Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3656
Title: Neural Network-Based PV Powered Electric Vehicle Charging Station
Authors: Barker, Teja
Ghosh, Arnab
Keywords: Electric Vehicles
Battery energy storage system
Adaptive Neuro-Fuzzy Inference System
MPPT
PID controller
Neural Network
PV
Issue Date: Feb-2022
Citation: Maiden Edition of IEEE Delhi Section International Conference on Electrical, Electronics and Computer Engineering. (DELCON 2022), Virtual mode, 11–13 February 2022
Abstract: Global warming and some dangerous climate changes are becoming more prevalent as the demand for modern transportation systems grows for economic development and cultural comfort. To tackle this global warming issue due to transportation every country pushing for Electric Vehicles (EVs), As the number of electric vehicles on the road rises, Charging EVs with a fossil fuel-based infrastructure alone is not cost-effective or efficient. As a result, a charging station based on renewable energy has enormous potential and control for electric vehicle charging. In the current scenario, a solar-powered electric vehicle charging station and a Battery Energy Storage System (BESS) are required. Additional grid assistance is recommended to ensure that the charging station has uninterrupted power without putting additional burden on the grid, For effective power management in the charging station between solar, BESS, grid, and EVs an efficient charging station design with Adaptive Neuro-Fuzzy Inference System (ANFIS) voltage-controlled MPPT, PID controller, Grid with Neural Network technique is designed and evaluated in MATLAB/Simulink.
Description: Copyright belongs to proceeding publisher
URI: http://hdl.handle.net/2080/3656
Appears in Collections:Conference Papers

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