TY  - JOUR
T1  - Enhancing Reliability of EV Charging Systems: Open-Circuit Fault Detection Using Advanced Signal Processing and Machine Learning
AU  - Okumuş, Hatice
AU  - Mollahasanoğlu, Merve
AU  - Ergün, Ebru
PY  - 2025
DA  - November
Y2  - 2025
DO  - 10.29137/ijerad.1684636
JF  - International Journal of Engineering Research and Development
JO  - IJERAD
PB  - Kirikkale University
WT  - DergiPark
SN  - 1308-5506
SP  - 525
EP  - 536
VL  - 17
IS  - 3
LA  - en
AB  - The environmentally friendly nature and low operational costs of electric vehicles (EVs) have significantly increased their adoption in recent years. As EV usage grows, the need for reliable and efficient power electronics in charging infrastructure becomes more critical. These systems rely on switching components such as IGBTs and SiC MOSFETs, which are prone to failure due to high operating temperatures and currents. To address this issue, this study proposes a novel method for detecting open-circuit faults in AC/DC rectifiers used in EV charging stations. The approach analyzes three-phase current signals on the AC side to identify faulty switching devices. Feature extraction is performed using a hybrid technique that combines Discrete Wavelet Transform (DWT) and the Teager–Kaiser Energy Operator (TKEO), capturing transient fault-related characteristics. The extracted features are then classified using k-Nearest Neighbors (k-NN) and Random Forest (RF) algorithms. Performance was evaluated using 10-fold and 5-fold cross-validation. In both settings, RF outperformed k-NN across all metrics. Under 5-fold validation, RF achieved accuracy, recall, precision, and F1-score values of 0.9933, 0.9933, 0.9935, and 0.9933, respectively. These results confirm the robustness and effectiveness of the RF-based method for fault detection, making it a promising tool for predictive maintenance and fault-tolerant EV charging systems.
KW  - Electric Vehicle
KW  - Power Electronics Fault Detection
KW  - Machine Learning
KW  - k-Nearest Neighbor
KW  - Random Forest
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UR  - https://doi.org/10.29137/ijerad.1684636
L1  - https://dergipark.org.tr/en/download/article-file/4809794
ER  -