Variable Valve Actuation (VVA) technology provides high potential in achieving high performance, low fuel consumption and pollutant reduction. However, more degrees of freedom impose a big challenge for engine characterization and calibration. In this study, a simulation based approach and optimization framework is proposed to optimize the setpoints of multiple independent control variables. Since solving an optimization problem typically requires hundreds of function evaluations, a direct use of the high-fidelity simulation tool leads to the unbearably long computational time. Hence, the Artificial Neural Networks (ANN) are trained with high-fidelity simulation results and used as surrogate models, representing engine's response to different control variable combinations with greatly reduced computational time. To demonstrate the proposed methodology, the cam-phasing strategy at Wide Open Throttle (WOT) is optimized for a dual-independent Variable Valve Timing (VVT) engine. The optimality of the cam-phasing strategy is validated with engine dynamometer tests.


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    Title :

    Cam-Phasing Optimization Using Artificial Neural Networks as Surrogate Models-Maximizing Torque Output


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    Powertrain & Fluid Systems Conference & Exhibition ; 2005



    Publication date :

    2005-10-24




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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