A Model-Based Technique for Spark Timing Control in an SI Engine Using Polynomial Regression Analysis

Model-based methodologies for the engine calibration process, employing engine cycle simulation and polynomial regression analysis, have been developed and the reliability of the proposed method was confirmed by validating the model predictions with dynamometer test data. From the results, it was clear that the predictions by the engine cycle simulation with a knock model, which considers the two-stage hydrocarbon ignition characteristics of gasoline, were in good agreement with the dynamometer test data if the model tuning parameters were strictly adjusted. Physical model tuning and validation were done, followed by the creation of a dataset for the regression analysis of charging efficiency, EGR mass, and MBT using a 4th order polynomial equation. The stepwise method was demonstrated to yield a logarithm likelihood ratio and its false probability at each term in the polynomial equation. The use of false acceptance probability enables an informed decision to be made with regard to the tradeoff between polynomial equation size and goodness of fit. The reliability of the logic was investigated by implementing the regression models into MBT control logic. The MBT outputs are in good agreement with dynamometer test data for different intake valve timing and RON near the operational conditions under which the model tuning was made.