An Efficient Dynamic Wireless Charging Methodology with Load Forecasting and Improved Gray Wolf Optimization for Electric Vehicles

Electric vehicles (EVs) are powered by electric motors, which draw energy from rechargeable batteries. The vehicles can provide a greener and more energy-efficient mode of transportation as compared to traditional gasoline engines. The EDWLIG model incorporates a combination of EV charging and grid communication through state-of-the-art wireless control, forecasting, and optimization. It brings adaptation of Constant Voltage (CV), Constant Current (CC), and Constant Voltage-Constant Current (CVCC) modes to battery conditions to regulate the safe. The model features an EV Load Forecasting Module that helps to predict charging requirements, a VPP-Based incentive willingness model that aims at getting users to participate in grid services, and an Improved Gray Wolf Optimization (I-GWO) algorithm as a tool for the productivity enhancement. The Energy Management Strategy (EMS) acts like a traffic controller for energy flowing from different sources to satisfy power demand while still observing the set limits. This analysis focuses on charging efficiency, energy management, power rating, voltage support, and temperature management.