This paper discusses some specific data-driven model structures suitable for prediction of NOX and soot emissions from a diesel engine. The model structures can be described as local linear regression models where the regression parameters are defined by two-dimensional look-up tables. It is highlighted that this structure can be interpreted as a B-spline function. Using the model structure, models are derived from measured engine data. The smoothness of the derived models is controlled by using an additional regularization term and the globally optimal model parameters can be found by solving a linear least-squares problem. Experimental data from a 5-cylinder Volvo passenger car diesel engine is used to derive NOX and soot models, using a leave-one-out cross validation strategy to determine the optimal degree of regularization. The model for NOX emissions predicts the NOX mass flow with an average relative error of 5.1% and the model for soot emissions predicts the soot mass flow with an average relative error of 29% for the measurement data used in this study. The behavior of the models for different engine management system settings regarding boost pressure, amount of exhaust gas recirculation, and injection timing has been studied. The models react to the different engine management system settings in an expected way, making them suitable for optimization of engine management system settings. Finally, the model performance dependence on the selected model complexity, and on the number of measurement data points used to derive the models has been studied.
The development and implementation of a diesel engine combustion system simulation model is described. The model is a crank angle based combustion model, which uses the conditions in the intake and exhaust manifolds together with the fuel injection signal from the engine control unit to estimate the in-cylinder pressure throughout a complete combustion cycle. The model is implemented in Matlab. Furthermore, a Simulink coupling has been developed and implemented such that the combustion model can be connected directly to a Simulink mean value model of an engine air system. The coupling makes the combustion model act like a continuous source and a continuous sink in a mean value model. The coupling makes it possible to continuously simulate an engine in steady-state or transient operation, while the combustion model produces estimated cylinder pressure traces for each combustion cycle. This makes it possible to estimate fuel consumption and to couple the model with emission models which use the cylinder pressure or the rate of heat release as input. The model is developed, calibrated and verified using measured data from a 2.4 liter Volvo diesel engine, equipped with a turbocharger, an exhaust gas recirculation system, and a common rail injection system. The combustion model estimates IM EP net with a correlation factor of 0.995 for the used data. The simulation time is in the range between 1 and 25 milliseconds for one combustion cycle on a standard computer, depending on the implementation.
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