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Integration of Diesel Engine, Exhaust System, Engine Emissions and Aftertreatment Device Models

机译：柴油发动机，排气系统，发动机排放和后处理装置型号集成

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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 were 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 were used to validate this sub-model individually. Several model integration issues were identified and addressed. These included time-step selection, continuous versus 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.
机译：已经创建了一个整体柴油发动机和后处理系统模型，其使用Matlab Simulink集成了柴油发动机，排气系统，发动机排放和柴油机微粒过滤器（DPF）模型。使用波开发了1-D发动机和排气系统模型。发动机排放模型将具有人工神经网络的现象学烟灰模型结合起来预测发动机出烟灰排放。来自轻型柴油发动机的实验数据用于校准发动机和发动机排放模型。 DPF模型预测干净和颗粒状催化壁流过滤器的行为。实验数据用于单独验证该子模型。确定并解决了几个模型集成问题。这些包括时间步骤选择，连续与子模型的有限触发，以及用于仿真速度的代码结构。不同子模型所需的时间步骤因级数而变化。实施了一种控制器系统，限制了具有非常小的时间步长的子模型的触发，以便保持模拟速度，同时最小化对计算精度的不利影响。模型集成允许在模拟各个组件时未见的子模型之间的动态交互的可视化。一个例子是在速度步进瞬态仿真期间是一个有趣的滤波压力下降过冲。在稳态和瞬态模拟中，总体模型结果适合期望。分析了三种稳态案例（基线，增强的发动机速度增加）和两个瞬态案例（增加加油和增加速度的基线）。研究了许多结果，强调了过滤器穿过过滤器的压降。观察到合理的趋势。本研究开发的系统将有助于设计和优化柴油汽车系统，以减少尾管排放。

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