Intelligent Fault Identification and Adaptive Diagnosis Algorithm of Electric Vehicle Power System Signals

With the growing adoption of electric vehicles, ensuring power system reliability is critical to safety and performance. Traditional fault diagnosis methods face challenges in accuracy and adaptability. This study proposes an intelligent fault identification framework integrating wavelet transform, convolutional neural networks (CNN), and adaptive compensation algorithms. Sensor signals are decomposed via wavelet transform for multi-scale feature extraction, fused with CNN-learned spatiotemporal patterns, and optimized using principal component analysis (PCA). Experimental validation demonstrates the algorithm’s effectiveness: it achieves 92% accuracy in multi-fault recognition (94% for control system faults), real-time diagnosis with delays below 250 ms, and robust noise resistance. Adaptive compensation restores signal stability, reducing fault-induced fluctuations from ±0.5 to ±0.1. These results confirm the method’s superiority in accuracy, real-time response, and robustness compared to conventional approaches. Future work should expand datasets and enhance real-time processing for broader EV applications.