Modeling Combined Catalyst Oxygen Storage and Reversible Deactivation Dynamics for Improved Emissions Prediction

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.