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Technical Paper

Knock Thresholds and Stochastic Performance Predictions: An Experimental Validation Study

Knock control systems are fundamentally stochastic, regulating some aspect of the distribution from which observed knock intensities are drawn. Typically a simple threshold is applied, and the controller regulates the resultant knock event rate. Recent work suggests that the choice of threshold can have a significant impact on closed loop performance, but to date such studies have been performed only in simulation. Rigorous assessment of closed loop performance is also a challenging topic in its own right because response trajectories depend on the random arrival of knock events. The results therefore vary from one experiment to the next, even under identical operating conditions. To address this issue, stochastic simulation methods have been developed which aim to predict the expected statistics of the closed loop response, but again these have not been validated experimentally.
Journal Article

Threshold Optimization and Performance Evaluation of a Classical Knock Controller

A new knock threshold optimization method is presented based on minimization of the total misclassification error of knocking / non-knocking engine operating conditions. The procedure can be used in conjunction with any knock-event-based controller, but is illustrated on a classical knock control strategy. Initial simulations suggest that the method delivers significant performance improvements with no changes other than a retuning of the controller. However, it is not possible rigorously to evaluate controller performance based on any individual experiment or simulation time history due to the random nature of the knock process. A recently developed stochastic simulation technique is therefore used to compute and compare the statistical properties of the closed loop steady state and transient response characteristics.
Technical Paper

A Novel Approach to Catalyst OBD

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.
Technical Paper

Model-based OBD for Three-Way Catalyst Systems

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.
Technical Paper

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.
Technical Paper

The Importance Of Reversible Deactivation Dynamics For On-Board Catalyst Control And OBD Systems

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
Technical Paper

Identification of Stochastic Models for Cyclic Variations from Measured Pressure Data

A stochastic model for the entire pressure-time history of cycle-by-cycle cylinder pressure variations is obtained by fitting simple parametric models of cylinder pressure development to 506 cycles of continuous experimental data taken at four operating conditions. The cyclic variation is therefore encapsulated in a sequence of cyclically varying model parameters whose statistical properties then complete the stochastic description. Different model forms, (including computationally efficient linearised models), are compared for their degree of fit, and for the ease with which the statistics of the identified parameters can be defined. This approach, which typically accounts for 80-90% of the rms cyclic pressure variation, provides a more complete quantification of the phenomena than previously available, and a basis for simulating statistically identical pressure traces.
Technical Paper

Cylinder Pressure Variations as a Stochastic Process

A framework for a new approach to modelling the cyclic pressure variations which occur in spark ignition engines, based on stochastic process theory, is discussed. The aim is to of establish a stochastic process model for the entire pressure/time history of cycle-by-cycle variation throughout the combustion period. Three types of model are discussed. In the first two it is possible to incorporate correlation across cycles, arising from “prior cycle effects”. In the third, simpler version, the individual cycles are treated as statistically independent. Through a statistical analysis of some pressure data acquired under typical engine conditions the basic characteristics of the stochastic process representations are illustrated.