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In this work, a model predictive functional control approach for automotive three-way catalyst oxygen storage state control is demonstrated on a Ford 2.0 liter I4 Duratec SI engine. The control system uses a UEGO sensor for the pre-catalyst air fuel ratio (AFR) measurement and a switching-type HEGO sensor for the post-catalyst measurement. The model predictive controller is the primary control loop within a multi-rate cascade control configuration that adapts the parameters of a post-catalyst HEGO relay controller in an optimal manner using a predictive functional control approach. This relay controller adjusts the target of a delay-compensated feedback controller for the pre-catalyst AFR in order to maintain the post-catalyst HEGO sensor signal within a specified range of the desired target voltage.

An integrated model-based three-way catalyst control and diagnostic monitoring system is described which has the potential for improved health discrimination while also significantly reducing the calibration burden. The catalyst is modeled as a simple limited integrator with an adaptive integral gain. The adaptive gain, used in the control system, is also used as a diagnostic metric since (among other variables) it reflects the catalyst oxygen storage capacity and hence the health of the system. The method has been applied to a 4.6 liter ULEV II gasoline engine, and tested over an EPA Federal Test Procedure drive cycle with a number of differently aged catalysts. Preliminary results are encouraging, and show that the method is able to discriminate between the catalysts, even those with similar age near the OBD threshold.

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.

The dynamic behavior of three-way catalysts has significant impact on tailpipe emissions levels, but remains one of the last unknowns in the overall vehicle emissions model. A simple empirical model (appropriate for use in real-time engine control and on-board diagnostic strategies) has therefore been identified using fast response input / output measurements of the actual process. The model is able to characterize the (significant) dynamic behavior which has recently been observed under rich conditions, as well as the more well known dynamics which arise from oxygen storage. The results therefore compare well with measured responses over a wide range of air / fuel ratio conditions.