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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    Titel :

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


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Wu, Bin (Autor:in) / Kramer, Denise M. (Autor:in) / Ohl, Gregory L. (Autor:in) / Prucka, Robert G. (Autor:in) / Filipi, Zoran (Autor:in)

    Kongress:

    Powertrain & Fluid Systems Conference & Exhibition ; 2005



    Erscheinungsdatum :

    2005-10-24




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch






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    2006-01-1512 Cam-phasing Optimization Using Artificial Neural Networks as Surrogate Models-Fuel Consumption and NOx Emissions

    Wu, B. / Prucka, R.G. / Filipi, Z.S. et al. | British Library Conference Proceedings | 2006


    Torque based combustion phasing control

    Larsson, Stefan | TIBKAT | 2005