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Big data-driven prognostics and health management of lithium-ion batteries:A review

Author

Listed:
  • Chen, Kui
  • Luo, Yang
  • Long, Zhou
  • Li, Yang
  • Nie, Guangbo
  • Liu, Kai
  • Xin, Dongli
  • Gao, Guoqiang
  • Wu, Guangning

Abstract

As the preferred green energy storage solution for the transition to renewable and sustainable energy sources, the prognostics and health management (PHM) of lithium-ion batteries play a crucial role in enhancing energy utilization efficiency, optimizing battery maintenance, and accurately detecting health degradation while predicting remaining useful life (RUL). With the rapid advancement of artificial intelligence(AI) and big data technologies, data-driven approaches have gained widespread adoption in the field of battery PHM due to their high accuracy, simplicity, and efficiency. This review provides a comprehensive analysis of the fundamental steps involved in data-driven battery PHM systems, including an in-depth examination of key aspects such as data acquisition, feature parameter construction, and diagnostic methods. The review further highlights prominent research trends rooted in data-driven approaches. Moreover, this study aims to propose novel methodologies and insights that describe the system behaviors of battery aging at both physical and mathematical scales. Ultimately, this work introduces new perspectives and techniques for battery PHM, expanding its applicability and offering valuable guidance for the on-board implementation of PHM systems.

Suggested Citation

  • Chen, Kui & Luo, Yang & Long, Zhou & Li, Yang & Nie, Guangbo & Liu, Kai & Xin, Dongli & Gao, Guoqiang & Wu, Guangning, 2025. "Big data-driven prognostics and health management of lithium-ion batteries:A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:rensus:v:214:y:2025:i:c:s1364032125001959
    DOI: 10.1016/j.rser.2025.115522
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