An accurate energy consumption forecast is crucial to accomplish the turn to electric vehicles. Especially the fear of breaking down due to running out of power discourages people purchasing electric cars. An accurate driving range prediction increases the usable driving range since less energy reserves are needed. In most cases, the energy consumption is computed by means of map and vehicle information only. The individual driving behavior of the motorist is barley considered. However, the habitual speed the driver chooses, or his braking and acceleration habits have a major impact on the energy usage. In this work, we create a driver profile of the individual driver whilst driving and improve the energy estimation forecast thereby. The driver profile consists of the driving-style and habitual parameters, like the habitual highway speed or the commonly used acceleration. With regards to the parameters gathered in this profile, a driver-specific speed profile for the planned track or the track ahead respectively, is predicted. The predicted speed profile, together with the height profile of the track, on-line traffic information, and a vehicle model leads to an improved, driver-specific energy forecast.


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    Titel :

    Driver Individual Energy Consumption Forecast


    Beteiligte:


    Erscheinungsdatum :

    2014


    Format / Umfang :

    8 Seiten, Bilder, Tabellen, 10 Quellen



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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