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State of Health Estimations for Lithium-Ion Batteries Based on MSCNN

Author

Listed:
  • Jiwei Wang

    (School of Electrical Engineering, Xinjiang University, Urumqi 830046, China)

  • Hao Li

    (School of Electrical Engineering, Xinjiang University, Urumqi 830046, China)

  • Chunling Wu

    (School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China)

  • Yujun Shi

    (School of Electrical Engineering, Xinjiang University, Urumqi 830046, China)

  • Linxuan Zhang

    (Department of Automation, Tsinghua University, Beijing 100084, China)

  • Yi An

    (School of Electrical Engineering, Xinjiang University, Urumqi 830046, China
    School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China)

Abstract

Lithium-ion batteries, essential components in new energy vehicles and energy storage stations, play a crucial role in health-status investigation and ensuring safe operation. To address challenges such as limited estimation accuracy and a weak generalization ability in conventional battery state of health (SOH) estimation methods, this study presents an integrated approach for SOH estimation that incorporates multiple health indicators and utilizes the multi-scale convolutional neural network (MSCNN) model. Initially, the aging characteristics of the battery are comprehensively analyzed, and then the health indicators are extracted from the charging data, including the temperature, time, current, voltage, etc., and the statistical transformation is performed. Subsequently, Pearson’s method is employed to analyze the correlation between these health indicators and identify those with strong correlations. A regression-prediction model based on the MSCNN model is then developed for estimating battery SOH. Finally, validation using a publicly available lithium-ion battery dataset demonstrates that, under similar operating conditions, the mean absolute error (MAE) for SOH estimation is less than 0.67%, the mean absolute percentage error (MAPE) is less than 0.37%, and the root mean square error (RMSE) is less than 0.74%. The MSCNN has good generalization for datasets with different working conditions.

Suggested Citation

  • Jiwei Wang & Hao Li & Chunling Wu & Yujun Shi & Linxuan Zhang & Yi An, 2024. "State of Health Estimations for Lithium-Ion Batteries Based on MSCNN," Energies, MDPI, vol. 17(17), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4220-:d:1462844
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    References listed on IDEAS

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    1. Deng, Zhongwei & Hu, Xiaosong & Lin, Xianke & Che, Yunhong & Xu, Le & Guo, Wenchao, 2020. "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression," Energy, Elsevier, vol. 205(C).
    2. S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
    3. Yang, Yixin, 2021. "A machine-learning prediction method of lithium-ion battery life based on charge process for different applications," Applied Energy, Elsevier, vol. 292(C).
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    Cited by:

    1. Cai, Hongchang & Tang, Xiaopeng & Lai, Xin & Wang, Yanan & Han, Xuebing & Ouyang, Minggao & Zheng, Yuejiu, 2024. "How battery capacities are correctly estimated considering latent short-circuit faults," Applied Energy, Elsevier, vol. 375(C).
    2. Bing Chen & Yongjun Zhang & Jinsong Wu & Hongyuan Yuan & Fang Guo, 2025. "Lithium-Ion Battery State of Health Estimation Based on Feature Reconstruction and Transformer-GRU Parallel Architecture," Energies, MDPI, vol. 18(5), pages 1-19, March.
    3. Liang Zhang & Qizhi Wu & Longfei Wang & Ling Lyu & Linru Jiang & Yu Shi, 2025. "Research on GRU-LSTM-Based Early Warning Method for Electric Vehicle Lithium-Ion Battery Voltage Fault Classification," Energies, MDPI, vol. 18(6), pages 1-25, March.

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