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An Innovative Framework for Forecasting the State of Health of Lithium-Ion Batteries Based on an Improved Signal Decomposition Method

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  • Ting Zhu

    (State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China
    Hubei Key Laboratory of Blasting Engineering, Jianghan University, Wuhan 430056, China)

  • Wenbo Wang

    (Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Yu Cao

    (School of Artificial Intelligence, Jianghan University, Wuhan 430056, China)

  • Xia Liu

    (School of Intelligent Manufacturing, Jianghan University, Wuhan 430056, China)

  • Zhongyuan Lai

    (State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China
    Hubei Key Laboratory of Blasting Engineering, Jianghan University, Wuhan 430056, China)

  • Hui Lan

    (School of Artificial Intelligence, Jianghan University, Wuhan 430056, China)

Abstract

The declining trend of battery aging has strong nonlinearity and volatility, which poses great challenges to the prediction of battery’s state of health (SOH). In this research, an innovative framework is initially put forward for SOH prediction. First, partial incremental capacity analysis (PICA) is carried out to analyze the performance degradation within a specific voltage range. Subsequently, the height of the peak, the position of the peak, and the area beneath the peak of the IC curves are retrieved and used as health features (HFs). Moreover, improved ensemble empirical mode decomposition based on fractal dimension (FEEMD) is first proposed and utilized to decompose HFs to reduce the nonlinearity and fluctuations. Additionally, a bidirectional gated recurrent unit with an attention mechanism (BiGRU-AM) is constructed for the prognosis of these sub-layers. Finally, the effectiveness and robustness of the proposed prognosis framework are validated using two battery datasets. The results of three groups of comparative experiments demonstrate that the maximum root mean squared error (RMSE) and mean absolute error (MAE) values reach merely 0.55% and 0.59%, respectively. This further demonstrates that the proposed FEEMD outperforms other benchmark models and can offer a reliable foundation for the health prognosis of lithium-ion batteries.

Suggested Citation

  • Ting Zhu & Wenbo Wang & Yu Cao & Xia Liu & Zhongyuan Lai & Hui Lan, 2025. "An Innovative Framework for Forecasting the State of Health of Lithium-Ion Batteries Based on an Improved Signal Decomposition Method," Sustainability, MDPI, vol. 17(11), pages 1-25, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:4847-:d:1664025
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    References listed on IDEAS

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