Open-Circuit Fault Diagnosis of Two-Stage Electric Vehicle Chargers Using Multi-Device Thermal Responses Under Real-World Charging Conditions

With the continuous increase in electric vehicle (EV) charging power and power density, timely and accurate diagnosis of open-circuit faults in power devices is essential for the reliable operation of EV chargers. To address the strong dependence of existing open-circuit fault diagnosis methods on electrical features and their limited generalization across operating conditions, an open-circuit fault diagnosis method for EV chargers employing a power factor correction (PFC) stage and an inductor-inductor-capacitor (LLC) resonant converter is proposed based on real-world charging conditions and multi-device dynamic thermal responses. First, an electrothermal model incorporating device losses, Foster thermal networks, and a state-of-charge (SOC)-dependent equivalent load is established to obtain multi-device junction-to-case temperature-difference responses. Then, representative SOC operating conditions are determined from real-world charging data, and fault simulations are performed to construct a thermal-response dataset. Finally, based on this dataset, a convolutional block attention module (CBAM)-temporal convolutional network (TCN)-gated recurrent unit (GRU)-Attention model is developed for fault identification and localization. Compared with single-SOC training, representative-SOC training improves the accuracy under noise-free unseen-SOC conditions from 64.62% to 99.74%, while maintaining 91.38% accuracy at a signal-to-noise ratio of 3 dB. These results demonstrate that the proposed method provides strong cross-SOC generalization capability and noise robustness.