Mitigation of Complexity in Charging Station Allocation for EVs using Chaotic Harris Hawks Optimization Charge Scheduling Algorithm

Abstract

The development of EV technology and EV migration is limited by various factors such as sizing of batteries, short driving ranges, optimal operations, and so on. The EV charging faces many difficulties such as waiting time, charging time, uneven charge scheduling, and uneven distributed charging stations. In Charging Station (CS), EVs usually spend much time in queues, mainly during peak hours of charging. Therefore, building a well-established charging station network should be derived from charging demand and proper charge scheduling to assist EVs for getting charged with less cost, less waiting time. Also, it should reduce the number of vehicles scheduling during the peak load time. This work aims to design a scheduling system for EV charging using an optimization strategy of Chaotic Harris Hawks optimization (CHHO) which reduces the total time spent on charging station and the distance of EV origin to destination. CHHO is authenticated using Vehicular Ad-hoc Network (VANET) simulation, and the performances are compared with algorithms Exponential Harris Hawk Optimization, Grey Wolf Optimizer, First in First Out and Random Allocation to demonstrate the efficacy of our technique. The proposed CHHO-based scheduling system yields better performance with the maximum remaining energy and significantly cuts the average travel time, and improves the utilization rate of EVs in charging stations compared to other algorithms. A detailed result and discussions on different case studies by varying number of vehicles and number of charging stations and the corresponding average waiting time were obtained and presented in this paper.