methods used for accurate battery monitoring. Third, we summarize the commonly used optimization methods in fault diagnosis and lifetime prediction. Fourth, we conduct a bibliometric analysis. Finally, we identify potential challenges for future LIPB research. Through our literature review, we find that: (1) model-based and data-driven approaches are currently more commonly used in state-estimation methods; (2) neural networks and deep learning are the most prevalent methods in fault diagnosis and lifetime prediction; (3) bibliometric analysis indicates a high interest in LIPB reliability technology in China compared to other countries; (4) this research needs further development in overall system reliability, research on real-world usage scenarios, and advanced simulation and modeling techniques.

Keywords: lithium-ion power battery; reliability technologies; state estimation; fault diagnosis; lifetime prediction; bibliometric analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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