Simulation Research On Control Model For Three-Way Catalyst Converter

An increased concern about automotive pollution in the last 30 years has led to very strict emission standards. Apart from converter design improvements, new control strategies have to be developed to achieve a high performance under transient operating conditions. Current engine control systems for gasoline engines are controlling the engine air-fuel ratio at stoichiometry. Such a controller performs quite well under stationary conditions, but a further improvement is possible under transient conditions if the dynamics of the catalytic converter would be included in the control scheme.The subject of the research presented in this thesis was the development of new control system for automotive three-way catalytic converters in order to fulfill future ultra-low exhaust emission standards. More specifically, the goal was to develop a model-based control system that can reduce the emissions under highly dynamic operation of the process. The main contribution of the thesis is the development of a control-oriented model of the catalytic converter on the basis of information extracted from the first principle model of the converter.The four main parts of the research were: development of the engine model to calculate the input lambda to the TWC; development of the control-oriented model of the catalytic converter and estimation of the model parameters; analyzing the influences of typically changing variables(mass flow, inlet gas temperature, inlet signal amplitudes and oxygen storage capacity)on the dynamic responses; development the novel model-based controller.As studying control of the catalytic converter the dynamic behavior of the engine cannot not be disregarded. The engine produces the input for the catalytic converter. A Mean Value Engine Model (MVEM) that has become widely accepted for control purposes in the last decade, will be used in all simulation studies. On the basis of the throttle input and controller output (fuel injection) the model calculates the inputs to the catalytic converter, exhaustλvalue and mass flow.The development of the first principle model for a catalytic converter was based on chemical kinetic models of the reactions taking place inside the converter.By adding appropriate mass transfer and energy equations a complete converter model was obtained. The main dynamic effect stems from oxygen storage and release on ceria, which is placed in the washcoat of the reactor.In the thesis the first principle model cannot be used for control directly. However, the information about the process dynamics that stems from the first principle model will be proven as crucial for building a simpler control-oriented model. In order to use the model information in the controller the rigorous model had to be reduced.The oxygen storage and release capability of ceria is mostly responsible for the converter dynamic behavior during normal operation. A simplified control-oriented model has been developed to predict the level of oxygen storage coverage. It is a one state nonlinear model with the state being the oxygen storage coverage. The model is used as an inferential sensor in the applied controller for predicting the degree of the oxygen storage coverage that cannot be measured.The only available measurements are the lambda signals in front of and behind the catalytic converter. The model has to link the measured lambda signals upstream and downstream the converter with the non measurable degree of ceria coverage (ROC).After developing the control-oriented model of the TWC, the model parameters like k d,ζand f (ζ)are estimated and calculated. The information about the process dynamics has led to an algorithm for the extrapolation of the control-oriented model obtained in one operating point to other operating conditions. In this way the model adaption procedure, which can also lead to substantial exhaust emissions and cannot be performed during a standard system operation, can be reduced. This helps to broaden the application range of the control-oriented model without a need for further parameter optimization.The control-oriented model has been tested by simulations. Of course, experiments may not be neglected, as they serve to validate the model.The prediction of the model was compared to the measurements and it was found to be quite fit.By simulations based on Matlab and experiments performed on an engine test bench, various parameters of importance are analyzed. Also influences of typically changing variables, such as mass flow, inlet gas temperature, inlet signal amplitudes and oxygen storage capacity, on the dynamic responses have been studied and simulated.The overall model accuracy is satisfactory for the model to be used as a basis for the control system development.After the development and estimation of the model, a model-based controller is developed and its feasibility is demonstrated. A step further is taken to design a controller in an optimal fashion to minimize the exhaust emissions. The goal of the catalytic converter controller is to find the optimal oxygen storage coverage and to find optimal trajectories to reach this steady state (fast response with a low exhaust emission). Namely, it is possible to control the average level of oxygen stored on ceria throughout the reactor as desired.Because of an explicit model presence (inferential sensor), the obvious choice is to apply Model Predictive Control. Model Predictive Control is at the moment the most applied advanced control methodology in industry.The controller uses a model to predict the future process behavior and to find the optimal control sequence to achieve the control goals. The control sequence is found by solving the optimization problem online and applying the first control output to the process. The optimization process is repeated at each sampling interval, thus requiring a lot of computational power. The Model Predictive Controller is thus replaced by an analytic nonlinear function which can be calculated very fast at each sampling interval. With a proper selection of the nonlinear function (neural network), a sufficient level of complexity and enough training points, the Model Predictive Controller can be approximated to an arbitrary accuracy. A Gaussian radial basis function network is used as the nonlinear function. In this thesis,the MPC control algorithm of the oxygen storage is presented,and the solution method of the dynamic optimization is slso given theoretically. Then stability of a model based MPC is discussed and assessed.The study of the MPC controller provides the theoretical foundation for the online control of the TWC in the on-board computer.