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The increasing number of controllable parameters in modern engine systems leads to complicated and enlarged engine control software. This in turn has led to dramatic increases in software development time and costs in recent years. Model-based control design seems to be an effective way to reduce development time and costs. In the present study, we have developed model-based methodologies for the engine calibration process using an engine cycle simulation technique combined with a regression analysis of engine responses. From the results it was clear that the engine cycle simulation technique was useful in the engine calibration process, if the empirical parameters included in physical models were adjusted at typical sampling-points in several engine speeds and loads. The cycle simulation produced a multi-dimensional MBT map, and a response surface method was employed in the modeling of the engine map dataset using a polynomial equation.

The increasing number of controllable parameters in modern engine systems has led to increasingly complicated and enlarged engine control software. This in turn has created dramatic increases in software development time and cost. Model-based control design seems to be an effective way to reduce development time and costs and also to enable engineers to understand the complex relationship between the many controllable parameters and engine performance. In the present study, we have developed model-based methodologies for the engine calibration process, employing engine cycle simulation and regression analysis. The reliability of the proposed method was investigated by validating the regression model predictions with measured data.