A Mean Value Model of the Exhaust System with SCR for an Automotive Diesel Engine

Nowadays requirements towards a reduction in fuel consumption and pollutant emissions of Internal Combustion Engines (ICE) keep on pushing manufacturers to improve engines performance through the enhancement of existing subsystems (e.g.: electronic fuel injection, air systems) and the introduction of specific devices (e.g.: exhaust gas recirculation systems, SCR, …). Modern systems require a combined design and application of different after-treatment devices. Mathematical models are useful tools to investigate the complexity of different system layouts, to design and to validate (HIL/SIL testing) control strategies for the after-treatment management.

This study presents a mean value model of an exhaust system with SCR; it has been coupled with a common rail diesel engine combustion black box model (Neural Network based). So, dedicated models for exhaust pipes, oxidation catalyst, diesel particulate filter and selective catalytic converter are developed. With this model a simulation study on a DOC-DPF-SCR exhaust system is performed, showing a good coherence with experimental data. This model has been intended as a flexible tool to perform the simulation of exhaust system behaviour for after-treatment control and diagnostic strategies development as well as system architecture analysis. On light-duty drive cycle, the behaviour of the after-treatment system applied to an Euro 5 B-segment vehicle is evaluated. The simulations have highlighted the necessity of accurate SCR control strategies to improve the warm-up phase and optimize reactant dosing.