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Capacity estimation of lithium-ion batteries with uncertainty quantification based on temporal convolutional network and Gaussian process regression

Author

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  • Zhang, Ran
  • Ji, ChunHui
  • Zhou, Xing
  • Liu, Tianyu
  • Jin, Guang
  • Pan, Zhengqiang
  • Liu, Yajie

Abstract

Reliable capacity estimation is crucial for safe operation of lithium-ion batteries (LIBs). This work combines the temporal convolutional network (TCN) and Gaussian process regression (GPR) to establish a novel probabilistic capacity estimation method. The proposed TCN-GPR method can not only provide accurate capacity estimation but also quantify the uncertainty of the estimation. Besides, the TCN-GPR method can automatically extract degradation features from partial charging segments, overcoming the limitations of manual experience. In addition, the TCN-GPR method can be applied to different types of LIBs through transfer learning using only a small amount of training data. For validation, the Oxford battery dataset is used to demonstrate the accuracy and robustness of the TCN-GPR method, where a mean absolute percentage error (MAPE) of less than 0.3% can be achieved with only a 15-min partial charging segment. Furthermore, our own experimental dataset is used to demonstrate the generalization ability of the TCN-GPR method through transfer learning, where a MAPE of less than 0.7% can be achieved by using only one battery cell as the training sample.

Suggested Citation

  • Zhang, Ran & Ji, ChunHui & Zhou, Xing & Liu, Tianyu & Jin, Guang & Pan, Zhengqiang & Liu, Yajie, 2024. "Capacity estimation of lithium-ion batteries with uncertainty quantification based on temporal convolutional network and Gaussian process regression," Energy, Elsevier, vol. 297(C).
    • Handle: RePEc:eee:energy:v:297:y:2024:i:c:s0360544224009277
    DOI: 10.1016/j.energy.2024.131154
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    References listed on IDEAS

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