Quality and performance are two important customer requirements in vehicle design. Driveline clunk negatively affects the perceived quality and must be therefore, minimized. This is usually achieved using engine torque management, which is part of engine calibration. During a tip-in event, the engine torque rate of rise is limited until all the driveline lash is taken up. However, the engine torque rise, and its rate can negatively affect the vehicle throttle response. Therefore, the engine torque management must be balanced against throttle response. In practice, the engine torque rate of rise is calibrated manually. This paper describes a methodology for calibrating the engine torque in order to minimize the clunk disturbance, while still meeting throttle response constraints. A set of predetermined engine torque profiles are calibrated in a vehicle and the transmission turbine speed is measured for each profile. The latter is used to quantify the clunk disturbance. Using the engine torque profiles and the corresponding turbine speed responses, a time-dependent metamodel is created using principal component analysis and Kriging. The metamodel predicts the turbine speed response due to any engine torque profile and is used in a subsequent optimization which minimizes a clunk disturbance measure while still meeting the throttle response target. The obtained optimal engine torque profile and the corresponding turbine speed were successfully validated experimentally. Compared with the production calibration, the clunk disturbance was reduced by thirty three percent and the throttle response was simultaneously improved by eleven percent.


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    Titel :

    Optimal Engine Torque Management for Reducing Driveline Clunk Using Time - Dependent Metamodels


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:

    Kongress:

    SAE 2007 Noise and Vibration Conference and Exhibition ; 2007



    Erscheinungsdatum :

    15.05.2007




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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