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High-accuracy state of health estimation for lithium-ion batteries via current fluctuation analysis and multi-feature fusion

Author

Listed:
  • Chen, Mengyang
  • Chen, Xueyang
  • Fang, Weiwei
  • Hou, Yuyang
  • Liu, Lili
  • Ye, Jilei
  • Wu, Yuping

Abstract

This study introduces a novel state of health (SOH) estimation framework for lithium-ion batteries, leveraging current fluctuation dynamics to enhance prediction accuracy. By systematically analyzing charge/discharge cycle data, we extract key fluctuation-based features, including voltage slope variation, current power spectral density (PSD), temperature effects, and signal energy parameters. These features are integrated into a multi-dimensional input set to train both individual and ensemble machine learning models. Our results demonstrate that incorporating current fluctuation characteristics significantly enhances SOH estimation performance, reducing root-mean-square error (RMSE) by over 50 % while achieving R2 values exceeding 0.8. The proposed hybrid iTransformer-Long Short Term Memory-Random Forest (iTransformer-LSTM-RF) achieves exceptional precision, with an R2 of 0.954 and an RMSE of 0.0035 on cell B0005. Furthermore, the method exhibits robust generalization across multiple sibling cells (B0005, B0006, B0007, B0018) and diverse operating conditions, maintaining R2 > 0.81 and RMSE <0.015. This approach not only advances high-accuracy battery health monitoring but also provides new insights into aging mechanisms through fluctuation dynamics, offering significant potential for practical battery management systems.

Suggested Citation

  • Chen, Mengyang & Chen, Xueyang & Fang, Weiwei & Hou, Yuyang & Liu, Lili & Ye, Jilei & Wu, Yuping, 2025. "High-accuracy state of health estimation for lithium-ion batteries via current fluctuation analysis and multi-feature fusion," Energy, Elsevier, vol. 339(C).
    • Handle: RePEc:eee:energy:v:339:y:2025:i:c:s0360544225047450
    DOI: 10.1016/j.energy.2025.139103
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    References listed on IDEAS

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