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Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries

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  • Ding, Pan
  • Liu, Xiaojuan
  • Li, Huiqin
  • Huang, Zequan
  • Zhang, Ke
  • Shao, Long
  • Abedinia, Oveis

Abstract

It is important to know the replace time for reducing the lithium-ion battery risk and assessing its reliability. For this purpose, the remaining useful life (RUL) can play an important role in the prognostics and health management of battery to solve the inaccurate prediction issue. The existing RUL prediction techniques for lithium-ion batteries are inefficient for learning long-term dependencies among capacity degradations. In this work, a new forecasting approach is proposed based on wavelet packet decomposition, two-dimensional convolutional neural network, and adaptive multiple error corrections. In this model, the bivariate Dirichlet mixture model is considered to make the heteroscedasticity of the unpredictable residuals signal based non-parametric distribution. To show the validity of the proposed model, the experimental data are considered based on Continental Europe and NASA Ames Prognostics Center of Excellence battery datasets. The obtained numerical analysis presents an accurate forecasting model. Different comparisons with the well-known models are made to show the validity of the suggested approach, which proves the superiority and forecasting stability of the proposed model.

Suggested Citation

  • Ding, Pan & Liu, Xiaojuan & Li, Huiqin & Huang, Zequan & Zhang, Ke & Shao, Long & Abedinia, Oveis, 2021. "Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
  • Handle: RePEc:eee:rensus:v:148:y:2021:i:c:s1364032121005748
    DOI: 10.1016/j.rser.2021.111287
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    Cited by:

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    3. Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    4. Li, Yang & Wang, Shunli & Chen, Lei & Qi, Chuangshi & Fernandez, Carlos, 2023. "Multiple layer kernel extreme learning machine modeling and eugenics genetic sparrow search algorithm for the state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 282(C).
    5. Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    6. Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Two-stage prediction method for capacity aging trajectories of lithium-ion batteries based on Siamese-convolutional neural network," Energy, Elsevier, vol. 295(C).
    7. Zhou, Yifei & Wang, Shunli & Xie, Yanxing & Zeng, Jiawei & Fernandez, Carlos, 2024. "Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm," Energy, Elsevier, vol. 300(C).
    8. Chunxiang Zhu & Zhiwei He & Zhengyi Bao & Changcheng Sun & Mingyu Gao, 2023. "Prognosis of Lithium-Ion Batteries’ Remaining Useful Life Based on a Sequence-to-Sequence Model with Variational Mode Decomposition," Energies, MDPI, vol. 16(2), pages 1-16, January.
    9. Zhong Huang & Linna Li & Guorong Ding, 2023. "A Daily Air Pollutant Concentration Prediction Framework Combining Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Network," Sustainability, MDPI, vol. 15(13), pages 1-22, July.
    10. 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).
    11. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    12. Yao, Fang & He, Wenxuan & Wu, Youxi & Ding, Fei & Meng, Defang, 2022. "Remaining useful life prediction of lithium-ion batteries using a hybrid model," Energy, Elsevier, vol. 248(C).
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