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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.

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.

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.

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.

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.

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

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.

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.

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 gas components CO, CO2, HC, NOx and the AFR in the exhaust from a SI engine, both upstream and down-stream of a Pd/Rh catalytic converter, have been monitored using fast response analyzers. Regular sequential step changes in the upstream air/fuel ratio (AFR), between two pre-set levels, have been implemented with both long and short periods between the steps. For transitions from rich to lean conditions, and vice-versa, several distinct zones for the output emissions characteristics, corresponding to different states of the catalyst surface, have been identified. These results suggest that, under reducing conditions, hydrogen is stored on the catalyst surface whereas under oxidizing conditions oxygen is stored by two different processes. These chemical insights facilitate the development of realistic models for tailpipe emissions from engines which are perturbed from steady state running.

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.

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.