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Performance Trade-offs of Machine Learning Hyperparameters in On-board Charger’s Power Factor Correction Fault Classification
- Park, Yi-Hyeong;
- Lee, Dong-In;
- Youn, Han-Shin;
- Kang, Chang Mook
Citations
SCOPUS
0초록
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.
키워드
Accuracy-computational time relationship; Hyperparameter optimization; Machine learning; On-Board Chargers (OBCs); Optimization algorithms; Charging (batteries); Fault detection; Industrial electronics; Learning algorithms; Learning systems; Machine learning; Optimization
- 제목
- Performance Trade-offs of Machine Learning Hyperparameters in On-board Charger’s Power Factor Correction Fault Classification
- 저자
- Park, Yi-Hyeong; Lee, Dong-In; Youn, Han-Shin; Kang, Chang Mook
- 발행일
- 2026-04
- 유형
- Conference paper
- 페이지
- 45 ~ 54