Model Predictive Control for Enhanced Inverter Management in Electric Vehicle Charging

The management of inverters in electric vehicle charger is particularly crucial in scenarios demanding high-quality output voltage. Numerous control strategies exist for regulating three-phase inverters. This study introduces an ef-ficient control approach employing model predictive control (MPC). By leveraging a discrete-time system model, the method anticipates the output voltage's behavior across all potential in-verter switching states. Subsequently, a cost function determines the most suitable switching state for the forthcoming sampling interval. Through MATLAB/Simulink simulations, conducted under BLDC motor load conditions, the viability and commend-able performance of this control approach are confirmed. The intended aim of the proposal is to enhance voltage quality by reducing Total Harmonic Distortion (THD). Upon comparing the waveform and THD performance, it was observed that the proposed controller achieved a lower THD of 8.00%, whereas the traditional FCS MPC yielded a THD of 8.21%. This indicated a reduction of approximately 1.56% with the proposed approach. Additionally, real-world validation of the MPC's efficacy and robustness is provided via experimental outcomes from HIL simulations. This article revisits the requirements of a high-performance inverter through the lens of digital signal processing. We utilize one of Texas Instruments' leading digital signal proces-sors, the TMS320F28379D. Specific model predictive controllers and electronic circuits have been devised, implemented, and evaluated to demonstrate their significance in advancing a high-performance inverter board.