Volume 21 No 3 (2023)
 Download PDF
A Novel Fuzzy based Fast DC Charging for Electric Vehicle Charging Station
B. Rajesh, V. Vijaya Kumar
The smart grid might make use of electric cars (EVs) as distributed energy storage devices to perform a variety of regulatory duties. The current trend, however, is to jointly manage a fleet of EVs via a new stakeholder called an aggregator in order to have a substantial enough influence on the grid. Based on the availability, mobility patterns, and state of charge (SOC) of each EV battery, the aggregator assigns an active and/or reactive set-point to each EV within a time frame. The goal of this research is to standardize EV charger currents based on the individual user set-points that are sent to the aggregator. A variety of control strategies for bidirectional active power regulation are compared under ideal and distorted source conditions. Management of charging operations for individual EVs is decentralized, as opposed to the centralized control that manages the transformation of power from the AC grid to the DC bus. Switching from vehicle-to-grid mode can be done with little disturbance since the electric power exchange does not need the station and automobiles to be in continual touch with one another. Energy from EV batteries is discharged into the GSS by several Discharging Units (DUs), which are responsible for supplying the grid with both active and reactive power. Here, we provide a fuzzy-based control strategy for EVs that helps the grid while reducing their active and reactive power usage. Active and reactive power flow from the GSS to the grid are determined by the node voltage and available energy in the GSS. Simulations in Matlab and Simulink are used to show how the station operates. The results prove that the proposed model is workable, and the control system is capable of fast DC charging and vehicle-to-grid functioning
Copyright © Neuroquantology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Articles published in the Neuroquantology are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJECSE right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.