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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.

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 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.

In recent years, integrated vehicle control systems have been developed to improve fuel economy and safety. As a result, engine control is shifting to torque-based systems for throttle / fuel / ignition control, to realize an engine torque demand from the system. This paper describes torque-based engine control technologies for SI (Spark Ignition) engine to improve torque control accuracy using a feedback control algorithm and an airflow sensor.