Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side

Abstract

Inverse model predictive control (IMPC) is a control technique that was recently proposed for power electronic converters. IMPC inherits the advantages of model predictive control (MPC) in terms of ability to handle complex and nonlinear systems and achieving multiple control objectives, while adhering to various constraints. Unlike MPC, IMPC offers a significantly reduced computational burden by omitting the iterative computations of the cost functions and states predictions. Nevertheless, both IMPC and MPC rely significantly on the dynamic model of the power converter. This makes them susceptible to uncertainties and disturbances. This article presents a novel technique to enhance the reliability and robustness of the IMPC for electric vehicle chargers by treating the converter's dynamic model as a black box. Then, an adaptive estimation strategy employing a recursive least square algorithm is proposed for online dynamic model estimation, which is then used by the IMPC for optimal switching states prediction. The key benefit of the proposed technique is the utilization of an accurate and real-time estimated dynamic model, which facilitates a reliable states prediction by the IMPC. The effectiveness of the proposed technique is demonstrated through extensive simulations and experimental validation for a three-phase three-level T-type rectifier.