Disturbance Robust Generalized Predictive Control Applied to an EV Charger Grid Converter

Abstract

Electric vehicles (EVs) are the best solution to tackle the critical challenge of reducing carbon emissions in the transportation sector. However, the widespread adoption of EVs relies on advancing fast-charging infrastructure technology. This includes overcoming challenges related to operating under disturbed conditions, which can impact the stability of the internal control loop. This article presents a method for robustly tuning a generalized predictive control (GPC) for an EV charger grid converter. This approach aims to enhance its performance in the face of disturbances in the grid voltage and internal filter parameters. One significant scientific gap in applying GPC in grid-tied converters concerns systematic tuning. This article addresses this gap by explicitly analyzing the impact of tuning on the stability and robustness of the GPC controller. The concept of robust stability margin, derived from singular value decomposition, is used for this purpose. Experimental results obtained from an EV charger prototype validated the tuning proposal aimed at maximizing the robustness and performance of the grid converter. The tests with different internal filters guaranteed a performance level within the defined error band. Furthermore, experimental tests have shown that the proposed controller is more robust than conventional MPC.