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 is widely used in various studies lately. The results pay the way for demanding solutions to address the concern of utilities pertaining to DERs load management to realize vehicle-to-grid (V2G) technologies.