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

Author

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

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.

Suggested Citation

  • Wang, Shunli & Ma, Chao & Gao, Haiying & Deng, Dan & Fernandez, Carlos & Blaabjerg, Frede, 2025. "Improved hyperparameter Bayesian optimization-bidirectional long short-term memory optimization for high-precision battery state of charge estimation," Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:energy:v:328:y:2025:i:c:s0360544225022406
    DOI: 10.1016/j.energy.2025.136598
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