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Lithium-ion battery capacity estimation based on battery surface temperature change under constant-current charge scenario

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  • Yang, Jufeng
  • Cai, Yingfeng
  • Mi, Chris

Abstract

Accurate estimation of battery actual capacity in real time is crucial for a reliable battery management system and the safety of electrical vehicles. In this paper, the battery capacity is estimated based on the battery surface temperature change under constant-current charge scenario. Firstly, the evolution of the smoothed differential thermal voltammetry (DTV) curves throughout the aging process is analyzed. Then, the change of the battery surface temperature, which is equivalent to the area under the DTV curve, over a specific voltage range is introduced as a direct feature of interest to reflect the battery actual capacity. In addition, the temperature variation transformation is utilized to reduce the influence of the initial battery inconsistency. Lastly, two battery degradation datasets are utilized to validate the proposed method. The maximum root mean-square errors of the estimation results by the reference correlation are less than 20 mAh and 60 mAh for the two employed batteries (respective nominal capacities are 740 mAh and 4800 mAh). Specifically, the mean estimation errors for the respective two batteries are reduced by approximately 24.74% and 39.60% after the temperature variation transformation. The proposed method is further compared with the existing DTV analysis method and demonstrates the superior performance.

Suggested Citation

  • Yang, Jufeng & Cai, Yingfeng & Mi, Chris, 2022. "Lithium-ion battery capacity estimation based on battery surface temperature change under constant-current charge scenario," Energy, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:energy:v:241:y:2022:i:c:s0360544221031285
    DOI: 10.1016/j.energy.2021.122879
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    References listed on IDEAS

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    1. Jiang, Bo & Dai, Haifeng & Wei, Xuezhe, 2020. "Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition," Applied Energy, Elsevier, vol. 269(C).
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    Citations

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    Cited by:

    1. Wang, Qiao & Ye, Min & Cai, Xue & Sauer, Dirk Uwe & Li, Weihan, 2023. "Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications," Applied Energy, Elsevier, vol. 350(C).
    2. Ospina Agudelo, Brian & Zamboni, Walter & Postiglione, Fabio & Monmasson, Eric, 2023. "Battery State-of-Health estimation based on multiple charge and discharge features," Energy, Elsevier, vol. 263(PA).
    3. Yang, Jufeng & Li, Xin & Sun, Xiaodong & Cai, Yingfeng & Mi, Chris, 2023. "An efficient and robust method for lithium-ion battery capacity estimation using constant-voltage charging time," Energy, Elsevier, vol. 263(PB).
    4. Jiang, Bo & Zhu, Yuli & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range," Energy, Elsevier, vol. 263(PC).
    5. Huaqin Zhang & Jichao Hong & Zhezhe Wang & Guodong Wu, 2022. "State-Partial Accurate Voltage Fault Prognosis for Lithium-Ion Batteries Based on Self-Attention Networks," Energies, MDPI, vol. 15(22), pages 1-14, November.

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