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A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model

Author

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  • Gu, Xinyu
  • See, K.W.
  • Li, Penghua
  • Shan, Kangheng
  • Wang, Yunpeng
  • Zhao, Liang
  • Lim, Kai Chin
  • Zhang, Neng

Abstract

State-of-health (SOH) estimation of lithium-ion batteries is crucial for ensuring the reliability and safety of battery operation while keeping maintenance and service costs down in the long run. This study suggests a novel SOH estimation based on data pre-processing methods and a convolutional neural network (CNN)-Transformer framework. In data pre-processing, highly related features are selected by the Pearson correlation coefficient (PCC). Principal correlation analysis (PCA) is also employed to minimize the computational burden of the estimation model by eliminating redundant feature information. Then, all the features are normalized by the min-max feature scaling method, which will speed up the training process to reach the minimum cost function. After pre-processing, all the features are fed into the CNN-Transformer model. The dataset of the battery from the NASA is employed as a training and testing dataset to build the proposed model. The simulations indicate that the proposed performance, proven by absolute estimation errors for each dataset, is within 1%. The estimation performance index is proven by mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) are held within 0.55%. These show that the proposed model can estimate the battery SOH with high accuracy and stability.

Suggested Citation

  • Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pb:s0360544222023830
    DOI: 10.1016/j.energy.2022.125501
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    2. Meng, Huixing & Geng, Mengyao & Han, Te, 2023. "Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    3. Xu, Huanwei & Wu, Lingfeng & Xiong, Shizhe & Li, Wei & Garg, Akhil & Gao, Liang, 2023. "An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries," Energy, Elsevier, vol. 276(C).
    4. Xue, Jingsong & Ma, Wentao & Feng, Xiaoyang & Guo, Peng & Guo, Yaosong & Hu, Xianzhi & Chen, Badong, 2023. "Stacking integrated learning model via ELM and GRU with mixture correntropy loss for robust state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 284(C).

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