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

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

Energy, 2023, vol. 282, issue C

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.

Keywords: Electric vehicles; State of health; Feature extraction; Lithium-ion battery; Convolutional neural network; Support vector regression (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:282:y:2023:i:c:s0360544223016109

DOI: 10.1016/j.energy.2023.128216

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