Health Monitoring Framework for Electric Vehicle Drive Train in Digital Twin

As electric vehicles (EVs) continue to evolve and become more intricate, it becomes increasingly important to monitor their health continuously to ensure both safe operation and optimal performance. To address this need, this research paper proposes a comprehensive health monitoring framework that leverages the concept of Digital Twin (DT). The DT incorporates a bond graph (BG) model, which accurately represents the intricate structure and functionality of the EV drivetrain. Additionally, the framework utilizes Support Vector Data Description (SVDD) to train and classify measured data effectively, enabling efficient fault detection and diagnosis. By integrating the developed BG model and SVDD into the digital twin, the framework enables real-time monitoring and predictive analysis of the EV's health status. The simulation results demonstrate the effectiveness of this framework, showcasing high accuracies of 98.7% during training and 96.21% during testing. These results validate the potential of the proposed approach to ensure the reliable and efficient operation of EVs while also minimizing the risk of malfunctions and ensuring a safe driving experience for users.