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A hybrid method for prognostics of lithium-ion batteries capacity considering regeneration phenomena

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  • Meng, Huixing
  • Geng, Mengyao
  • Xing, Jinduo
  • Zio, Enrico

Abstract

Prognostics and health management (PHM) is crucial to the reliability and safety of lithium-ion batteries. In this respect, the capacity regeneration phenomenon that occurs during the process of battery degradation brings a challenge to the accuracy of capacity prediction. In this paper, a hybrid method is proposed for the accurate prediction of lithium-ion batteries capacity considering regeneration. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to decompose the raw capacity signal into the global degradation trend components and the local fluctuation components. Then, each component is separately fed to the adaptive neuro-fuzzy inference system (ANFIS) for prediction. Finally, the individual outputs of the ANFIS models are recomposed to obtain the ultimate prediction results. The proposed method is validated by application to NASA lithium-ion battery experimental data. The results obtained show that the proposed method can obtain satisfactory prediction accuracy, wherein the negative impact of capacity regeneration on the prediction accuracy is reduced.

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  • Meng, Huixing & Geng, Mengyao & Xing, Jinduo & Zio, Enrico, 2022. "A hybrid method for prognostics of lithium-ion batteries capacity considering regeneration phenomena," Energy, Elsevier, vol. 261(PB).
    • Handle: RePEc:eee:energy:v:261:y:2022:i:pb:s0360544222021636
    DOI: 10.1016/j.energy.2022.125278
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    7. Mingsan Ouyang & Peicheng Shen, 2022. "Prediction of Remaining Useful Life of Lithium Batteries Based on WOA-VMD and LSTM," Energies, MDPI, vol. 15(23), pages 1-20, November.
    8. Seo, Younggeon & Kim, Taeyi & Barde, Stephane, 2025. "Enhancing battery SOH prediction with Butler–Volmer informed neural networks in data-scarce environments," Energy, Elsevier, vol. 335(C).
    9. Hairui Wang & Xin Ye & Yuanbo Li & Guifu Zhu, 2023. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    10. Xuliang Tang & Heng Wan & Weiwen Wang & Mengxu Gu & Linfeng Wang & Linfeng Gan, 2023. "Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    11. Yao, Jiachi & Chang, Zhonghao & Han, Te & Tian, Jingpeng, 2024. "Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems," Energy, Elsevier, vol. 294(C).
    12. Yin, Xiuxian & He, Wei & Cao, You & Ma, Ning & Zhou, Guohui & Li, Hongyu, 2024. "A new health state assessment method based on interpretable belief rule base with bimetric balance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
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