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Though very important for the system performance, the dynamic behavior of the catalytic converter has mainly been neglected in the design of exhaust emission control systems. Since the major dynamic effects stem from the oxygen storage capabilities of the catalytic converter, a novel model-based control scheme, with the explicit control of the converter's oxygen storage level is proposed. The controlled variable cannot be measured, so it has to be predicted by an on-line running model (inferential sensor). The model accuracy and adaptability are therefore crucial. A simple algorithm for the model parameter identification is developed. All tests are performed on a previously developed first principle model of the catalytic converter so that the controller effectiveness and performance can clearly be observed.

The performance of a three-way catalytic converter under transient operation can be improved by controlling the level of oxygen stored on ceria at some optimal level. A model-based controller, with the model estimating the level of ceria coverage by oxygen, can achieve this goal. A simple, dynamic model is based on step responses of the converter and is used to train the controller off-line. The controller is a neuro-fuzzy approximation of a model predictive controller. Thus, it retains a high performance while being less computationally involving. The system performance has been experimentally tested by a specially designed, highly transient test cycle.