Soft Computing Techniques Implication for the Exhaustive Intelligent Review Analysis for Electric Vehicle Charging System

Abstract

This paper shows the detailed intelligent monitoring survey analysis of an electric vehicle's charging system from various generating sources by using soft computing techniques. The different modes of charging, such as the conductive and inductive charging methods, along with their efficiency, power output, and cost, have been discussed. The performance parameters like state of charge (SOC) and THD (%) of various existing literature have been analyzed. The purpose of this study is to compare the available Electric Vehicle Charging Technologies and figure out the best possible alternative for present-day traction scenarios, as well as use the results to identify the shortcomings of the present technologies. The results of this study can be further utilized in the fabrication of new and flawless EV charging technologies that are both cost-and power-efficient, offer hassle-free traction, and make the transition from ICE to EV a viable alternative for customers. At the end of this study, it is observed that performance parameters have been greatly improved with battery swap technology in comparison to other methodologies, which mainly comprise Fuzzy Logic Control (FLC) and Artificial Neural Networks (ANN).