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Health Factor Extraction of Lithium-Ion Batteries Based on Discrete Wavelet Transform and SOH Prediction Based on CatBoost

Author

Listed:
  • Mei Zhang

    (College of Electrical and Information Engineering, Anhui University of Science and Technology (AUST), Huainan 232001, China)

  • Wanli Chen

    (College of Electrical and Information Engineering, Anhui University of Science and Technology (AUST), Huainan 232001, China)

  • Jun Yin

    (College of Electrical and Information Engineering, Anhui University of Science and Technology (AUST), Huainan 232001, China)

  • Tao Feng

    (College of Electrical and Information Engineering, Anhui University of Science and Technology (AUST), Huainan 232001, China)

Abstract

Aiming to accurately identify the state of health (SOH) and the remaining useful life (RUL) of lithium-ion batteries, in this paper, we propose an algorithm for the health factor extraction and SOH prediction of the batteries based on discrete wavelet transform and the Cauchy–Gaussian variation tent sparrow search algorithm (DWT-CGTSSA). Firstly, concerning the inconsistent data length, discrete wavelet transform (DWT) was adopted to decompose the battery’s signals and extract features. Then, the Cauchy–Gaussian variation tent sparrow search algorithm (CGTSSA) was utilized to extract features and obtain the optimal feature subset after encoding. Finally, the optimal feature subset was used to establish a prediction model based on CatBoost for predicting the SOH of lithium-ion batteries. Experiments were conducted for verification. The experimental results showed that the model established in this research is capable of realizing the prediction between different battery packs. The B0005 battery from dataset A was taken as the training set to predict the complete SOH of B0006 and B0007 batteries. For the prediction model of CGTSSA-CatBoost, the goodness of fit (R 2 ) exceeded 0.99, and the value of mean square error (MSE) was less than 1‰. A comparison with other state-of-the-art prediction models verified the superior performance of the CGTSSA-CatBoost model. Under different working conditions, the R 2 of all models in dataset B exceeded 0.98.

Suggested Citation

  • Mei Zhang & Wanli Chen & Jun Yin & Tao Feng, 2022. "Health Factor Extraction of Lithium-Ion Batteries Based on Discrete Wavelet Transform and SOH Prediction Based on CatBoost," Energies, MDPI, vol. 15(15), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5331-:d:869434
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    References listed on IDEAS

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    1. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
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    3. Shuxiang Song & Chen Fei & Haiying Xia, 2020. "Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction," Energies, MDPI, vol. 13(4), pages 1-13, February.
    4. Chen, Zewang & Shi, Na & Ji, Yufan & Niu, Mu & Wang, Youren, 2021. "Lithium-ion batteries remaining useful life prediction based on BLS-RVM," Energy, Elsevier, vol. 234(C).
    5. Sungwoo Jo & Sunkyu Jung & Taemoon Roh, 2021. "Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge," Energies, MDPI, vol. 14(21), pages 1-16, November.
    6. Claudio Rossi & Carlo Falcomer & Luca Biondani & Davide Pontara, 2022. "Genetically Optimized Extended Kalman Filter for State of Health Estimation Based on Li-Ion Batteries Parameters," Energies, MDPI, vol. 15(9), pages 1-18, May.
    7. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
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    Cited by:

    1. Ko, Chi-Jyun & Chen, Kuo-Ching, 2024. "Using tens of seconds of relaxation voltage to estimate open circuit voltage and state of health of lithium ion batteries," Applied Energy, Elsevier, vol. 357(C).

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