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This work describes the application of Non-Linear Autoregressive Models with Exogenous Inputs (NLARX) in order to predict the NOx emissions of heavy-duty diesel engines. Two experiments are presented: 1.) a Non-Road-Transient-Cycle (NRTC) 2.) a composition of different engine operation modes and different engine calibrations. Data sets are pre-processed by normalization and re-arranged into training and validation sets. The chosen model is taken from the MATLAB Neural Network Toolbox using the algorithms provided. It is teacher forced trained and then validated. Training results show recognizable performance. However, the validation shows the potential of the chosen method.

Traditionally, university research in engine technology has been focused on fundamental engine phenomena. Increasingly however, research topics are developing in the form of systems issues. Examples include air and exhaust gas recirculation (EGR) management, after-treatment systems, engine cooling, hybrid systems and energy recovery. This trend leads to the need for engine research to be conducted using currently available products and components that are re-configured or incrementally improved to support a particular research investigation. A production engine will include an electronic control unit (ECU) that must be understood and utilised or simply removed and circumvented. In general the intellectual property (IP) limitations places on ECUs by their suppliers mean that they cannot be used. The supplier of the ECU is usually unable to reveal any detail of the implementation. As a consequence any research using production hardware is seriously disadvantaged from the beginning.