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Volume 15
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Article

Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Algorithm and Fusion of Multiple Health Features

by
Guoju Liu
1,
Zhihui Deng
2,
Yonghong Xu
1,*,
Lianfeng Lai
2,*,
Guoqing Gong
1,
Liang Tong
1,
Hongguang Zhang
3,
Yiyang Li
1,
Minghui Gong
1,
Mengxiang Yan
1 and
Zheng Ye
1
1
College of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China
2
College of Mechanical and Electrical Engineering, Ningde Normal University, Ningde 352000, China
3
College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2025, 15(13), 7555; https://doi.org/10.3390/app15137555
Submission received: 26 May 2025 / Revised: 2 July 2025 / Accepted: 3 July 2025 / Published: 5 July 2025
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Abstract

Lithium-ion batteries play a vital role in human society. Therefore, it is of critical significance to reliably predict the evolution of State of Health (SOH) degradation patterns in order to improve the high accuracy and stability of lithium-ion battery SOH prediction. This paper proposes a novel SOH predication method by combing the four-vector intelligent metaheuristic (FVIM) with the CNN-LSTM-Attention basic model. The model adopts the collaborative architecture of a convolutional neural network and time series module, strengthens the cross-level feature interaction by introducing a multi-level attention mechanism, then uses the FVIM optimization algorithm to optimize the key parameters to realize the overall model architecture. By analyzing the charging voltage curve of lithium-ion batteries, the health factors with high correlation are extracted, and the correlation between the health factors and battery capacity is verified using two correlation coefficients. After the model is verified on a single NASA battery aging dataset, the model is compared with other models under the same relevant parameters and environmental settings to verify the high-precision prediction of the model. During the analysis and comparison process, CNN-LSTM-Attention-FVIM achieved a high fitting ability for battery SOH prediction estimation, with the mean absolute error (MAE) and root mean square error (RMSE) within 0.99% and 1.33%, respectively, reflecting the model’s high generalization ability and high prediction performance.
Keywords: state of health; lithium-ion battery; health factor; correlation coefficient; deep learning state of health; lithium-ion battery; health factor; correlation coefficient; deep learning

Share and Cite

MDPI and ACS Style

Liu, G.; Deng, Z.; Xu, Y.; Lai, L.; Gong, G.; Tong, L.; Zhang, H.; Li, Y.; Gong, M.; Yan, M.; et al. Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Algorithm and Fusion of Multiple Health Features. Appl. Sci. 2025, 15, 7555. https://doi.org/10.3390/app15137555

AMA Style

Liu G, Deng Z, Xu Y, Lai L, Gong G, Tong L, Zhang H, Li Y, Gong M, Yan M, et al. Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Algorithm and Fusion of Multiple Health Features. Applied Sciences. 2025; 15(13):7555. https://doi.org/10.3390/app15137555

Chicago/Turabian Style

Liu, Guoju, Zhihui Deng, Yonghong Xu, Lianfeng Lai, Guoqing Gong, Liang Tong, Hongguang Zhang, Yiyang Li, Minghui Gong, Mengxiang Yan, and et al. 2025. "Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Algorithm and Fusion of Multiple Health Features" Applied Sciences 15, no. 13: 7555. https://doi.org/10.3390/app15137555

APA Style

Liu, G., Deng, Z., Xu, Y., Lai, L., Gong, G., Tong, L., Zhang, H., Li, Y., Gong, M., Yan, M., & Ye, Z. (2025). Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Algorithm and Fusion of Multiple Health Features. Applied Sciences, 15(13), 7555. https://doi.org/10.3390/app15137555

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