With the tendency towards lightweight vehicles a new problem arises. Due to the high ratio of load to empty weight, driving dynamics is influenced. Passengers and luggage limit especially model-based driving dynamics systems as the physical parameters experience a higher change compared to common cars. This problem is analyzed in detail. Furthermore, an algorithm is presented, that is able to determine these vehicle parameters online while driving, without the need for any further sensors than standard ESC equipment. The filter is tested with a multi-body simulation model of the electric lightweight car MUTE. It was shown that passengers and luggage load do have an enormous influence on the physical vehicle parameters of future lightweight cars compared to nowadays vehicles. To determine those values, an Unscented Kalman Filter was proposed that is able to estimate mass, yaw moment of inertia and COG position based on a normal car ride to improve vehicle dynamics controllers. The included nonlinear single-track model was extended on the one hand by longitudinal dynamics and, on the other hand, by a combined tire-suspension model. Thus, the algorithm is able to determine its own tire model at the same time is estimates the other parameters.


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    Titel :

    Online estimation of physical vehicle parameters with ESC sensors for adaptive vehicle dynamics controllers


    Beteiligte:


    Erscheinungsdatum :

    2013


    Format / Umfang :

    19 Seiten, 11 Bilder, 2 Tabellen, 11 Quellen


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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