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Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network

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  • Biao Yang

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
    Yunnan Provincial Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China
    Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology, Kunming 650093, China)

  • Yinshuang Wang

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China)

  • Yuedong Zhan

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China)

Abstract

State of charge (SOC) is the most important parameter in battery management systems (BMSs), but since the SOC is not a directly measurable state quantity, it is particularly important to use advanced strategies for accurate SOC estimation. In this paper, we first propose a bidirectional long short-term memory (BiLSTM) neural network, which enhances the comprehensiveness of information by acquiring both forward and reverse battery information compared to the general one-way recurrent neural network (RNN). Then, the parameters of this network are optimized by introducing a Bayesian optimization algorithm to match the data characteristics of lithium batteries with the network topology. Finally, two sets of lithium battery public data sets are used to carry out experiments under different constant temperature and variable temperature environments. The experimental results show that the proposed model can effectively fit the actual measurement curve. Compared with traditional long short-term memory network (LSTM) and BiLSTM models, the prediction accuracy of the Bayes-BiLSTM model is the best, with a root mean square error (RMSE) within 1%, achieving a better ability for capturing long-term dependencies. Overall, the model exhibits high accuracy, adaptability, and generalization for the SOC estimation of batteries with different chemical compositions.

Suggested Citation

  • Biao Yang & Yinshuang Wang & Yuedong Zhan, 2022. "Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network," Energies, MDPI, vol. 15(13), pages 1-18, June.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4670-:d:847988
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    References listed on IDEAS

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    Cited by:

    1. Xin Zhang & Jiawei Hou & Zekun Wang & Yueqiu Jiang, 2022. "Joint SOH-SOC Estimation Model for Lithium-Ion Batteries Based on GWO-BP Neural Network," Energies, MDPI, vol. 16(1), pages 1-17, December.
    2. Yu, Hanqing & Zhang, Lisheng & Wang, Wentao & Li, Shen & Chen, Siyan & Yang, Shichun & Li, Junfu & Liu, Xinhua, 2023. "State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteries," Energy, Elsevier, vol. 278(C).
    3. Xinghao Zhang & Yan Huang & Zhaowei Zhang & Huipin Lin & Yu Zeng & Mingyu Gao, 2022. "A Hybrid Method for State-of-Charge Estimation for Lithium-Ion Batteries Using a Long Short-Term Memory Network Combined with Attention and a Kalman Filter," Energies, MDPI, vol. 15(18), pages 1-26, September.
    4. Ran Li & Hui Sun & Xue Wei & Weiwen Ta & Haiying Wang, 2022. "Lithium Battery State-of-Charge Estimation Based on AdaBoost.Rt-RNN," Energies, MDPI, vol. 15(16), pages 1-15, August.
    5. Ouyang, Tiancheng & Pan, Mingming & Huang, Youbin & Tan, Xianlin & Qin, Peijia, 2023. "Thermodynamic design and power prediction of a solar power tower integrated system using neural networks," Energy, Elsevier, vol. 278(PA).

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