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The paper provides an overview of a developed methodology and a toolchain for modeling and control of a complex aftertreatment system for passenger cars. The primary objective of this work is to show how the use of this methodology allows to streamline the development process and to reduce the development time thanks to a model based semi-automatic control design methodology combined with piece-wise optimal control. Major improvements in passenger car tailpipe NOx removal need to be achieved to fulfil the upcoming post EURO 6 norms and Real Driving Emissions (RDE) limits. Multi-brick systems employing combinations of multiple Selective Catalytic Reduction (SCR) catalysts with an Ammonia Oxidation Catalysts, known also as Ammonia Clean-Up Catalyst (CUC), are proposed to cover operation over a wide temperature range. However, control of multi-brick systems is complex due to lack of available sensors in the production configurations.

Turbocharger maps measured on a gas stand test bench are commonly used to represent turbine and compressor performance. The maps are useful source of information for mean value modeling, engine calibration optimization, virtual sensing and feedback control design. For some tasks, representing the maps by fitted functional forms can be more convenient than using interpolation of the map data directly. The functional representation usually allows for wider extrapolation ranges and more reliable application of numerical optimization methods. In literature most successful functional forms chosen to represent the compressor flow characteristics are based on rational polynomials of dimensionless head and flow parameters. Turbine flow characteristics, on the other hand, are commonly modeled as orifices or orifices with variable cross-section in case of variable geometry turbines (VGT).

Over the past few years, innovative engine layouts have enabled significant reductions in both fuel consumption and pollutant emissions. However, exponential growth of powertrain control strategies complexity has inevitably accompanied these achievements. As a result, control and calibration development time and effort have become an ever-growing concern in powertrain design. An illustrative example of this complexity is Diesel Particulate Filters (DPF), which requires periodic regeneration to eliminate the accumulated soot. The main challenge for a DPF is to enhance the efficiency of these regeneration events, which depend largely on the quality of the regeneration temperature control. In this paper, we describe the DPF regeneration process, especially the main constraints and identification tests. We then give a simulation based comparison of two model based control solutions for the DPF thermal control during regeneration.