In the transition to electric fleets around the world, electricity demand from electric vehicle (EV) fleets is expected to become significant in the future. Since fleet cars can display different charging characteristics than individual EVs, analyzing the charging behavior patterns of fleet cars is essential. To do so, this study first examines real EV fleet data from 724 charging events using data analytics methods. Based on this analysis, a charging behavior model is then developed to predict the realistic charging demand of an EV fleet with any number of EVs. In order to overcome the limitations of traditional probability density functions, this study utilizes Gaussian Mixture Models and Kernel distribution in developing charging behaviour models, i.e., charging start and end times, and total charging energy. The models’ behaviours are then compared in terms of goodness-of-fit (GoF) to determine the best match for the original data, in which normalised root mean squared error serving as the fitness criteria.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Modeling and Data Analysis of Electric Vehicle Fleet Charging


    Contributors:


    Publication date :

    2022-06-15


    Size :

    1581925 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Electric vehicle fleet charging system

    HENDRIX WALTER M / HENDRIX SCOTT B | European Patent Office | 2016

    Free access

    European electric vehicle fleet: driving and charging data analysis

    Corchero, C. / Gonzalez-Villafranca, S. / Sanmarti, M. | IEEE | 2014


    Electric Vehicle Fleet Charging System

    HENDRIX WALTER M / HENDRIX SCOTT B | European Patent Office | 2018

    Free access

    CHARGING OF AN ELECTRIC VEHICLE FLEET

    MCGRATH SEAMUS / SARKAR RUEBEN | European Patent Office | 2018

    Free access

    Charging of an electric vehicle fleet

    MCGRATH SEAMUS / SARKAR REUBEN | European Patent Office | 2021

    Free access