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A comprehensive review of DC arc faults and their mechanisms, detection, early warning strategies, and protection in battery systems

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  • Xu, Wenqiang
  • Wu, Xiaogang
  • Li, Yalun
  • Wang, Hewu
  • Lu, Languang
  • Ouyang, Minggao

Abstract

With the active promotion of green, low-carbon, and intelligent strategies in the energy sector, the application of battery systems such as electric vehicles and energy storage stations is becoming increasingly widespread globally. However, it has also resulted in a higher frequency of DC electrical safety incidents. DC arc faults caused by mechanical collisions, loose connections, and insulation damage, among other things, have become one of the leading causes of battery system safety accidents. Currently, there is a lack of in-depth and comprehensive research on arc faults specifically in battery systems. To ensure the safe operation of batteries and other system components, battery systems must have fast, effective, and reliable protection measures. This review comprehensively reviews DC arc fault detection, early warning strategies, and protection technologies in battery systems. Methods used for fault detection are discussed and compared in detail. Timely and accurate warning strategies are summarized, and protective measures against arc-causing disasters are outlined. Some perspectives on future research are presented. This review is of great significance in guiding future improvements in addressing arc fault issues in battery systems.

Suggested Citation

  • Xu, Wenqiang & Wu, Xiaogang & Li, Yalun & Wang, Hewu & Lu, Languang & Ouyang, Minggao, 2023. "A comprehensive review of DC arc faults and their mechanisms, detection, early warning strategies, and protection in battery systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:rensus:v:186:y:2023:i:c:s1364032123005312
    DOI: 10.1016/j.rser.2023.113674
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    1. Wang, Fengfei & Tang, Shengjin & Han, Xuebing & Yu, Chuanqiang & Sun, Xiaoyan & Lu, Languang & Ouyang, Minggao, 2024. "Capacity prediction of lithium-ion batteries with fusing aging information," Energy, Elsevier, vol. 293(C).

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