Enhancing Reliability of EV Charging Systems: Open-Circuit Fault Detection Using Advanced Signal Processing and Machine Learning
Abstract
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.
Keywords
Electric Vehicle, Power Electronics Fault Detection, Machine Learning, k-Nearest Neighbor, Random Forest
References
- Agrawal, M., Rajapatel, M. S. (2020). Global perspective on electric vehicle. International Journal of Engineering Research & Technology, 9(1), 8-11.
- Cai, F., Zhan, M., Chai, Q., Jiang, J. (2022). Fault diagnosis of DAB converters based on ResNet with adaptive threshold denoising. IEEE Transactions on Instrumentation and Measurement, 71, 1-10.
- Chen, Y., Tang, Z., Weng, X., He, M., Zhou, S., Liu, Z., Jin, T. (2024). A diagnostic method for open-circuit faults in DC charging stations based on improved S-transform and LightGBM. Energies, 17(2), 404.
- Du, B., He, Y., Zhang, C. (2021). Intelligent diagnosis of cascaded H‐bridge multilevel inverter combining sparse representation and deep convolutional neural networks. IET Power Electronics, 14(6), 1121-1137.
- Fazli, A., Poshtan, J. (2024). Wind turbine fault detection and isolation robust against data imbalance using KNN. Energy Science & Engineering, 12(3), 1174-1186.
- Geng, X., Wang, L., Yu, P., Hu, W., Liang, Q., Zhang, X., & Zhang, X. (2025). A method of EEG signal feature extraction based on hybrid DWT and EMD. Alexandria Engineering Journal, 113, 195-204.
- Ghosh, S. S., Chattopadhyay, S., Das, A., Medikondu, N. R., Almawgani, A. H., Alhawari, A. R., Das, S. (2024). Wavelet-based rapid identification of IGBT switch breakdown in voltage source converter. Microelectronics Reliability, 152, 115283.
- Gohari, M., & Ghorbani, M. H. (2025). Classification and diagnosis of the rotor unbalance parameters via hybridized EMD and RF. Journal of Vibration Engineering & Technologies, 13(4), 246.
- Gong, W., Chen, H., Zhang, Z., Zhang, M., & Gao, H. (2020). A data-driven-based fault diagnosis approach for electrical power DCDC inverter by using modified convolutional neural network with global average pooling and 2-D feature image”, IEEE Access, 8, 73677-73697.
- Gopal, S. D., Jawahar, R., Athmanathan, R., Pandi, M. (2024). Power electronics converters for an electric vehicle fast charging station based energy storage system and renewable energy sources: Hybrid approach. Optimal Control Applications and Methods, 45(2), 646- 673.