Design and Implementation of a Hardware Test-bed for Real-time EV-Grid Integration Analysis

This study presents the design of an electric vehicle (EV)-grid integration hardware test-bed to implement smart EV charging algorithms. The proposed test-bed allows for creating various grid events via flexible integration of power hardware (e.g., controllable loads, and battery energy storage systems) and analyzing their impacts on EV charging. The design uses a real-time digital simulator (OPAL-RT OP5600) to simulate a complex distribution grid model with primary and secondary networks. A grid simulator (AMETEK MX22.5) physically realizes a selected node of the simulated grid to power actual loads, including a Nissan Leaf EV and a load bank. The EVGI test-bed is demonstrated based on the custom hardware and software implementation of the J1772 charging protocol using dSPACE MicroLabBox. We tested the proposed hardware test-bed by implementing a machine learning (ML) supported and decentralized EV charging algorithm based on the Additive Increase and Multiplicative Decrease (AIMD) control method. The results showed that the EV charging current successfully followed the control commands generated by the proposed smart charging controller running on the EVSE and regulated the charging power to effectively reduce the system loading caused by high EV penetration.