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Pre- and post-catalyst Exhaust Gas Oxygen (EGO) sensors are traditionally used to monitor oxygen storage capacity for On Board Diagnostic (OBD) purposes. In this paper the same sensors are used instead to monitor catalyst-promoted hydrogen generation, exploiting the sensor's otherwise undesirable sensitivity to the hydrogen content in the exhaust. This offers a new approach to catalyst health diagnosis since hydrogen generation and HC conversion efficiency both depend on the degree of activation (or deactivation) of the catalyst surface, and are therefore strongly correlated to each other. The approach has the advantage that it is more directly related to catalyst deterioration or malfunction as defined (in terms of HC emissions levels) under current OBD legislation.

In this paper, we review previous approaches to oxygen-related OBD strategies and then discuss the use of a new model-based approach together with a distribution-free statistical testing strategy for fault detection. The method is illustrated using experimental pre- and post-catalyst data for which a simplified catalyst-plus-sensor model has been developed. By monitoring the distribution of prediction errors between the ‘healthy’ model output, and the actual catalyst response even small levels of oxygen storage degradation can be detected with a high degree of confidence.

Reversible catalyst deactivation dynamics can have a significant effect on both conversion efficiency and post-catalyst EGO sensor distortion, yet are often ignored in conventional oxygen storage modeling for on-board catalyst control and OBD systems. The aim of the present paper is to include these dynamics in an extended model which exploits the otherwise unfortunate effects of sensor distortion to provide a measure of catalyst deactivation, and hence obtain more accurate predictions of conversion efficiency. Furthermore, by fitting the combined oxygen storage and reversible deactivation model to the data, unbiased estimates of the true post-catalyst AFR can be obtained which are then available for improved catalyst control and diagnostic strategies.

Transient measurements of pre- and post-catalyst exhaust gas components and AFR are used to investigate the relationship between post-catalyst AFR and tailpipe emissions. This relationship is critical to the ability of on-board oxygen storage dominated models to predict emissions levels. The results suggest that under rich, or rich-biased conditions, dynamic deactivation processes significantly reduce catalyst efficiency, and that modeling oxygen storage effects alone may result in over-prediction of tailpipe pollutants. Catalyst deactivation is also shown to be correlated to hydrogen-induced distortion in the Exhaust Gas Oxygen (EGO) sensors used for measuring AFR. The dynamics of reversible catalyst deactivation are therefore important both for its direct effect on dynamic conversion efficiency, and for its indirect effect on dual EGO sensor dependent catalyst control and OBD strategies