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The goal of the Clean Engine Vehicle project (CEV) was the conversion of a gasoline engine to dedicated natural gas operation in order to achieve a significant reduction in CO2 emissions. The targeted reduction was 30% compared with a gasoline vehicle with similar performance. Along with the reduction in emissions, the second major requirement of the project, however, was compliance of the results with Euro-4 and SULEV emission limits. The project entailed modifications to the engine and the pre-existing model-based engine control system, the introduction of an enhanced catalytic converter and downsizing and turbocharging of the engine. As required by the initiators of the project, all components used were commonly available, some of them just being optimized or modified for natural gas operation.

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%.

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.

Very stringent limits for exhaust gas emissions as well as high claims for onboard diagnosis (OBD) of the three-way catalytic converter (TWC) demand a sophisticated control and observer strategy which can both further reduce the exhaust gas emissions and also estimate the relevant parameters allowing to monitor the decreasing performance of the TWC over its lifetime. The most crucial parameter and state, respectively, are widely believed to be the oxygen storage capacity (OSC) and the relative oxygen level (ROL) of the TWC. The TWC's performance decreases with a diminishing OSC. Therefore, an accurate estimation of the OSC can be used for OBD. Keeping the ROL at an optimal level by means of control enhances the TWC's performance significantly, even during transients of the air/fuel ratio imposed by the driver. In order to monitor both the ROL and the OSC, an observer has been derived from a complex TWC model.

The selection and configuration of sensors can strongly influence the closed-loop dynamics of a system. Therefore a methodology for finding the best sensor placement is a valuable tool. This paper deals with this problem by formulating an optimization problem and applies the new method on an SI engine. The best sensor configuration is one that minimizes the overall system costs, yet still meets the system constraints. Before solving the optimization problem, the system is modeled, different sensor configurations are defined, the appropriate controller and the feedback term are developed, and the locations and size of the various errors present in the model are determined. Then, the objective function and the system constraints are defined and the optimization problem is solved considering the worst-case combination of modeling errors, which is computed using genetic algorithms. The objective function is defined as the sum of the sensor costs and of a penalty term.