Search Results

The purpose of this study is to develop control-oriented modeling methodology and apply to an actual control design in turbocharged spark ignition engines. A grey-box modeling approach was adapted to accelerate the system calibration time, while providing accurate system dynamics. An engine simulator based on first principles models was utilized to investigate the statistical model derivation process. A recursive least squares method with forgetting factor was employed to estimate model parameters related to turbocharger and vehicle/drivetrain behaviors, which seemed to be major factors causing delay of turbocharger system. The concept was demonstrated through its application to the actual control design, and the reliability of the proposed method was theoretically investigated. According to the model evaluation results, approximated behavior models are in good agreement with time series data yielded by the engine simulator under various transient operations.

The purpose of this study is to develop model-based methodologies which employ thermo-fluid dynamic engine simulation and multiple-objective optimization schemes for engine control and calibration, and to validate the reliability of the method using a dynamometer test. In our technique, creating a total engine system model begins by first entirely capturing the characteristics of the components affecting the engine system's behavior, then using experimental data to strictly adjust the tuning parameters in physical models. Engine outputs over the full range of engine operation conditions as determined by design of experiment (DOE) are simulated, followed by fitting the provided dataset using a nonlinear response surface model (RSM) to express the causal relationship among engine operational parameters, environmental factors and engine output. The RSM is applied to an L-jetronic® air-intake system control logic for a turbocharged engine.

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 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.

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.

Time-resolved continuous images of wrinkling flame front cross-sections were acquired by a laser-light sheet technique in an optically accessible spark ignition engine. The test engine was operated at various engine speeds and compression ratios. The fractal dimension of the curve, D2, was measured in a time series for each cycle. Analysis of the data shows that as the flame propagates the fractal dimension, D2, is close to unity a short time after spark ignition and then increases. Examination of the relationship between the growth rate of the fractal dimension, ΔD2/Δt, and D2 reveals that the higher D2 is, the lower ΔD2/Δt becomes. An Empirical equation for ΔD2/Δt was derived as a function of the ratio of the turbulence intensity to the laminar burning velocity and pressure. This model was tested in an SI engine combustion simulation, and results compared favorably with experimental data.