Load Management of a Benchmark Electric Vehicle Charger Using an Intelligent Controller for HIL Implementation

An intelligent controller is developed based on machine learning algorithm incorporated into predictive control strategy. The model of interest is trained during transfer learning process utilizing measurement dataset. The introduced control scheme provides an optimal load dispatching among distributed energy resources (DERs) interconnected with a benchmarked Electric Vehicle (EV) battery charger. The proposed scheme offers an easy realization on various embedded hardware systems independently. In this study, a few operational conditions of an exemplary charging station are examined via Hardware-in-the-Loop (HIL) testing. The findings are then compared to the classical finite-control-set model predictive control (FCS-MPC) which has been widely used in recent studies. Several operation scenarios have been evaluated and the obtained results are paving the way for future solutions addressing the concern of utilities pertaining to DERs load management for vehicle-to-grid (V2G) technologies.