Abstract The calibration phase of a new engine at test bench is an expensive and time-consuming process. To support the engine development process, in this paper a numerical methodology aiming to define the optimal control parameters is proposed for a downsized VVA SI engine. First, a 1D engine mod...
Abstract The calibration phase of a new engine at test bench is an expensive and time-consuming process. To support the engine development process, in this paper a numerical methodology aiming to define the optimal control parameters is proposed for a downsized VVA SI engine. First, a 1D engine model is build-up in GT-Power and is enhanced with phenomenological sub-models. 1D model is then validated against the experimental findings, at high- and part-load operations. In a second stage, a numerical calibration strategy is defined, to automatically identify, for various engine loads/speeds, the control parameters, ensuring optimal performance and complying with proper system limitations. Complete engine maps are computed for different control strategies (EIVC and Throttled). An application example is also presented, where computed maps are embedded in a vehicle model to predict the CO2 emission produced along a NEDC cycle.
Abstract The calibration phase of a new engine at test bench is an expensive and time-consuming process. To support the engine development process, in this paper a numerical methodology aiming to define the optimal control parameters is proposed for a downsized VVA SI engine. First, a 1D engine model is build-up in GT-Power and is enhanced with phenomenological sub-models. 1D model is then validated against the experimental findings, at high- and part-load operations. In a second stage, a numerical calibration strategy is defined, to automatically identify, for various engine loads/speeds, the control parameters, ensuring optimal performance and complying with proper system limitations. Complete engine maps are computed for different control strategies (EIVC and Throttled). An application example is also presented, where computed maps are embedded in a vehicle model to predict the CO2 emission produced along a NEDC cycle.
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