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Research on SOC Prediction of Lithium-Ion Batteries Based on OLHS-DBO-BP Neural Network

Author

Listed:
  • Genbao Wang

    (School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 315211, China
    College of New Energy, Ningbo University of Technology, Ningbo 315336, China
    Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China)

  • Yejian Xue

    (College of New Energy, Ningbo University of Technology, Ningbo 315336, China)

  • Yafei Qiao

    (Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China)

  • Chunyang Song

    (Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China)

  • Qing Ming

    (School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 315211, China)

  • Shuang Tian

    (Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China)

  • Yonggao Xia

    (Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China)

Abstract

Accurately estimating the state of charge (SOC) of lithium-ion batteries is of great significance for extending battery lifespan and enhancing the efficiency of energy management. Regarding the issue of the relatively low estimation accuracy of SOC by the backpropagation neural network (BPNN), an enhanced dung beetle optimizer (DBO) algorithm is proposed to optimize the initial weights and thresholds of the BPNN. This overcomes the drawback of a single BP neural network being prone to local optimum and accelerates the convergence rate. Simulation analyses on the experimental data of NCM and A123 lithium batteries were conducted in Matlab R2022a. The results indicate that the proposed algorithm in this paper has an average SOC estimation error of less than 1.6% and a maximum error within 2.9%, demonstrating relatively high estimation accuracy and robustness, and it holds certain theoretical research significance.

Suggested Citation

  • Genbao Wang & Yejian Xue & Yafei Qiao & Chunyang Song & Qing Ming & Shuang Tian & Yonggao Xia, 2024. "Research on SOC Prediction of Lithium-Ion Batteries Based on OLHS-DBO-BP Neural Network," Energies, MDPI, vol. 17(23), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6052-:d:1534741
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    References listed on IDEAS

    as
    1. Stephen Leary & Atul Bhaskar & Andy Keane, 2003. "Optimal orthogonal-array-based latin hypercubes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(5), pages 585-598.
    2. Jian Ouyang & Hao Lin & Ye Hong, 2024. "Whale Optimization Algorithm BP Neural Network with Chaotic Mapping Improving for SOC Estimation of LMFP Battery," Energies, MDPI, vol. 17(17), pages 1-22, August.
    3. Jiankai Xue & Bo Shen, 2024. "A survey on sparrow search algorithms and their applications," International Journal of Systems Science, Taylor & Francis Journals, vol. 55(4), pages 814-832, March.
    4. Hao Wang & Yanping Zheng & Yang Yu, 2021. "Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter," Mathematics, MDPI, vol. 9(15), pages 1-12, July.
    Full references (including those not matched with items on IDEAS)

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