E3S Web Conf.
Volume 309, 20213rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|Number of page(s)||4|
|Published online||07 October 2021|
Frequency Analysis of Grid Connected EVS by using Artificial Neural Network (ANN)
EEE Department, GRIET, Hyderabad
* Ranjith Kumar Reddy Banda: email@example.com
The vehicle-to-grid (V2G) model is able to provide the power-systems that have been built to incorporate the hybrid electric vehicle model on a wide scale with distributed reserve. The authors suggested an amended V2G control model that would concurrently manage different renewable power sources, vehicle idle time and electricity generation on a vehicle consumer day basis. In respect of the desired status of battery and the detected plug-on terminal, vehicle-to-grid power is tested. This article presents an intelligent decision- making system based on an artificial neural network (ANN) that uses data logged by the M2MAMI for the planning and management of electricity charge. The ANN has been trained with household energy usage and EV energy requires the data and convention to determine when to charge the vehicle (G2V) or to discharge it (V2G). Charge Terminology, Electric Cars, Energy storage, Neural Network. Charge Scheduling. In this paper, MATLAB/Simulink implements the proposed control block. Different virtual images evaluate the performance of the control structure, interface, communications, device efficiency and time responses.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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