Deep learning-based power prediction aware charge scheduling approach in cloud based electric vehicular network

Abstract

Electric vehicles (EVs) are considered emerging popularity due to their cost-saving and eco-friendliness nature. The charging of EVs with elevated efficiency is a major issue for users as the hardware facilities, like charging stations are limited. Thus, the EV charging scheduling model is required to discover the charging schedule for each EV. An optimization-driven framework for EV charge scheduling in a Vehicular ad hoc network (VANET) topology is presented in this paper. First, the network simulation with the charge station (CS) and the EV locations are performed. The Deep Maxout network (DMN) is employed for predicting the power. To charge at the charging station, the vehicles are scheduled based on various parametric factors. The proposed Fractional Feedback Tree Algorithm (FFTA) is used to schedule charges in the EV network. The Feedback Artificial Tree (FAT) algorithm and fractional calculus (FC) are combined to create the proposed FFTA. Additionally, a new model of the fitness function is created taking into account variables like the average waiting time, the distance, the predicted power, and the quantity of EVs that are requested to be charged. With the shortest distance of 11.00 km, the highest power of 8.17 J, the shortest average waiting time of 0.47 s, and less number of EVs charged at 4.4, the proposed FFTA offered improved performance.