Improved hybrid neural network based on CNN-BiLSTM-attention for co-estimation of SOC and SOE in lithium-ion batteries.

Luo, Tao; Shi, Haotian; Li, Ke; Li, Haoran; Wang, Shunli; Yu, Chunmei; Fernandez, Carlos

Abstract

As the core of modern energy storage technology, lithium-ion batteries are widely used in fields such as electric vehicles, renewable energy storage, and portable electronic devices. Accurately estimating the state-of-charge (SOC) and state-of-energy (SOE) of lithium batteries is crucial for the safety and efficiency of battery management systems. This article proposes an improved convolutional neural network - bidirectional long short-term memory neural network-attention (CNN-BiLSTM-Attention) hybrid neural network model to estimate the SOC and SOE of lithium-ion batteries. To effectively capture long-term dependencies in time series, the BiLSTM is proposed based on long short-term memory neural networks. Meanwhile, by introducing an encoder-decoder attention mechanism, complex data can be processed more effectively, thereby improving the accuracy and reliability of estimation. The results indicate that CNN-BiLSTM-Attention has the smallest mean absolute error (MAE) and root mean square error (RMSE). Under the time condition of 35 °C, the model estimates the MAE and RMSE of SOC and SOE to be around 1 %, with SOC estimating an MAE of 0.97 %. In addition, the model exhibits robustness in data processing and effectively handles the bias of random data.

Citation

LUO, T., SHI, H., LI, K., LI, H., WANG, S., YU, C. and FERNANDEZ, C. 2025. Improved hybrid neural network based on CNN-BiLSTM-attention for co-estimation of SOC and SOE in lithium-ion batteries. Journal of energy storage [online], 131(Part B), article number 117651. Available from: https://doi.org/10.1016/j.est.2025.117651

• Journal Article Type: Article
• Acceptance Date: Jul 3, 2025
• Online Publication Date: Jul 8, 2025
• Publication Date: Sep 30, 2025
• Deposit Date: Jul 17, 2025
• Publicly Available Date: Jul 9, 2026
• Journal: Journal of energy storage
• Print ISSN: 2352-152X
• Electronic ISSN: 2352-1538
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 131
• Issue: Part B
• Article Number: 117651
• DOI: https://doi.org/10.1016/j.est.2025.117651
• Keywords: Lithium-ion battery; State of charge; State of energy; Joint estimation; Hybrid neural network
• Public URL: https://rgu-repository.worktribe.com/output/2929090