With the idea of designing new electric vehicles that reach high energy efficiencies by drastically reducing the curb weight, a completely new problem arises with respect to driving dynamics. The weight of everyday load has an increasing influence on the car's mass and inertia properties as well as on the tire properties. Thus, vehicle dynamics and its parameters change with every trip. This work shows the consequences on passive behavior for different everyday load scenarios. An estimation algorithm is proposed that is able to determine the trip-individual parameters online and to provide vehicle dynamics controllers with adapted values. Looking at future vehicles with very low curb weights of around 500 kg, the considerable influence of typical everyday load scenarios has been shown. Relative shifts in inertia or tire parameters of over 30 % are easily achieved. Thus, cars loaded in a special manner show doubled maximal amplitudes in lateral acceleration and side slip angle than other vehicles loaded differently. To adapt vehicle dynamics controllers by providing trip-individual parameters, an estimation algorithm based on an Unscented Kalman Filter was proposed. It is able to estimate all load-sensitive parameters during the first meters of driving. Therefore, a vehicle model was used that extends the single-track model by several effects. This way, relative estimation errors below 10 % could be reached.


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    Titel :

    Load problem of lightweight electric vehicles and solution by online model adaptation


    Beteiligte:


    Erscheinungsdatum :

    2014


    Format / Umfang :

    22 Seiten, Bilder, Tabellen, Quellen


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch





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