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Volume 10 Issue 1
Jan.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Q. Xu, M. Wu, E. Khoo, Z. H. Chen, and  X. L. Li,  “A hybrid ensemble deep learning approach for early prediction of battery remaining useful life,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 177–187, Jan. 2023. doi: 10.1109/JAS.2023.123024
Citation: Q. Xu, M. Wu, E. Khoo, Z. H. Chen, and  X. L. Li,  “A hybrid ensemble deep learning approach for early prediction of battery remaining useful life,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 177–187, Jan. 2023. doi: 10.1109/JAS.2023.123024

A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life

doi: 10.1109/JAS.2023.123024
Funds:  This work was supported by Agency for Science, Technology and Research (A*STAR) under the Career Development Fund (C210112037)
More Information
  • Accurate estimation of the remaining useful life (RUL) of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage. A fundamental understanding of the factors affecting RUL is crucial for accelerating battery technology development. However, it is very challenging to predict RUL accurately because of complex degradation mechanisms occurring within the batteries, as well as dynamic operating conditions in practical applications. Moreover, due to insignificant capacity degradation in early stages, early prediction of battery life with early cycle data can be more difficult. In this paper, we propose a hybrid deep learning model for early prediction of battery RUL. The proposed method can effectively combine handcrafted features with domain knowledge and latent features learned by deep networks to boost the performance of RUL early prediction. We also design a non-linear correlation-based method to select effective domain knowledge-based features. Moreover, a novel snapshot ensemble learning strategy is proposed to further enhance model generalization ability without increasing any additional training cost. Our experimental results show that the proposed method not only outperforms other approaches in the primary test set having a similar distribution as the training set, but also generalizes well to the secondary test set having a clearly different distribution with the training set. The PyTorch implementation of our proposed approach is available at

    https://github.com/batteryrul/battery_rul_early_prediction

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    Highlights

    • An innovative hybrid deep model is proposed for early prediction of Lithium-ion battery remaining useful life
    • A non-linear correlation-based approach is proposed for feature selection from excessive domain knowledge-based features
    • A novel snapshot ensemble learning strategy upon the proposed deep learning framework is developed to further enhance the generalization capabilities of deep model
    • State-of-the-art performance have been achieved on the A123 battery dataset

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