Design of Grid Connected RES Based EV Charger using LFNN Control Algorithm

Le résumé fourni par la source

A novel Legendre Functional Neural Network (LFNN) algorithm for three-phase voltage source converter (VSC) regulation is proposed in this research. The study also takes into account a Renewable Energy Sources (RES) like PV connected to the VSC's DC link and an on-board EV charger. The grid and electric vehicles (EVs) receive real electricity from the PV. Bidirectional Buck-Boost converters, an emerging field of study, are used to accomplish both Grid to Vehicle (G2V) and Vehicle-to-Grid (V2G) operations. When an EV is operating in G2V mode, power is supplied from the grid to charge its battery. When operating in V2G mode, the energy stored in the EV battery bank is recycled and sent back to the utility grid, assisting with voltage control, load balancing, peak sliver, and system reliability enhancement. The battery bank and the bidirectional DC-DC converter are connected at the three-phase VSC's DC connection and are managed to enable charging and discharging modes. The MATLAB platform is used to analyze the performance of Renewable Energy Sources (RES) like PV with VSC and PV, EV with VSC. The voltage and current used to charge and discharge batteries are controlled by PWM control technology. This paper analyzes the dynamics of the suggested system and presents the findings using the newly suggested algorithm.

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