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State of health estimation of lithium-ion battery based on constant current charging time feature extraction and internal resistance compensation

Author

Listed:
  • Sun, Jinlei
  • Liu, Xinwei
  • Li, Xin
  • Chen, Siwen
  • Xing, Shiyou
  • Guo, Yilong

Abstract

Existing state of health (SOH) estimation methods for lithium-ion batteries generally require complete charge-discharge curves or involve complex algorithms and computational processes. To address this issue, a lithium-ion battery health state estimation method considering internal resistance compensation is proposed in this paper. The feature of Constant Current Charging Time (CCCT) proposed in this paper is derived from the open-circuit voltage (OCV) curve, replacing the commonly used incremental capacity at the voltage peak, thereby eliminating the need for complex calculations. By compensating for the battery charging voltage curve using internal resistance, the method mitigates the impact of Incremental Capacity (IC) curve shifts caused by different charging rates on the CCCT feature. Pearson correlation coefficients are applied to optimize the length and position of voltage segments in the charging voltage curve. Additionally, the gradient boosting regression tree algorithm is utilized to achieve SOH estimation. The effectiveness of the proposed SOH estimation method is validated. Experimental results show that the Mean Absolute Error (MAE) values of the proposed SOH estimation method are 3.31 %, 2.67 %, 1.79 %, and 1.28 % for voltage segments of 10 mV, 25 mV, 50 mV, and 100 mV, respectively.

Suggested Citation

  • Sun, Jinlei & Liu, Xinwei & Li, Xin & Chen, Siwen & Xing, Shiyou & Guo, Yilong, 2025. "State of health estimation of lithium-ion battery based on constant current charging time feature extraction and internal resistance compensation," Energy, Elsevier, vol. 315(C).
    • Handle: RePEc:eee:energy:v:315:y:2025:i:c:s0360544225001148
    DOI: 10.1016/j.energy.2025.134472
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