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Fractional Feedback Political Optimizer with Prioritization-based Charge Scheduling in Cloud-assisted Electric Vehicular Network
Balasubramaniam S and K. Satheesh Kumar
Purpose: The Electric Vehicles (EVs) are discovered by various manufacturers for protecting the environment from the CO2 emissions. The EVs are operated based on the battery power where the battery is charged from the Charging Station (CS). The cloud-based EV charging model comprised of cloud, charging station, internet and EV charging scheduling model. The conventional charge scheduling approaches is not successful always when multiple EV is waiting in the CS. Thus, it is necessary to introduce an effective charge scheduling model.
Design/Methodology/Approach: This paper introduced a Fractional Feedback Political Optimizer (FFPO)-based charge scheduling model for recharging the EVs, where the scheduling is based on the priority of EVs. The network is simulated initially, and then the charging station is selected based on Fractional Feedback Tree Algorithm (FFTA). The power prediction is carried out using Deep Maxout Network(DMN). Then, the priority-based charge scheduling is carried out using FFPO algorithm in order to recharge the EVs.
Findings: The experimental results demonstrates that the developed FFPO algorithm attained the distance of 15.344 km, power of 2.6455 J, Average waiting time of 2.544 second and Number of vehicles charged is 9.
Originality/Values: The proposed FFPO is designed by incorporating FFTA and Political Optimizer (PO) in which the fitness parameters, like priority, response time, and delay are considered. The FFTA is derived by the integration of Fractional Calculus (FC) and Feedback Artificial Tree (FAT) Algorithm.
Keywords: Charging station, Electric vehicle,Fractional Calculus, Feedback Artificial Tree Algorithm, Political Optimizer