Constrained Model Predictive Control for Optimized Unidirectional Three Phase G2V Applications for Battery Electric Vehicle Charge /

Abstract

The Battery Electric Vehicle charger serves as a crucial link between the grid and the ve­ hicle in three-phase configurations with lithium-ion batteries on the vehicle side, promis­ ing faster charging, extended lifespan, and higher power density. However, managing power flow in these setups presents significant challenges. For balanced onboard charg­ ing, an unregulated rectifier DC-DC buck converter has been implemented. This research introduces an innovative control system for BEV chargers featuring a three-phase model, enabling efficient unidirectional power transfer in Grid-to-Vehicle (G2V) mode. A con­ strained Model Predictive Controller (MPC) is devised to regulate the unidirectional power converter during G2V operations, ensuring precise control of charger current and output voltage. The MPC is tailored with a unit prediction horizon, meaning it predicts just one step ahead, to keep computational expenses low. Its design aims for perfor­ mance similar to our preferred linear controller, the H-infinity controller. We employ inverse optimal control techniques to set the weight matrices for the cost function. Us­ ing MATLAB simulations, we compare its performance with H-infinity optimal control based on linear matrix inequality, confirming its similarity in performance. Furthermore, we evaluate the MPC against sliding mode control, a nonlinear approach, demonstrating its ability to achieve comparable or even better performance. Moreover, the efficacy of the suggested controllers for BEV chargers is confirmed through Hardware-in-the-Loop experimental validation, utilizing the dual-core microcontroller Delfino F28369D.