Frequency Analysis of Grid Connected EVS by using Artificial Neural Network (ANN)

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, ve...

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Bibliographic Details
Published in:E3S web of conferences 2021, Vol.309, p.1027
Main Authors: Banda, Ranjith Kumar Reddy, Davala, Karunakumar, Padan, Dola Gobinda, Rajagiri, Anil Kumar
Format: Article
Language:English
Subjects:
Artificial neural networks
Decision making
Electric charge
Electric power systems
Electric vehicles
Electricity
Energy charge
Energy consumption
Energy management
Energy storage
Energy usage
Frequency analysis
Hybrid electric vehicles
Idling
Neural networks
Power consumption
Power management
Power sources
Renewable energy
Residential energy
Vehicle-to-grid
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Summary: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.
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202130901027