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Health estimation of lithium-ion batteries with voltage reconstruction and fusion model

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  • Liu, Xinghua
  • Li, Siqi
  • Tian, Jiaqiang
  • Wei, Zhongbao
  • Wang, Peng

Abstract

Accurate state of health (SOH) estimation is crucial for Lithium-ion battery in electric vehicles (EVs). This work proposes a battery SOH estimation method based on voltage reconstruction and fusion models. Firstly, a voltage curve reconstruction method based on importance sampling is proposed to solve the V–Q curve. Then, feature factors related to SOH are extracted and their correlation with SOH is analyzed. Furthermore, a SOH estimation fusion model is established based on improved Support Vector Regression (SVR) and Convolutional Neural Network (CNN). Finally, the accuracy of the proposed algorithm is verified in 20% and 30% small sample scenarios, respectively. The experimental results show that the numerical evaluation indicators of the proposed method are superior to Gauss Process Regression (GPR), CNN, whale optimization algorithm-SVR (WOA-SVR) and Long Short Term Memory (LSTM) neural network, which indicates that the proposed method has good performance.

Suggested Citation

  • Liu, Xinghua & Li, Siqi & Tian, Jiaqiang & Wei, Zhongbao & Wang, Peng, 2023. "Health estimation of lithium-ion batteries with voltage reconstruction and fusion model," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223016109
    DOI: 10.1016/j.energy.2023.128216
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    References listed on IDEAS

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