Improved hyperparameter Bayesian optimization-bidirectional long short-term memory optimization for high-precision battery state of charge estimation

Shunli Wang, Chao Ma, Haiying Gao, Dan Deng, Carlos Fernandez and Frede Blaabjerg

Energy, 2025, vol. 328, issue C

Abstract: At a time when new energy sources are constantly developing, mitigating the safety hazards of lithium batteries and prolonging their lifespan. In this paper, we take a ternary lithium-ion battery as an experimental object and carry out research based on the fusion method of deep learning and modeling for its high-precision state of charge (SOC) estimation requirements. This paper explores the construction of a battery dynamic model and hyperparameter optimization method based on a neural network. It also incorporates Kalman filter to investigate the noise correction strategy of a neural network model. Experimentally verified that the BO-BiLSTM-UKF fusion algorithm in this paper has a maximum error of only 0.113 %, which verifies the accuracy and strong robustness of the model. Its MAE and RMSE are reduced by 96.13 % and 95.73 % compared with the LSTM network model, which has better adaptability and estimation ability. In this paper, a network dynamic prediction fusion method based on the equivalent model is constructed and experimentally verified by different temperatures, complex working conditions and step-by-step simulation.

Keywords: New energy lithium-ion battery; SOC; Noise reduction (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:328:y:2025:i:c:s0360544225022406

DOI: 10.1016/j.energy.2025.136598

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