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Estimation of Lithium-Ion Battery State of Health-Based Multi-Feature Analysis and Convolutional Neural Network–Long Short-Term Memory

Author

Listed:
  • Xin Ma

    (School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Xingke Ding

    (School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Chongyi Tian

    (School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Changbin Tian

    (School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Rui Zhu

    (School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China)

Abstract

Accurate estimation of battery state of health (SOH) is critical to the efficient operation of energy storage battery systems. Furthermore, precise SOH estimation methods can significantly reduce resource waste by extending the battery service life and optimizing retirement strategies, which is compatible with the sustainable development of energy systems under carbon neutrality goals. Conventional methods struggle to comprehensively characterize the health degradation properties of batteries. To address that limitation, this study proposes a data-driven model based on multi-feature analysis using a hybrid convolutional neural network and long short-term memory (CNN-LSTM) architecture, which synergistically extracts multi-dimensional degradation features to enhance SOH estimation accuracy. The framework begins by systematically collecting the voltage, current, and other parameters during charge–discharge cycles to construct a temporally resolved multi-dimensional feature matrix. A correlation analysis employing Pearson correlation coefficients subsequently identifies key health indicators strongly correlated with SOH degradation. At the same time, the K-means clustering method was adopted to identify and process the outliers of CALCE data, which ensures the high quality of data and the stability of the model. Then, CNN-LSTM hybrid neural network architecture was constructed. The experimental results demonstrated that the absolute value of MBE for the dataset provided by CALCE was less than 0.2%. The MAE was less than 0.3%, and the RMSE was less than 0.4%. Furthermore, the proposed method demonstrated a strong performance on the dataset provided by NASA PCoE. The experimental results indicated that the proposed method significantly reduced the estimation error of SOH across the entire battery lifecycle, and they fully verified the superiority and engineering applicability of the algorithm in battery SOH estimation.

Suggested Citation

  • Xin Ma & Xingke Ding & Chongyi Tian & Changbin Tian & Rui Zhu, 2025. "Estimation of Lithium-Ion Battery State of Health-Based Multi-Feature Analysis and Convolutional Neural Network–Long Short-Term Memory," Sustainability, MDPI, vol. 17(9), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:4014-:d:1645773
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    References listed on IDEAS

    as
    1. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2022. "A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction," Energy, Elsevier, vol. 251(C).
    2. Jiahui Zhao & Yong Zhu & Bin Zhang & Mingyi Liu & Jianxing Wang & Chenghao Liu & Yuanyuan Zhang, 2022. "Method of Predicting SOH and RUL of Lithium-Ion Battery Based on the Combination of LSTM and GPR," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
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    4. Wang, Jianfeng & Zuo, Zhiwen & Wei, Yili & Jia, Yongkai & Chen, Bowei & Li, Yuhan & Yang, Na, 2024. "State of charge estimation of lithium-ion battery based on GA-LSTM and improved IAKF," Applied Energy, Elsevier, vol. 368(C).
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