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Health State Prediction of Lithium-Ion Battery Based on Improved Sparrow Search Algorithm and Support Vector Regression

Author

Listed:
  • Deyang Yin

    (The School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China)

  • Xiao Zhu

    (The School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China)

  • Wanjie Zhang

    (The School of Intelligent Manufacturing, Changzhou Technician College Jiangsu Province, Changzhou 213164, China)

  • Jianfeng Zheng

    (The School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China)

Abstract

The state of health (SOH) prediction of lithium-ion batteries is a pivotal function within the battery management system (BMS), and achieving accurate SOH predictions is crucial for ensuring system safety and prolonging battery lifespan. To enhance prediction performance, this paper introduces an SOH prediction model based on an improved sparrow algorithm and support vector regression (ISSA-SVR). The model uses nonlinear weight reduction (NWDM) to enhance the search capability of the Sparrow algorithm and combines a mixed mutation strategy to reduce the risk of local optimal traps. Then, by analyzing the characteristics of different voltage ranges, the charging time from 3.8 V to 4.1 V, the discharge time of the battery, and the number of cycles are selected as the feature set of the model. The model’s prediction capabilities are validated across various working environments and training sample sizes, and its performance is benchmarked against other algorithms. Experimental findings indicate that the proposed model not only confines SOH prediction errors to within 1.5% but also demonstrates robust adaptability and widespread applicability.

Suggested Citation

  • Deyang Yin & Xiao Zhu & Wanjie Zhang & Jianfeng Zheng, 2024. "Health State Prediction of Lithium-Ion Battery Based on Improved Sparrow Search Algorithm and Support Vector Regression," Energies, MDPI, vol. 17(22), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5671-:d:1519925
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    References listed on IDEAS

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    1. Li, Junfu & Lai, Qingzhi & Wang, Lixin & Lyu, Chao & Wang, Han, 2016. "A method for SOC estimation based on simplified mechanistic model for LiFePO4 battery," Energy, Elsevier, vol. 114(C), pages 1266-1276.
    2. Lin, Mingqiang & Yan, Chenhao & Meng, Jinhao & Wang, Wei & Wu, Ji, 2022. "Lithium-ion batteries health prognosis via differential thermal capacity with simulated annealing and support vector regression," Energy, Elsevier, vol. 250(C).
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