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Basic knowledge about the reaction kinetics of the selective catalytic reduction (SCR) as well as measurement data from a dynamometer are used for the design of a physical mean-value model of an SCR catalytic converter system. The converter system consists of an injection device for urea solution and a coated metallic honeycomb-type converter. It is mounted in the tailpipe of a mobile, heavy-duty diesel engine. The core of the catalytic converter model is a series of identical SCR cells describing the thermal and chemical behavior of the SCR catalytic converter. It may be used to design dynamic, model-based feedforward controllers for the injection of reducing agent. Measurements on the dynamometer show that these controllers significantly improve the performance of the SCR system.

An advanced controller for a urea SCR (Selective Catalytic Reduction) catalytic converter system for a mobile heavy-duty diesel engine is presented. The after-treatment system is composed of the injecting device for urea solution and a single SCR catalytic converter. The control strategy consists of three parts: A primary feedforward controller, a surface coverage observer, and a feedback controller. A nitrogen oxide (NOx) gas sensor with non-negligible cross-sensitivity to ammonia (NH3) is used for a good feedback control performance. The control strategy is validated with ESC and ETC cycles: While the average NH3 slip is kept below 10 ppm, the emission of NOx is reduced by 82%.

The dynamics of the fuel-path subsystem of an SI engine, between fuel injection command signal and measured air-to-fuel ratio, is modeled approximately by a series connection of a first-order low-pass filter and a time delay element. The three parameters involved in this approximation, i.e., the time constant and the gain factor of the low-pass filter as well as the time delay, depend on the operating point of the engine. In order to design a gain-scheduled controller for the entire operating range of the engine, the parameters are identified for a number of operating points. For the automation of the parameter identification of all operating points desired, an on-line identification based on the recursive least-squares method is used. The algorithm for the decision of whether to increase or decrease the integer part of the current estimated time delay, which is a multiple of the sampling period, is based on an estimation of the fractional part of the time delay at each point.