Model Predictive Control of High-Efficiency Motor Drives for Electric Mobility

The rapid growth of electric mobility has intensified the demand for high-efficiency motor drives that 
can maximize energy utilization, extend battery life, and improve overall vehicle performance. 
Traditional motor control strategies, such as Field-Oriented Control (FOC) and Direct Torque Control 
(DTC), often face limitations in balancing dynamic performance, efficiency, and constraint handling, 
particularly under fast-changing operating conditions. This research investigates the application of 
Model Predictive Control (MPC) for high-efficiency electric motor drives, offering a systematic 
approach to real-time optimization of torque, current, and switching behavior. A comprehensive 
mathematical model of the motor-inverter system is developed, incorporating constraints on voltage, 
current, and switching frequency. The proposed MPC framework employs a finite control set to 
predict future system states and select optimal control actions that minimize a multi-objective cost 
function encompassing torque ripple, energy loss, and thermal stress. Simulation studies demonstrate 
significant improvements in efficiency, torque tracking, and dynamic response compared to 
conventional control methods. Furthermore, hardware-in-the-loop validation confirms the practical 
feasibility of MPC implementation for real-time electric vehicle applications. The findings indicate 
that MPC not only enhances the operational efficiency of electric drives but also supports the 
integration of advanced power electronics technologies, including wide bandgap semiconductor 
devices, thereby contributing to the next generation of high-performance electric mobility solutions