Enhanced quantile regression long short-term memory hybrid neural network for the state of charge point and interval estimation of lithium-ion batteries.

Zou, Yuanru; Wang, Shunli; Hai, Nan; Blaabjerg, Frede; Fernandez, Carlos; Cao, Wen

Abstract

The state of charge (SOC) estimation accuracy of lithium-ion batteries directly affects the reliability and management efficiency of clean energy storage systems. However, due to the nonlinear characteristics of batteries and complex working conditions, there are still significant challenges in high-precision SOC estimation. Therefore, this paper proposes a hybrid neural network model based on long short-term memory (LSTM). Specifically, the model extracts multidimensional features through two-dimensional convolution and LSTM neural network with attention mechanism is performed for estimation. In addition, the quantile regression loss function is used in the training of the hybrid neural network to give it confidence interval estimation capability. Finally, the experimental data of different working conditions at multiple temperatures were utilized to validate and analyze the proposed method. The results show that the proposed estimation method has an MAE less than 0.58%, an MSE less than 0.008%, an RMSE less than 0.81%, an R2greater than 99.91%, and a stable confidence interval estimation capability. In summary, this paper innovatively proposes an effective SOC estimation solution, which provides new ideas for future SOC estimation of energy storage battery management systems, and has important theoretical and practical application significance.

Citation

ZOU, Y., WANG, S., HAI, N., BLAABJERG, F., FERNANDEZ, C. and CAO, W. 2025. Enhanced quantile regression long short-term memory hybrid neural network for the state of charge point and interval estimation of lithium-ion batteries. Energy [online], 332, article number137201. Available from: https://doi.org/10.1016/j.energy.2025.137201

• Journal Article Type: Article
• Acceptance Date: Jun 17, 2025
• Online Publication Date: Jun 19, 2025
• Publication Date: Sep 30, 2025
• Deposit Date: Jun 20, 2025
• Publicly Available Date: Jun 20, 2026
• Journal: Energy
• Print ISSN: 0360-5442
• Electronic ISSN: 1873-6785
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 332
• Article Number: 137201
• DOI: https://doi.org/10.1016/j.energy.2025.137201
• Keywords: Lithium-ion battery; State of charge estimation; Convolutional neural network; Attention mechanism; Long short-term memory neural network; Quantile regression
• Public URL: https://rgu-repository.worktribe.com/output/2885948