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Lithium-ion battery state-of-health estimation: A self-supervised framework incorporating weak labels

Author

Listed:
  • Wang, Tianyu
  • Ma, Zhongjing
  • Zou, Suli
  • Chen, Zhan
  • Wang, Peng

Abstract

The State-of-Health (SOH) estimation of Lithium-ion (Li-ion) batteries is critical for the safe and reliable operation of the batteries. Deep learning technologies are currently the popular methods for SOH estimation due to the advantages of no modeling and automatic feature extraction. However, existing methods require a large amount of annotated data to ensure model fitting, and the collection and labeling of battery aging data are time-consuming and laborious. Therefore, a self-supervised framework incorporating weak labels (SSF-WL) is proposed in this paper to obtain excellent estimation results on a small amount of annotated data. First, a novel data processing method based on the Gramian angular field, difference calculation, and raw data is proposed to enrich information and enhance features. Then, a five-layer Transformer encoder is constructed in SSF-WL for feature extraction. Finally, the model is pre-trained and fine-tuned on the proposed SSF-WL to obtain the estimated results of SOH. The proposed method is validated on the 124 commercial battery and Oxford databases. Experiments indicate that when using only 30% of the annotated training data, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) obtained by SSF-WL are 0.5219%/0.6085% lower than traditional supervised learning on the 124 commercial battery database, respectively. Moreover, the SSF-WL pre-trained model on a large unannotated database can be transferred to different types of batteries with a small annotated database and obtains on-par or better estimation results than the model trained from scratch.

Suggested Citation

  • Wang, Tianyu & Ma, Zhongjing & Zou, Suli & Chen, Zhan & Wang, Peng, 2024. "Lithium-ion battery state-of-health estimation: A self-supervised framework incorporating weak labels," Applied Energy, Elsevier, vol. 355(C).
    • Handle: RePEc:eee:appene:v:355:y:2024:i:c:s0306261923016963
    DOI: 10.1016/j.apenergy.2023.122332
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    References listed on IDEAS

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