Advanced Powertrain Fault Diagnosis for Electric Buses: An IoV Approach

Electrical buses (E-Bus) are one of the potential methods for reducing e-mobility emissions. The operational performance of this technology remains an open subject. Digitization and physical modeling allow for intelligent condition monitoring of the E-Bus system. This study presents a digital-twin framework for E-Bus powertrain failure diagnosis. The digital framework used system modeling, artificial intelligence (AI) models, and Internet-of-Vehicle (IoV) connections for real E-Bus operation data to predict powertrain inter-turn errors. The suggested digital twin framework offers a dependable approach for monitoring the E-Bus powertrain. The bestselected predictive AI model for the E-Bus case study achieves $\mathbf{6 3. 7 5 \%}$ diagnosis sensitivity and $\mathbf{0. 4 2 6 \%}$ prediction error across varied loading conditions.