A non-linear full vehicle model for simulation of vehicle ride and handling performance is proposed. The model effectively estimates the suspension spring compressions, thus improving the accuracy of normal force calculations. This is achieved by developing models for suspension kinematics, which are then integrated with the commonly used 14 degrees of freedom vehicle dynamics models. This integrated model effectively estimates parameters like camber angles, toe angles and jacking forces, which are capable of significantly affecting the handling performance of the vehicle. The improvements in the accuracy of spring compressions help in simulating the effects of non-linear suspension elements, and the accuracy of handling simulation is enhanced by the improvements in normal force estimates. The model developed in Simulink is validated by comparing the results to that from ADAMS car.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A non-linear vehicle dynamics model for accurate representation of suspension kinematics


    Beteiligte:


    Erscheinungsdatum :

    2015


    Format / Umfang :

    13 Seiten, 19 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




    On-Vehicle Evaluation of Heavy Truck Suspension Kinematics

    Ahmadian, Mehdi / Ahn, Young Kong | SAE Technical Papers | 2003



    On-vehicle evaluation of heavy truck suspension kinematics

    Ahmadian,M. / Ahn,Y.K. / Advanced Vehicle Dynamics Lab.,US et al. | Kraftfahrwesen | 2003


    Neural-Network-Based Suspension Kinematics and Compliance Characteristics and Its Implementation in Full Vehicle Dynamics Model

    Duan, Yupeng / Zhang, Yunqing / Wu, Jinglai | British Library Conference Proceedings | 2022


    Neural-Network-Based Suspension Kinematics and Compliance Characteristics and Its Implementation in Full Vehicle Dynamics Model

    Duan, Yupeng / Zhang, Yunqing / Wu, Jinglai | British Library Conference Proceedings | 2022