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Electrochemical Energy Reviews ›› 2025, Vol. 8 ›› Issue (2): 10-.doi: 10.1007/s41918-025-00245-0

• • 上一篇    下一篇

Advancements, Challenges, and Future Trajectories in Advanced Battery Safety Detection

Yanan Wei1,2, Min Wang3, Mengmeng Zhang1, Tao Cai4, Yunhui Huang1, Ming Xu1,2   

  1. 1. School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China;
    2. Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518057, Guangdong, China;
    3. College of New Energy, China University of Petroleum (East China), Qingdao 266580, Shandong, China;
    4. School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
  • 收稿日期:2024-11-14 修回日期:2025-01-24 出版日期:2025-06-20 发布日期:2025-11-12
  • 通讯作者: Yunhui Huang Email:E-mail:huangyh@hust.edu.cn;Ming Xu Email:E-mail:ming.xu@hust.edu.cn E-mail:huangyh@hust.edu.cn;ming.xu@hust.edu.cn
  • 基金资助:
    The authors acknowledge financial support from the National Key R&D Program of China (grant No. 2022YFB3807700), the Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China (grant No. 52472047), the Natural Science Foundation of Hubei Province, China (grant No. 2022CFA031), the Science, Technology and Innovation Commission of Shenzhen Municipality (grant No. JCYJ20210324135207020).

Advancements, Challenges, and Future Trajectories in Advanced Battery Safety Detection

Yanan Wei1,2, Min Wang3, Mengmeng Zhang1, Tao Cai4, Yunhui Huang1, Ming Xu1,2   

  1. 1. School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China;
    2. Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518057, Guangdong, China;
    3. College of New Energy, China University of Petroleum (East China), Qingdao 266580, Shandong, China;
    4. School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
  • Received:2024-11-14 Revised:2025-01-24 Online:2025-06-20 Published:2025-11-12
  • Contact: Yunhui Huang Email:E-mail:huangyh@hust.edu.cn;Ming Xu Email:E-mail:ming.xu@hust.edu.cn E-mail:huangyh@hust.edu.cn;ming.xu@hust.edu.cn
  • Supported by:
    The authors acknowledge financial support from the National Key R&D Program of China (grant No. 2022YFB3807700), the Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China (grant No. 52472047), the Natural Science Foundation of Hubei Province, China (grant No. 2022CFA031), the Science, Technology and Innovation Commission of Shenzhen Municipality (grant No. JCYJ20210324135207020).

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摘要： The widespread use of high-energy–density lithium-ion batteries (LIBs) in new energy vehicles and large-scale energy storage systems has intensified safety concerns, especially regarding the safe and reliable operation of large battery packs composed of hundreds of individual cells. This review begins with an analysis of the causes and failure mechanisms, and then continues with an examination of the many connections and influences among different factors to elucidate the complex and unpredictable issues of LIB safety. The analysis includes examples of large-scale battery failures to illustrate how failures propagate within extensive battery networks, highlighting the unique challenges associated with monitoring the safety of large-scale battery packs. Subsequently, a comparative assessment of numerous detection technologies is further conducted to underscore the challenges encountered in battery safety detection, particularly in large-scale battery systems. Additionally, the paper discusses the role of artificial intelligence (AI) in addressing battery safety concerns, explores the future trajectory of safety detection technology, and outlines the necessity and foundational framework for constructing smart battery management systems (BMSs). The discussion focuses on how AI and smart BMSs can be tailored to manage the complexities of large-scale battery packs, enabling real-time monitoring and predictive maintenance to prevent catastrophic failures.

关键词: Lithium-ion batteries, Battery safety, Safety detection technology, Battery management system, Lage battery pack

Abstract: The widespread use of high-energy–density lithium-ion batteries (LIBs) in new energy vehicles and large-scale energy storage systems has intensified safety concerns, especially regarding the safe and reliable operation of large battery packs composed of hundreds of individual cells. This review begins with an analysis of the causes and failure mechanisms, and then continues with an examination of the many connections and influences among different factors to elucidate the complex and unpredictable issues of LIB safety. The analysis includes examples of large-scale battery failures to illustrate how failures propagate within extensive battery networks, highlighting the unique challenges associated with monitoring the safety of large-scale battery packs. Subsequently, a comparative assessment of numerous detection technologies is further conducted to underscore the challenges encountered in battery safety detection, particularly in large-scale battery systems. Additionally, the paper discusses the role of artificial intelligence (AI) in addressing battery safety concerns, explores the future trajectory of safety detection technology, and outlines the necessity and foundational framework for constructing smart battery management systems (BMSs). The discussion focuses on how AI and smart BMSs can be tailored to manage the complexities of large-scale battery packs, enabling real-time monitoring and predictive maintenance to prevent catastrophic failures.

Key words: Lithium-ion batteries, Battery safety, Safety detection technology, Battery management system, Lage battery pack

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