New Energy Vehicle Fault Diagnosis System Based on Deep Learning

The fast advancement of new energy vehicles has raised greater demands for fault diagnosis technology. In response to this challenge, researchers have designed a fault diagnosis model utilizing deep learning. The model integrates CNN and LSTM networks and is trained with extensive real-world operational data. Experimental results demonstrate that the model achieves outstanding performance in diagnosing ten common fault types, attaining an overall accuracy of 97.2% and an F1 score of 0.968, the model excels in real-time performance, with an average diagnosis delay of only 15ms, but also demonstrates strong generalization ability, maintaining an accuracy of 93.7% for new vehicle data. Compared to traditional methods, the model demonstrates notable advantages in managing complex situations and multiple faults, Offering strong support for optimizing the efficiency of new energy vehicle maintenance.