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Battery Fault Diagnosis Method Based on Online Least Squares Support Vector Machine

Author

Listed:
  • Tongrui Zhang

    (Houston International Institute, Dalian Maritime University, Dalian 116026, China
    Cullen College of Engineering, The University of Houston, Houston, TX 77204, USA)

  • Ran Li

    (Engineering Research Center of Automotive Electronics Drive Control and System Integration, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China)

  • Yongqin Zhou

    (Engineering Research Center of Automotive Electronics Drive Control and System Integration, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China)

Abstract

Battery fault diagnosis technology is crucial for the reliable functioning of battery systems. This research introduces an online least squares support vector machine method tailored for battery fault diagnosis. After examining battery fault types and gathering relevant data, this method creates a diagnostic model, effectively addressing small and sporadic fault data that is inadequately handled by conventional support vector machines. Recognizing that certain battery malfunctions evolve over time and are multifaceted, confidence intervals have been integrated into the diagnostic models, enhancing accuracy. Upon testing this model using empirical data, it demonstrated rapid diagnostic capabilities and outperformed other algorithms in identifying progressive faults, ensuring precise fault identification, minimizing false alarms, and bolstering battery system safety.

Suggested Citation

  • Tongrui Zhang & Ran Li & Yongqin Zhou, 2023. "Battery Fault Diagnosis Method Based on Online Least Squares Support Vector Machine," Energies, MDPI, vol. 16(21), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:21:p:7273-:d:1267957
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    References listed on IDEAS

    as
    1. Chen, Zeyu & Xiong, Rui & Tian, Jinpeng & Shang, Xiong & Lu, Jiahuan, 2016. "Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 365-374.
    2. Zhang, Shuzhi & Jiang, Shiyong & Wang, Hongxia & Zhang, Xiongwen, 2022. "A novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack," Applied Energy, Elsevier, vol. 322(C).
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