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Prediction of Lithium Battery Health State Based on Temperature Rate of Change and Incremental Capacity Change

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  • Tao Zhang

    (College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China)

  • Yang Wang

    (College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China)

  • Rui Ma

    (College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China)

  • Yi Zhao

    (School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China)

  • Mengjiao Shi

    (Key Laboratory of Bio-Based Material Science and Technology, Ministry of Education, Northeast Forestry University, Harbin 150040, China)

  • Wen Qu

    (College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China)

Abstract

With the use of Li-ion batteries, Li-ion batteries will experience unavoidable aging, which can cause battery safety issues, performance degradation, and inaccurate SOC estimation, so it is necessary to predict the state of health (SOH) of Li-ion batteries. Existing methods for Li-ion battery state of health assessment mainly focus on parameters such as constant voltage charging time, constant current charging time, and discharging time, with little consideration of the impact of changes in Li-ion battery temperature on the state of health of Li-ion batteries. In this paper, a new prediction method for Li-ion battery health state based on the surface difference temperature (DT), incremental capacity analysis (ICA), and differential voltage analysis (DVA) is proposed. Five health factors are extracted from each of the three curves as input features to the model, respectively, and the weights, thresholds, and number of hidden layers of the Elman neural network are optimized using the Whale of a Whale Algorithm (WOA), which results in an average decrease of 43%, 49%, and 46% in MAE, RMSE, and MAPE compared to the Elman neural network. For the problem where the three predictions depend on different sources, the features of the three curves are fused using the weighted average method and predicted using the WOA–Elman neural network, whose MAE, RMSE, and MAPE are 0.00054, 0.0007897, and 0.06547% on average. The results show that the proposed method has an overall error of less than 2% in SOH prediction, improves the accuracy and robustness of the overall SOH estimation, and reduces the computational burden to some extent.

Suggested Citation

  • Tao Zhang & Yang Wang & Rui Ma & Yi Zhao & Mengjiao Shi & Wen Qu, 2023. "Prediction of Lithium Battery Health State Based on Temperature Rate of Change and Incremental Capacity Change," Energies, MDPI, vol. 16(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7581-:d:1280083
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    References listed on IDEAS

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    1. Stefano Leonori & Luca Baldini & Antonello Rizzi & Fabio Massimo Frattale Mascioli, 2021. "A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells," Energies, MDPI, vol. 14(21), pages 1-29, November.
    2. Yang, Fangfang & Wang, Dong & Zhao, Yang & Tsui, Kwok-Leung & Bae, Suk Joo, 2018. "A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries," Energy, Elsevier, vol. 145(C), pages 486-495.
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