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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.

Knock control strategies attempt to optimize the tradeoff between improving torque output and engine efficiency while also regulating knock intensity and protecting the engine from damage. This tradeoff must be made in a stochastic framework since knocking combustion behaves as a random process. This paper therefore examines the marginal and joint statistical properties of both knock intensity, and IMEP under knock limited conditions. Autocorrelation and Pearson chi-squared tests are also used to validate the cyclic independence of the data, or to identify prior cycle effects. The results and joint distribution give insight into the tri-variate relationship between knock intensity, IMEP, and spark advance, providing a foundation for improved knock/IMEP simulation and optimized controller design.

Simulation tools are critical to control system development and design. Simulation of knock control systems, however, is complicated both by the stochastic nature of knock, and by the wide range of signal bandwidth and dimensionality within the feedback loop. The aim of this paper is therefore to provide a review, and in some cases an extension, of knock simulation methods from a control-oriented perspective, focusing particularly on the statistical properties of knock intensity and knock events. Deterministic notions of closed-loop performance can be misleading in this context, and recent work using Markov- and Monte-Carlo-based methods is briefly summarized as a means to obtain a more rigorous and complete description of closed-loop behavior. The methods are illustrated using a classical knock controller design.

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.

This paper collates and summarizes recent advances in knock analysis, simulation and control. The statistical properties of knock intensity and knock events are reviewed showing in particular that knock intensity behaves as an independent random process, and that knock events conform to a binomial distribution. These properties have a significant impact on knock control and simulation. Traditional and recently proposed cumulative-summation-based and Likelihood-based knock control strategies are reviewed and illustrated in this context. Efficient tools for simulating both specific instances of the closed loop time response, and the evolution of the distribution of these responses based on a Markov-like approach, are also briefly reviewed. Finally, it is shown how an optimization of the knock threshold and an associated retuning of the controller parameters can result in significantly improved closed loop performance without any other modification of the control algorithm.

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.

Production NO sensors have a strong cross-sensitivity to ammonia which limits their use for closed-loop SCR control and diagnostics since increases in sensor output can be caused by either gas component. Recently, Ammonia/NO Ratio (ANR) perturbation methods have been proposed for determining the dominant component in the post-SCR exhaust as part of the overall SCR control strategy, but these methods or the issue of sensor cross-sensitivity have not been critically evaluated or studied in their own right. In this paper the dynamic sensor direct- and cross-sensitivities are estimated from experimental FTIR data (after compensating for the dynamics of the gas sampling system) and compared to nominal values provided by the manufacturer. The ANR perturbation method and the use of different input excitations are then discussed within an analytical framework, and applied to experimental data from a large diesel engine.

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.

In this paper a new knock control algorithm is developed based on a stochastic interpretation of the knock signal and on a control objective specified as a certain percentage of knocking cycles. Unlike previous ‘stochastic’ knock controllers, the new algorithm does not average or low pass filter the knock intensity signal and the transient response of the controller is consequently much faster. The performance of the new controller is compared in detail with the response of a traditional deterministic controller using a simple but effective knock simulation tool. The results show that the new controller is able to operate at a more advanced mean spark angle and that there is much less cyclic variance about this mean. The transient response to excess knocking events is as fast, or faster, than the conventional controller, though the rate of recovery from overly retarded conditions is slower.

Increasingly, advanced engine management systems incorporate high speed Digital Signal Processing (DSP) units for analyzing high-bandwidth, in-cycle signals such as those obtained from cylinder pressure, or knock sensors. In order to develop, calibrate and test the robustness of these algorithms, it is helpful to work in a simulation environment capable of simulating high-speed in-cycle data and its interaction with the engine management and DSP control strategies. Typically, however, in-cycle simulation is both deterministic and highly computationally intensive so a realistic, cyclically-varying simulation of in-cycle data is hard to generate. In this paper an alternative approach is used, based on initially recording files of high-speed, in-cycle data at different engine conditions. This database is then used to simulate the engine response as the specified engine condition varies, by playing back data from the appropriate files at each time instant.

Look-up tables are widely used in engine management strategies to characterize nonlinear relationships between inputs such as speed and load, and the desired output. However, the calibration of such tables can be time consuming, and is prone to errors due to fluctuating engine measurements, or to small mismatches between the actual test operating condition and the desired operating point in the lookup table grid. In this paper a recursive least-squares identification technique is used to automate the calibration of the table values as the engine is operated over the desired range. The memory and computational requirements of the technique have been optimized so that it can run in real time on a typical engine management system, and the same technique may be used to adapt the table during normal operation if a feedback value is available. The adaptation rate can be adjusted depending on the noise in the available signals.

In this work, a discrete,time-based, delay-compensated, adaptive PID control algorithm for air fuel ratio control in an SI engine is presented. The controller operates using feedback from a wide-ranging Universal Exhaust Gas Oxygen (UEGO) sensor situated in the exhaust manifold. Time delay compensation is used to address the difficulties traditionally associated with the relatively long and time-varying time delay in the gas transport process and UEGO sensor response. The delay compensation is performed by computing a correction to the current control move based on the current delay and the corresponding values of the past control moves. The current delay is determined from the measured engine speed and load using a two dimensional map. In order to achieve good servo operation during target changes without compromising regulator performance a two degree of freedom controller design has been developed by adding a pre-filter to the air fuel ratio target.

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.

Chemical balance analysis between two points such as engine intake and exhaust is widely used to compute air fuel ratio (AFR) from exhaust gas component measurements. The same set of equations can also be used in many other ways depending on the set of known and unknown variables. Balance analysis can be used across a catalyst, for example, or if the AFR is known it can be used to estimate the unknown concentration of a given gas component. Current chemical balance analysis programs do not have this flexibility. Indeed it is often even necessary to modify the underlying code depending upon whether the measured gas concentrations are sampled dry or wet. This paper describes a highly flexible and user-friendly program developed in the Matlab environment for performing chemical balance analysis between gases entering and leaving a reaction system.

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 relationship between the concentration of various gas components (CO,NO,HC) at the output of a three-way catalytic converter and the input and output air-fuel ratios (AFRs) is examined. A simple linear-in-the-parameters model is developed and it is assumed that the model parameters in the lean and rich regions are different. The model is fitted to some experimental step response data obtained from fast gas response analysers and UEGO sensors, using a recursive linear least-squares estimation method. A reasonably good fit to the data is obtained, particularly for NO and CO. Results from step tests for different AFR ranges are combined to obtain an overall picture of the dependency of the gas components on measured AFR values. The proposed model provides the possibility of predicting the dynamic performance of catalytic converters from a knowledge of the input and output AFR values.

The transient response of a catalytic converter to fluctuations in exhaust gas composition has a significant impact on tailpipe emissions. Advanced emission control strategies therefore need to incorporate a model for such behavior, which must also be sufficiently simple for practical implementation in-vehicle. To this end, a variety of semi-empirical models have been developed, including most recently a number of oxygen “storage-dominated” models. In this paper a new storage-dominated model is developed, which includes for the first time the effects of space velocity. The parameters of model may be estimated using the invariant embedding method.