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

doi:10.1109/JAS.2023.123024

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	A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life
	Qing Xu; Min Wu; Edwin Khoo; Zhenghua Chen; Xiaoli Li
刊名	IEEE/CAA Journal of Automatica Sinica
	2023
卷号	10 期号:1 页码:177-187
关键词	Deep learning early prediction lithium-ion battery remaining useful life (RUL)
ISSN号	2329-9266
DOI	10.1109/JAS.2023.123024
英文摘要	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.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50734]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Qing Xu,Min Wu,Edwin Khoo,et al. A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(1):177-187.
APA	Qing Xu,Min Wu,Edwin Khoo,Zhenghua Chen,&Xiaoli Li.(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.
MLA	Qing Xu,et al."A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life".IEEE/CAA Journal of Automatica Sinica 10.1(2023):177-187.