Vehicle speed is one of the important quantities in vehicle dynamics control. Estimation of the slope angle is in turn a necessity for correct dead reckoning from vehicle acceleration. In the present work, estimation of vehicle speed is applied to a hybrid vehicle with an electric motor on the rear axle and a combustion engine on the front axle. The wheel torque information, provided by electric motor, is used to early detect excessive wheel slip and improve the accuracy of the estimate. A best-wheel selection approach is applied as the observation variable of a Kalman filter which reduces the influence of slipping wheels as well as reducing the computational effort. The performance of the proposed algorithm is illustrated on a test data recorded at a winter test ground with excellent results, even for extreme conditions such as when all four wheels are spinning.


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    Titel :

    Longitudinal velocity and road slope estimation in hybrid electric vehicles employing early detection of excessive wheel slip


    Beteiligte:
    Klomp, Matthijs (Autor:in) / Gao, Yunlong (Autor:in) / Bruzelius, Fredrik (Autor:in)


    Erscheinungsdatum :

    2014


    Format / Umfang :

    17 Seiten, 16 Bilder, 3 Tabellen, 13 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch