Deep Learning-Driven Acoustic Diagnostics for EV Powertrain Maintenance Education
Abstract
The rapid expansion of electric vehicles (EVs) has placed increasing importance on the reliability and efficiency of powertrain systems, creating a demand for advanced educational methods to train technicians in maintenance and diagnostics. Traditional training approaches often rely on textual manuals, hands-on demonstrations, and limited audio samples, which restrict learners' ability to recognize subtle acoustic signatures indicative of powertrain faults. To address this gap, this paper proposes a Deep Learning-Driven Acoustic Diagnostics framework for EV Powertrain Maintenance Education (DL-AD-EV), which leverages convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze real-time acoustic signals from electric motors, inverters, and drivetrain components. DL-AD-EV automatically identifies characteristic sound patterns associated with component degradation, anomalies, and operational inefficiencies, and organizes these findings into an interactive, educational knowledge base to support adaptive learning and rapid fault recognition. Experimental evaluation on a curated dataset of EV powertrain acoustic recordings demonstrated that DL-AD-EV achieved an F1-score of 90% in fault classification, improved diagnostic learning efficiency by 33%, and enhanced student retention of maintenance procedures by 28% compared to conventional manual-based training approaches. These results indicate that deep learning-based acoustic analysis can significantly improve both the accuracy of fault identification and the quality of EV maintenance education. In conclusion, DL-AD-EV provides a scalable, intelligent, and immersive learning framework, bridging the gap between theoretical understanding and practical diagnostic skills, and equipping future technicians with the capabilities required for efficient EV powertrain maintenance.