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Production PM sensors are now available and are likely to be key components of PM aftertreatment systems designed to meet 2013 OBD requirements. In this paper a highly simplified analysis is used to give insight into the sensor response of resistive-based devices, and to motivate possible diagnostic strategies. The method has been applied to successive sets of FTP data recorded with DPF's of different failure levels, and despite the very approximate nature of the underlying model, the method appears to discriminate reliably between them.

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.

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

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.