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Diesel engines provide a solution for the reduction of carbon dioxide (CO2) from motor vehicles. For diesel engines, however, technology to reduce nitrogen oxide (NOx) emissions is essential. This report focuses on Urea - Selective Catalytic Reduction (SCR) as an aftertreatment system for NOx reduction. The NOx conversion performance of SCR catalyst depends on exhaust gas temperature and the NO2/NOX ratio. In order to raise the NO2/NOX ratio, it is essential to raise the temperature of oxidation catalyst. For these purposes, it is necessary to raise the temperature of oxidation catalyst and SCR catalyst to high level in order to enhance NOx conversion. Temperature rising is implemented by in-cylinder fuel injection (post-injection).

To detect malfunctions of the EGR system of a passenger car diesel engine, a neural network approach was selected using Self Organizing Maps (SOM). Self Organizing Maps are self-learning technologies that can be used to retrieve typical data patterns in large data sets. This technology is very efficient for identifying if patterns from a new, modified or changed system are similar to already existing patterns. The SOM outputs a measure of similarity to ‘typical system behavior patterns’. As an OBD function, this value is a measure for system anomaly detection. Performing dynamic tests using standard driving cycles, not only was the occurrence of a malfunction within the EGR system detected by the neural network, the cause of the malfunction could also be identified.