This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts


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    Titel :

    Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models


    Beteiligte:


    Erscheinungsdatum :

    2022


    Format / Umfang :

    1 Online-Ressource (192 p.)



    Medientyp :

    Buch


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt