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Cell-level online electrochemical impedance spectrum measurement towards advanced management for large-capacity commercial lithium iron phosphate batteries on energy storage: an engineering practice

Author

Listed:
  • Gao, Zichao
  • Hou, Zhaozheng
  • Xiong, Cheng
  • Li, Fubing
  • Luo, Zixuan
  • Chen, Jian
  • Xiao, Lingyun
  • Dong, Honglei
  • Li, Qiangwei
  • Zhou, Sida
  • Yang, Shichun

Abstract

The electrochemical impedance spectrum (EIS) is recognized to be an effective method for the next-generation battery management system, but it is far from engineering due to the cell-level exciting sources and other technical problems. In this article, the online electrochemical impedance spectrum based battery management system is designed for engineering practice, including a master board and different kinds of slave boards with the application of online EIS sample. And a 8-serial connected 280 Ah lithium iron phosphate (LFP) battery module is designed for validating the engineer-based BMS. Further, a multi-featured model-inspired diagnosis strategy coupling 11 features and more than 20 rules is proposed with the diverse abuse conditional validation, especially for the advanced early-warning of thermal runaway compared against voltage/temperature-based methods. Furthermore, the proposed method can also help find out the battery safe operational boundary and to avoid the over-charging and over-discharging. Finally, the voltage-independent state-of-charge correction method based on EIS is also discussed, which help provide the innovative view for online SOC estimation for LFP battery under the voltage plateau period. It hopes that the article may provide the view from engineering on EIS, and help advance the progress of associating research.

Suggested Citation

  • Gao, Zichao & Hou, Zhaozheng & Xiong, Cheng & Li, Fubing & Luo, Zixuan & Chen, Jian & Xiao, Lingyun & Dong, Honglei & Li, Qiangwei & Zhou, Sida & Yang, Shichun, 2025. "Cell-level online electrochemical impedance spectrum measurement towards advanced management for large-capacity commercial lithium iron phosphate batteries on energy storage: an engineering practice," Energy, Elsevier, vol. 337(C).
    • Handle: RePEc:eee:energy:v:337:y:2025:i:c:s0360544225040812
    DOI: 10.1016/j.energy.2025.138439
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    References listed on IDEAS

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    1. Michael Hess & Tsuyoshi Sasaki & Claire Villevieille & Petr Novák, 2015. "Combined operando X-ray diffraction–electrochemical impedance spectroscopy detecting solid solution reactions of LiFePO4 in batteries," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
    2. Penelope K. Jones & Ulrich Stimming & Alpha A. Lee, 2022. "Impedance-based forecasting of lithium-ion battery performance amid uneven usage," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
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    5. Yunwei Zhang & Qiaochu Tang & Yao Zhang & Jiabin Wang & Ulrich Stimming & Alpha A. Lee, 2020. "Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
    6. Yuan, Yongjun & Jiang, Bo & Chen, Qinpin & Wang, Xueyuan & Wei, Xuezhe & Dai, Haifeng, 2025. "A comparative study of battery state-of-charge estimation using electrochemical impedance spectroscopy by different machine learning methods," Energy, Elsevier, vol. 328(C).
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