Modeling and model-based control of a three-way catalytic converter

Abstract

An increased concern about automotive pollution in the last 30 years has led to very stringent emission standards. The subject of the research presented in this thesis was the development of new control strategies for automotive threeway catalytic converters in order to fulfill future ultra-low exhaust emission standards. More specifically, the goal was to develop a model-based control strategy that can reduce the emissions under highly dynamic operation of the process, i.e. city driving. Also a possible improvement of the catalyst light-off (reduction of the temperature needed for the converter to become operational) has been studied. The main contribution of the thesis is the development of a model-based controller on the basis of information extracted from the first principle modeling of the converter. The three main parts of the research were: development of the rigorous, first principle model of the catalytic converter; development of the controloriented model of the catalytic converter and connecting it with the engine model; development and testing of the novel model-based controller by both simulations and experiments. 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 model predictions have been compared to experimentally measured data. An improvement of the converter’s light-off by means of oscillating inlet feed (oscillations of the inlet lambda value) and secondary air injection (additional air is injected behind the engine exhaust valves) was studied. It is shown that the light-off improvement is possible if right operating conditions are kept. After the converter light-off the main dynamic effect stems from oxygen storage and release on ceria, which is placed in the washcoat of the reactor. By properly controlling this process an extra buffer can be obtained to allow temporary excursions of the engine lambda (air/fuel ratio) value, which are inevitable during a dynamic operating regime. 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). In order to use the model information in the controller the rigorous model had to be reduced. A simplified control-oriented model has been developed to predict the level of oxygen storage coverage on-line. It is a one state nonlinear model with the state being the oxygen storage coverage. It was found experimentally that in some cases a two state model which makes a distinction between oxygen stored on the ceria surface and in the bulk can lead to a better prediction. The model can automatically be tuned on the basis of the catalytic converter step responses during which the inlet and outlet lambda values are measured. The information about the nonlinear process dynamics obtained from the first principle modeling 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 tuning procedure, which can also lead to substantial exhaust emissions and cannot be performed during a standard system operation, can be reduced. The prediction of the model was compared to the rigorous model prediction and it was found to be quite accurate. The model is used as an inferential sensor in the applied controller in the engine control unit for predicting the degree of the oxygen storage coverage that cannot be measured. The actual controller is an analytic approximation of the developed Model Predictive Controller. The developed Model Predictive Controller is capable of using the process information available from the model to find an optimal control behavior set by the control objectives. This controller requires solving an optimization problem at every sampling instant, which cannot be achieved in the engine control unit due to limited processing capabilities. Therefore, the optimization problems for the expected operating conditions are solved off-line and used to train a simple neural network that emulates the Model Predictive Controller. The controller has been tested by simulations on the first principle model, and by experiments performed on an engine test bench. The performed tests simulated highly dynamic system operation, when the majority of emissions occur, such as city driving. Due to a proper use of the model information, the novel controller leads to the emission reduction under above given conditions.