Digital Twin for Motor & MCU incorporating Deep learning AI-ML algorithms for intelligent prognostics for Electric vehicles

As electric vehicles (EVs) become more advanced, so ensuring the reliability of critical components like the motor and Motor Control Unit (MCU) is essential. This paper presents a digital twin model designed to predict failures in motor and MCU components using machine learning. The approach focuses on detecting early signs of failure through real-world data and advanced analytics. We collected thermal and performance data from field vehicles, capturing both normal (healthy) and abnormal (faulty) operating conditions. Using this dataset, we developed and trained an Auto Encoder-based machine learning model that learns what “normal” looks like and flags deviations as potential issues. One key outcome of this study is the successful early prediction of Insulated Gate Bipolar Transistor (IGBT) degradation, where the system identified subtle behavioral changes long before any visible failure symptoms appeared. This digital twin acts as a virtual replica of the physical components, continuously monitoring and comparing real-time data to the learned normal behavior. It serves as a powerful tool for predictive maintenance, helping to reduce downtime, avoid unexpected failures, and optimize vehicle performance. The strength of this work lies in combining actual component-level data with a robust machine learning pipeline to create a scalable and practical failure prediction system. We are currently expanding this model to cover a wider range of failure scenarios for both motors and MCUs. This study offers a significant step toward smarter, more reliable electric vehicles by enabling early detection of potential failures through digital twins and AI.