A Hybrid Optimization-Based Artificial Neural Network Model for Wireless Power Transfer in Electric Vehicles

Abstract

Electric Vehicles (EVs) are powered by a battery mounted in the vehicle, which powers the motor and drives the wheels. Most commercial EVs can be charged by plugging them into a charging station. Such conductive recharging has various drawbacks, including physical plugging of the cable, safety concerns, and charging time. Manually charging EVs might be dangerous due to the chance of an electric spark or disaster. Advances in Wireless Power Transfer (WPT) demonstrate the capacity to transfer significant amounts of electricity over short and medium-range distances. The ultimate purpose of this paper is to improve the efficacy of electric car wireless charging systems. Here, a hybrid optimization-based Artificial Neural Network (ANN) model is applied to improve the efficacy of the WPT model in EVs. To optimize the weights of the ANN classifier, a hybrid approach termed as Grasshopper-Assisted Elephant Herd Optimization (GA-EHO) method is proposed. The GA-EHO is derived through the hybridization of Elephant Herd Optimization (EHO) Algorithm and the Grasshopper Optimization Algorithm (GOA) techniques. Finally, the experimental study reveals that at 70% learning rate, the proposed ANN system achieves a minimal MSE value of 0.0528, which is lower than other current classifiers, such as SVM, LSTM, and CNN.