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A flexible RUL prediction method based on poly-cell LSTM with applications to lithium battery data

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  • Wang, Jiaolong
  • Zhang, Fode
  • Zhang, Jianchuan
  • Liu, Wen
  • Zhou, Kuang

Abstract

Remaining useful life estimation is one of the major prognostic targets in the battery management system. Since the degradation process of the lithium battery is a complex nonlinear process, including temporary capacity regeneration as well as noise interference, the deep learning model is a popular method to discuss the remaining useful life prediction. However, most deep learning networks, including the convolutional neural network, deep belief network, recurrent neural network, and their variants, cannot process input data with different update states according to data importance. In order to improve the prediction accuracy of RUL, this paper proposes a novel model named Poly-Cell Long Short-Term Memory Network, which adds a hierarchical division unit and a poly-cell unit The model determines the importance of the input data through the hierarchical division unit and then uses the poly-cell unit to update the cell state according to the features’ importance.The proposed RUL prediction method is illustrated by using the lithium battery data. The experimental results show the efficiency of the proposed prediction approach.

Suggested Citation

  • Wang, Jiaolong & Zhang, Fode & Zhang, Jianchuan & Liu, Wen & Zhou, Kuang, 2023. "A flexible RUL prediction method based on poly-cell LSTM with applications to lithium battery data," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022005919
    DOI: 10.1016/j.ress.2022.108976
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    2. Zhou, Danhua & Wang, Bin & Zhu, Chao & Zhou, Fang & Wu, Hong, 2023. "A light-weight feature extractor for lithium-ion battery health prognosis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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