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As EV adoption grows, reliable On-Board Chargers (OBCs) are essential for safe and efficient charging. Diagnosing OBC faults is challenging due to varied fault types. Traditional rule-based methods struggle with modern systems, prompting the use of machine learning. Our study shows that applying performance trade-off about various machine learning model, significantly shows fault classification F1 score while ensuring real-time performance in PFC fault diagnostics in OBCs.

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Performance Trade-offs of Machine Learning Hyperparameters in On-board Charger’s Power Factor Correction Fault Classification

  • 会议论文

  • 发表日期：

    2026年

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  • Yi-Hyeong Park,
  • Dong-In Lee,
  • Han-Shin Youn,
  • Chang Mook Kang

摘要

As EV adoption grows, reliable On-Board Chargers (OBCs) are essential for safe and efficient charging. Diagnosing OBC faults is challenging due to varied fault types. Traditional rule-based methods struggle with modern systems, prompting the use of machine learning. Our study shows that applying performance trade-off about various machine learning model, significantly shows fault classification F1 score while ensuring real-time performance in PFC fault diagnostics in OBCs.