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State of health estimation for lithium-ion batteries based on hybrid attention and deep learning

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  • Zhao, Hongqian
  • Chen, Zheng
  • Shu, Xing
  • Shen, Jiangwei
  • Lei, Zhenzhen
  • Zhang, Yuanjian

Abstract

Accurate state of health estimation of lithium-ion batteries is imperative for reliable and safe operations of electric vehicles. This study presents a hybrid attention and deep learning method for state of health prediction of lithium-ion batteries. First, the temperature difference curves are calculated from the charging data and subsequently smoothed by the Kalman filter. Next, the health features related to capacity degradation are extracted from the differential temperature curves to characterize the relationship between temperature and aging. Then, a hybrid attention and deep learning model integrating the strengths of convolutional neural network, gated recurrent unit recurrent neural network and attention mechanism is developed to forecast the battery's state of health. The superior prediction performance of the proposed method is verified by comparing with eleven mainstream methods. All the estimation errors can be maintained within 1.3% without extracting highly correlated health features, illustrating the promising accuracy and reliability of the developed state of health estimation method. In addition, the results validate that the proposed algorithm can achieve satisfied robustness to battery inconsistency.

Suggested Citation

  • Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Lei, Zhenzhen & Zhang, Yuanjian, 2023. "State of health estimation for lithium-ion batteries based on hybrid attention and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:reensy:v:232:y:2023:i:c:s0951832022006810
    DOI: 10.1016/j.ress.2022.109066
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    References listed on IDEAS

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    Cited by:

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    2. Yao, Jiachi & Han, Te, 2023. "Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data," Energy, Elsevier, vol. 271(C).
    3. Zhou, Danhua & Wang, Bin & Zhu, Chao & Zhou, Fang & Wu, Hong, 2023. "A light-weight feature extractor for lithium-ion battery health prognosis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
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    5. Che, Yunhong & Zheng, Yusheng & Forest, Florent Evariste & Sui, Xin & Hu, Xiaosong & Teodorescu, Remus, 2024. "Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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