Model-Based Calibration Process for Producing Optimal Spark Advance in a Gasoline Engine Equipped with a Variable Valve Train

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. It is shown that the interaction terms, including the independent variables of the engine speed, load and additional controllable parameters, play an important role in the approximation of engine responses. It is also shown that a squared multiple correlation coefficient adjusted for the degree of freedom was available for obtaining the optimized polynomial regression model. This model-based methodology was applied to the optimal spark advance calibration in an intake variable valve timing engine system. The results show that on the whole, the regression model agrees with the experimental responses.