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

Tongrui Zhang (), Ran Li and Yongqin Zhou
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Tongrui Zhang: Houston International Institute, Dalian Maritime University, Dalian 116026, China
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

Energies, 2023, vol. 16, issue 21, 1-17

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.

Keywords: lithium battery; online least squares support vector machine; fault diagnosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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