To solve the problem that control system of the intelligent vehicle is hard to measure the vehicle mass and road gradient, this paper built a longitudinal dynamics model of vehicle. Based on theoretical model, discrete steady-state Kalman filter was used to estimate gradient of slope and vehicle mass, and simulation platform was established by Carsim and Maltab/Simulink to verify the accuracy and instantaneity of the algorithm. A proper acceleration sensor was selected, according to the stable Kalman filter theory. A real test was conducted, and the instantaneity and accuracy of this method for vehicle mass and road slope was verified by comparing with the data from inertial navigator.


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    Titel :

    Estimation of vehicle mass and road slope based on steady-state Kalman filter


    Beteiligte:
    Hao, Shengqiang (Autor:in) / Luo, Peipei (Autor:in) / Xi, Junqiang (Autor:in)


    Erscheinungsdatum :

    01.10.2017


    Format / Umfang :

    733424 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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