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

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

IEEE/CAA Journal of Automatica Sinica

10.1109/JAS.2023.123024

http://dx.doi.org/10.1109/jas.2023.123024

Qing Xu, Min Wu, Edwin Khoo, Zhenghua Chen, Xiaoli Li

artificial intelligence, Information Systems, Control and Systems Engineering, Control and Optimization

06 January 2023

Xu, Q., Wu, M., Khoo, E., Chen, Z., & Li, X. (2023). A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life. IEEE/CAA Journal of Automatica Sinica, 10(1), 177–187. https://doi.org/10.1109/jas.2023.123024

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

Publisher Copyright

This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Career Development Fund
Grant Reference no. : C210112037

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