Knowledge Commons of Institute of Automation，CAS
| A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life | |
| Qing Xu; Min Wu; Edwin Khoo; Zhenghua Chen; Xiaoli Li | |
| Source Publication | IEEE/CAA Journal of Automatica Sinica
 |
| ISSN | 2329-9266 |
| 2023 | |
| Volume | 10Issue:1Pages:177-187 |
| Abstract | 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. |
| Keyword | Deep learning early prediction lithium-ion battery remaining useful life (RUL) |
| DOI | 10.1109/JAS.2023.123024 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50734 |
| Collection | 学术期刊_IEEE/CAA Journal of Automatica Sinica |
| Recommended Citation 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| JAS-2022-0825.pdf（6437KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View | |
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