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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.