Electric vehicle (EV) numbers on the road are increasing day by day and in the coming 5–10 years time, the number will reach almost 30% of the existing vehicle in the day’s population. Similar to the independent system operator (ISO) for the electric power system, the electric utility or the government will have to bring on an electric vehicle fleet operator (EVFO) for managing the vehicles on the road as well as the charging station. The uncertainty in the vehicle mileage is always a worry for EV users. Soon, public charging infrastructures will be deployed and the EV customer who is anxious about the running mileage of the vehicle will tend to choose a path to the destination which has access to a sufficient number of charging stations on the way. Though various routing calculations are existing nowadays, nature-inspired optimization techniques could play a vital role in adapting themselves to such routing problems. Being the most efficient one in terms of precision and accuracy, the ant colony optimization (ACO)-based optimal route scheduling (ORS)—“ACO-ORS”—is proposed in this paper. In this work, it is assumed that multiple EVs are travelling from the source point different to the destinations and all of them with a concern of their running mileage. All the vehicles are operated satisfactorily with optimal routing connecting the charging stations and the results obtained can be proposed as benchmark results for large fleet operations (LFO). Hence, the problem is formulated in such a way that the running EV will reach the destination through the shortest path containing sufficient charging station points. The system is modelled in the MATLAB platform.


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    Title :

    ACO-Based Optimal Route Scheduling for EV Fleet Operation


    Additional title:

    Advs in Intelligent Syst., Computing




    Publication date :

    2021-10-14


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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