Model Predictive Control and Fault Diagnosis for IPMSM EV Traction Drive

This paper introduces an advanced Model Predictive Control (MPC) scheme for Interior Permanent Magnet Synchronous Motor (IPMSM) traction drive, integrating Maximum Torque per Ampere (MTPA) and Field Weakening (FW) strategies supported by precomputed reference tables. This paper focus on MTPA that ensures the high operational efficiency as it selects the smallest current vector reference by using the reference tables for the desired torque and speed. FW that allows operation at higher speeds increasing the operating range. The MPC strategy that has been used in this paper controls the stator currents while considering the voltage vectors capability of the inverter to control torque and flux. The real time faults are injected in the simulation to test the system. Additionally, the system is made fault tolerant by activating Freewheeling and active short circuit. The performance of PI controller and MPC with respect to wide speed operation and torque ripples are compared in this paper. Lastly, in the simulation results we observe that the suggested approach works well under various load scenarios by minimizing the computational burden and Torque ripples with the efficient operation in both speed and torque mode of Electric Vehicles. Key Words: Model Predictive Control (MPC), Interior Permanent Magnet Synchronous Motor (IPMSM), Maximum Torque per Ampere (MTPA), Field Weakening (FW), Free Wheeling, Active Short Circuit.