Remaining Useful Life Estimation of Lithium-Ion Batteries Using ‌Alpha Evolutionary Algorithm-Optimized Deep Learning

Li, Fei; Yang, Danfeng; Li, Jinghan; Wang, Shuzhen; Wu, Chao; Li, Mingwei; Li, Chuanfeng; Han, Pengcheng; Qian, Huafei

doi:10.3390/batteries11100385

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Open AccessArticle

Remaining Useful Life Estimation of Lithium-Ion Batteries Using ‌Alpha Evolutionary Algorithm-Optimized Deep Learning

by

Fei Li

^1,†

,

Danfeng Yang

^1,†,

Jinghan Li

²,

Shuzhen Wang

^1,*,

Chao Wu

^1,*,

Mingwei Li

¹,

Chuanfeng Li

¹,

Pengcheng Han

¹ and

Huafei Qian

³

¹

School of Power Electrical Engineering, Luoyang Institute of Science and Technology, Luoyang 471023, China

²

College of Information Engineering and Artificial Intelligence, Henan University of Science and Technology, Luoyang 471023, China

³

Harbin Shenkong Technology Co., Ltd., Harbin 150028, China

^*

Authors to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Batteries 2025, 11(10), 385; https://doi.org/10.3390/batteries11100385

Submission received: 5 September 2025 / Revised: 23 September 2025 / Accepted: 15 October 2025 / Published: 20 October 2025

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Abstract

The precise prediction of the remaining useful life (RUL) of lithium-ion batteries is of great significance for improving energy management efficiency and extending battery lifespan, and it is widely applied in the fields of new energy and electric vehicles. However, accurate RUL prediction still faces significant challenges. Although various methods based on deep learning have been proposed, the performance of their neural networks is strongly correlated with the hyperparameters. To overcome this limitation, this study proposes an innovative approach that combines the Alpha evolutionary (AE) algorithm with a deep learning model. Specifically, this hybrid deep learning architecture consists of convolutional neural network (CNN), time convolutional network (TCN), bidirectional long short-term memory (BiLSTM) and multi-scale attention mechanism, which extracts the spatial features, long-term temporal dependencies, and key degradation information of battery data, respectively. To optimize the model performance, the AE algorithm is introduced to automatically optimize the hyperparameters of the hybrid model, including the number and size of convolutional kernels in CNN, the dilation rate in TCN, the number of units in BiLSTM, and the parameters of the fusion layer in the attention mechanism. Experimental results demonstrate that our method significantly enhances prediction accuracy and model robustness compared to conventional deep learning techniques. This approach not only improves the accuracy and robustness of battery RUL prediction but also provides new ideas for solving the parameter tuning problem of neural networks.

Keywords: lithium-ion battery; remaining useful life (RUL); Alpha evolutionary algorithm; deep learning

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MDPI and ACS Style

Li, F.; Yang, D.; Li, J.; Wang, S.; Wu, C.; Li, M.; Li, C.; Han, P.; Qian, H. Remaining Useful Life Estimation of Lithium-Ion Batteries Using ‌Alpha Evolutionary Algorithm-Optimized Deep Learning. Batteries 2025, 11, 385. https://doi.org/10.3390/batteries11100385

AMA Style

Li F, Yang D, Li J, Wang S, Wu C, Li M, Li C, Han P, Qian H. Remaining Useful Life Estimation of Lithium-Ion Batteries Using ‌Alpha Evolutionary Algorithm-Optimized Deep Learning. Batteries. 2025; 11(10):385. https://doi.org/10.3390/batteries11100385

Chicago/Turabian Style

Li, Fei, Danfeng Yang, Jinghan Li, Shuzhen Wang, Chao Wu, Mingwei Li, Chuanfeng Li, Pengcheng Han, and Huafei Qian. 2025. "Remaining Useful Life Estimation of Lithium-Ion Batteries Using ‌Alpha Evolutionary Algorithm-Optimized Deep Learning" Batteries 11, no. 10: 385. https://doi.org/10.3390/batteries11100385

APA Style

Li, F., Yang, D., Li, J., Wang, S., Wu, C., Li, M., Li, C., Han, P., & Qian, H. (2025). Remaining Useful Life Estimation of Lithium-Ion Batteries Using ‌Alpha Evolutionary Algorithm-Optimized Deep Learning. Batteries, 11(10), 385. https://doi.org/10.3390/batteries11100385

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Remaining Useful Life Estimation of Lithium-Ion Batteries Using ‌Alpha Evolutionary Algorithm-Optimized Deep Learning

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