Fractional-Sea Lion Optimization Based Routing and Charge Scheduling in Internet of Electric Vehicles

Abstract

The increasing impact of emissions from fuel vehicles accounted to mitigate the emissions worldwide. This study develops a multi-objective model for charge scheduling in the Internet of Electric Vehicles (IoEV). The objective is to create a method for charging EVs in the IoEV network that is energy conscious. Here, the position of the charge station and the location of the EV are used to simulate the IoEV network. Following network simulation, charging planning is completed. First, the proposed Fractional-based Sea lion optimization algorithm (Fractional-SLO), which was developed by combining Fractional calculus (FC) with Sea Lion Optimization (SLO), is used to choose the path. Distance and energy are used to calculate one’s aptitude for selecting a path. The proposed Fractional-SLO algorithm is then used to schedule charges after that. It is now possible to model the fitness for charge scheduling using delay and energy cost. The proposed Fractional-SLO promised improved performance with a 0.279-min delay and a 20.337-km distance. When 50 vehicles are involved, the proposed method produces delays that are, respectively, 60.21%, 64.87%, 14.69%, and 17.56% smaller than those of the existing methods, namely MDP, Joint EV Routing and Charging Discharge Scheduling Strategy, and Aggregate Cost Perspective.