An overall diesel engine and aftertreatment system model has been created that integrates diesel engine, exhaust system, engine emissions, and diesel particulate filter (DPF) models using MATLAB Simulink. The 1-D engine and exhaust system models were developed using WAVE. The engine emissions model combines a phenomenological soot model with artificial neural networks to predict engine out soot emissions. Experimental data from a light-duty diesel engine was used to calibrate both the engine and engine emissions models. The DPF model predicts the behavior of a clean and particulate-loaded catalyzed wall-flow filter. Experimental data was used to validate this sub-model individually.
Several model integration issues were identified and addressed. These included time-step selection, continuous vs. limited triggering of sub-models, and code structuring for simulation speed. Required time-steps for different sub models varied by orders of magnitude. A system of controllers were implemented which limited the triggering of sub-models with very small time-steps so that simulation speed was maintained while minimizing the adverse effects on calculation accuracy.
Integration of the models allowed for the visualization of dynamic interactions between sub-models that were not seen when simulating individual components. An example of which was an interesting filter pressure drop overshoot during a speed-step transient simulation.
In both steady state and transient simulations, overall model results fit expectations. Three steady-state cases (a baseline, an increased fueling, and an increased engine speed) and two transient cases (baseline to increased fueling and baseline to increased speed) were analyzed. While numerous results were studied, pressure drop across the filter was emphasized.
Reasonable trends were observed. The system developed in this study will assist in the design and optimization of diesel automotive systems for reduction of tailpipe emissions.