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Automotive applications equipped with Diesel engines are subject to stringent legislative requirements in terms of particulate matter (PM) emissions. A Diesel particulate filter (DPF) reduces the tailpipe PM content in the exhaust gas to fulfill the emission thresholds of law's authority. As an emission relevant component, the DPF efficiency must be monitored according to the on-board diagnostic (OBD) regulation. In the US and European market, these OBD regulation limits are becoming more tightened. Currently established methods rely on the analysis of the differential pressure sensor signal across DPF. The measured pressure loss depends on a lot of disturbance variables. Thus, monitoring concepts based on differential pressure become increasingly difficult to comply with future OBD legislation. New developed PM sensors offer the possibility to detect and quantify the particulate emissions in DPF downstream position. This information can be processed in a DPF efficiency OBD monitor.

In modern engine control applications, there is a distinct trend towards model-based control schemes. There are various reasons for this trend: Physical models allow deeper insights compared to heuristic functions, controllers can be designed faster and more accurately, and the possibility of obtaining an automated application scheme for the final engine to be controlled is a significant advantage. Another reason is that if physical effects can be separated, higher order models can be applied for different subsystems. This is in contrast to heuristic functions where the determination of the various maps poses large problems and is thus only feasible for low order models. One of the most important parts of an engine management system is the air-to-fuel control. The catalytic converter requires the mean air-to-fuel ratio to be very accurate in order to reach its optimal conversion rate. Disturbances from the active carbon filter and other additional devices have to be compensated.