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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.

Vehicle certification data are used to study the effectiveness of the major powertrain technologies used by car manufacturers to reduce fuel consumption. Methods for differentiating vehicles effectively were developed by leveraging theoretical models of engine and vehicle fuel consumption. One approach normalizes by displacement per unit distance, which puts both fuel used and vehicle work in mean effective pressure units, and is useful when comparing engine technologies. The other normalizes by engine rated power, a customer-relevant output metric. The normalized work/power is proportional to weight/power, the most fundamental performance metric. Certification data for 2016 and 2017 U.S. vehicles with different powertrain technologies are compared to baseline vehicles with port fuel injection (PFI) naturally aspirated engines and six-speed automatic transmissions.

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.

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.

In 1999, the first generation NOx sensor from NGK Spark Plug, Co., Ltd. was commercialized for use in gasoline LNT NOx after-treatment systems [ 1 ]. Since then, as emissions regulations and OBD requirements have become more stringent, the demand for a high-accuracy NOx sensor with fast light-off has increased, particularly for diesel after-treatment systems. To meet such market demands, NGK Spark Plug, Co., Ltd. has developed, in collaboration with Ford Motor Company, a second generation NOx sensor.

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.

A wavelet-based technique for reducing the impulsive character of sound recordings is presented. The amount of impulsive content removed may be adjusted by varying a statistical threshold. The technique is validated for a diesel idle sound-quality application. The wavelet-based modification produces a substantial decrease in impulsive character as verified by an objective sound-quality metric for engine “ticking”. Informal subjective assessment of the modified results found them to be realistic and free from artifacts. The procedure is expected to be useful for sound-quality simulation and target-setting for diesel powertrain noise and other automotive sounds containing both impulsive and non-impulsive content.

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.

Recent applied mathematics research on the properties of the invertible shift-invariant discrete wavelet transform has produced new ways to visualize, separate, and synthesize impulsive sounds, such as thuds, slaps, taps, knocks, and rattles. These new methods can be used to examine the joint time-frequency characteristics of a sound, to select individual components based on their time-frequency localization, to quantify the components, and to synthesize new sounds from the selected components. The new tools will be presented in a non-mathematical way illustrated by two real-life sound quality problems, extracting the impulsive components of a windshield wiper sound, and analyzing a door closing-induced rattle.

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

Scalograms based on shift-invariant orthonormal wavelet transforms can be used to analyze impulsive and transient sounds in the presence of more stationary sound backgrounds, such as wind noise or drivetrain noise. The visual threshold of detection for impulsive features on the scalogram (signal energy content vs. time and frequency,) is shown to be similar to the audible threshold of detection of the human auditory system for the corresponding impulsive sounds. Two examples of impulsive sounds in a realistic automotive sound background are presented: automotive interior rattle in a vehicle passenger compartment, and spark knock recorded in an engine compartment.

Spark knock pressure oscillations can be detected by a cylinder pressure transducer or by an accelerometer mounted on the engine block. Accelerometer-based detection is lower cost but is affected by extraneous mechanical vibrations and the frequency response of the engine block and accelerometer. The knock oscillation frequency changes during the expansion stroke because the chamber geometry is changing due to the piston motion and the burned gases are cooling. Spectrogram analysis shows the time-dependent frequency content of the pressure and acceleration signals, revealing characteristic signatures of knock and mechanical vibrations. Illustrative spectrograms are presented which yield physical insight into accelerometer-based knock detection.

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.

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.

The frequency of the pressure oscillations caused by spark knock depends on the temperature-dependent speed of sound in the combustion gases. Engine dynamometer tests showed a 6.5% (390 Hz) reduction in the knock fundamental frequency as the air/fuel ratio was swept from 13:1 to 20:1. Engine cycle simulation model predictions of maximum burned gas temperatures correlate well with the data. A robust knock detection system must be insensitive to the range of burned gas temperature (frequency of pressure oscillations) that will be encountered with a particular engine control system operating under the expected range of fuels and environmental conditions.