Digital Twin-Driven Predictive Maintenance for Electric Vehicle Powertrains: Case Studies and Quantitative Performance Insights

The rapidly increasing EV adoption across the globe demands advanced service strategies to ensure high levels of reliability, long component life and low operations costs. The key components of the EV powertrain, which are highly vulnerable to different electrical and mechanical failures, include sensor malfunctions, which can lead to poor performance, unforeseen repeated issues, and safety issues. The typical maintenance techniques are unable to address this dynamic and complicated behavior at a systems level among EVs, therefore making the malfunctions unpredictable, and the resources are very unproductive. This paper provides an evaluation of the opportunities of DT technology to revolutionise predictive maintenance in drive systems of PMSMs. It dwells upon the key principles of the DT architecture that may enable the AI/ML-managed PdM and provides the descriptions of the case studies that lead to tremendous decreases in unplanned downtimes, depending on the correctness of RUL estimates. The opportunities for future research are also noted, such as explainable AI and augmented cybersecurity. Concisely, the review under analysis reveals that DTs have a significant contribution to making future EV powertrains more reliable, efficient, and sustainable.