Inverse Bond Graph Model-Based Active Fault Tolerant Control for Health Monitoring of Electric Vehicle Path Tracking

This article deals with the integration of fault estimation with inverse Bond Graph model for health monitoring of an electric vehicle. This autonomous vehicle is a multiple-input multiple-output system with four electromechanical traction subsystems. The innovative interest of this work is to exploit one graphical approach not only for vehicle dynamics modeling and diagnosis but also for fault estimation and fault-tolerant control. For robust fault diagnosis, residuals are generated in the presence of uncertainties. The purpose of using fault estimation is to generate an accurate fault magnitude to the inverse bond graph system. The latter aims to compensate for the power generated by the fault. This structure is then applied to an electric vehicle in order to monitor the system in real-time and to correct the tracking in faulty situations.