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A model which calculates the hydrocarbon emissions from an SI engine is presented. The model was developed in order to obtain a better under-standing of experimental results from an engine operating on different fuels and lubricants. The model is based on the assumptions that fuel is stored in crevices and oil film during intake and compression followed by desorption during expansion and exhaust. The model also calculates the amount of desorbed material that undergoes in cylinder oxidation and exhaust port oxidation. The model succesfully predicts the trends followed by varying different engine parameters. The effect of changing the lubricant is of the same order of magnitude as found experimentally, but the effect of changing the fuel could not be predicted very well by the model. A possible explanation is, that the lubricant film thickness varies due to viscosity variations of the oil film, when the fuel is dissolved in the film.

The effects of lubricant composition on hydrocarbon emissions from a SI engine have been experimentally investigated. Results based on measurements of solubilities of different fuel components in different types of lubricants are presented. 2 lubricants and two hydrocarbons were chosen for testing in a single cylinder engine. Emissions and performance was measured for various fuel air ratios and ignition timings. The lubricant with the lowest solubility with respect to isooctane also showed the lowest hydrocarbon emissions. The influence of the lubricant was greatest at lean air fuel ratios. Xylene is much more soluble in the lubricants than isooctane and gave lower hydrocarbon emissions when the engine was operated at rich air fuel ratios. At leaner mixtures, isooctane gave lower emissions. The results indicate that the lubricant plays a contributing, but not dominating role in hydrocarbon emissions from gasoline engines.

The effects of mixture turbulence on combustion in a spark-ignition engine were investigated using a CFR engine. The apparent instantaneous turbulent flame speed during combustion was calculated from a combustion heat release model that used measured cylinder pressures and assumed spherical flame propagation. This flame speed was correlated with turbulent intensities measured in the motored engine. The ratio of fully developed turbulent flame speed to laminar flame speed was found to be a linear function of motored turbulent intensity.

Mean Value Engine Models (MVEMs) are simplified, dynamic engine models which are physically based. Such models are useful for control studies, for engine control system analysis and for model based engine control systems. Very few published MVEMs have included the effects of Exhaust Gas Recirculation (EGR). The purpose of this paper is to present a modified MVEM which includes EGR in a physical way. It has been tested using newly developed, very fast manifold pressure, manifold temperature, port and EGR mass flow sensors. Reasonable agreement has been obtained on an experiemental engine, mounted on a dynamometer.

While a large number of dynamic simulation models have been presented for various four-cycle spark ignition engine subsystems in the literature, very few have been presented for the entire engine which can claim an acceptable level of accuracy for engineering purposes. This paper presents a nonlinear three state (three differential equation) dynamic model of an SI engine which has the same steady state accuracy as a typical dynamometer measurement of the engine over its entire speed/load operating range (±2.0%). The model's accuracy for fast transients is of the same order in the same operating region. Because the model is so mathematically compact, it has few adjustable parameters and is thus simple to fit to a given engine either on the basis of measurements or given the steady state results of a larger cycle simulation package. The model can easily be run on a Personal Computer (PC) using a ordinary differential equation (ODE) integrating routine or package.

An important paradigm for the modelling of naturally aspirated (NA) spark ignition (SI) engines for control purposes is the Mean Value Engine Model (MVEM). Such models have a time resolution which is just sufficient to capture the main details of the dynamic performance of NA SI engines but not the cycle-by-cycle behavior. In principle such models are also physically based, are very compact in a mathematical sense but nevertheless can have reasonable prediction accuracy. Presently no MVEMs have been constructed for intercooled turbocharged SI engines because their complexity confounds the simple physical understanding and description of such engines. This paper presents a newly constructed MVEM for a turbocharged SI engine which contains the details of the compressor and turbine characteristics in a compact way. The model has been tested against the responses of an experimental engine and has reasonable accuracy for realistic operating scenarios.

Mean Value Engine Models (MVEMs) are dynamic models which describe dynamic engine variable (or state) responses as mean rather than instantaneous values on time scales slightly longer than an engine event. Such engine variables are the independent variables in nonlinear differential (or state) equations which can be quite compact but nevertheless quite accurate. One of the most important of the differential equations for a spark ignition (SI) engine is the intake manifold filling (often manifold pressure) state equation. This equation is commonly used to estimate the air mass flow to an SI engine during fast throttle angle transients to insure proper engine fueling. The purpose of this paper is to derive a modified manifold pressure state equation which is simpler and more physical than those currently found in the literature. This new formulation makes it easier to calibrate a MVEM for different engines and provides new insights into dynamic SI engine operation.

Conventional electronic engine control systems suffer from poor transient air/fuel ratio control accuracy. This is true of speed-throttle, speed-density, and mass air flow (MAF) control systems with either single point (or central) or port fuel injection. The reason for this is that they fail to 1. compensate for the nonlinear dynamics of the fuel film in the intake manifold or in the vicinity of the intake valves. 2. estimate correctly the air mass flow at the location of the injector(s). This paper presents a nonlinear fuel film compensation network and a nonlinear closed loop observer. The nonlinear fuel film compensator gives improved global cancellation of the fuel film dynamics, while the closed loop observer has improved robustness with respect to modelling error and measurement noise. The closed loop observer is based on a modified constant gain extended Kalman filter.

Many existing classical electronic control systems (speed-throttle, speed-density, MAF (mass air flow)) are based on quasistatic engine models and static measured engine maps. They are thus time consuming to adapt to new engine types, are sensitive to dynamic sensor errors and in general have undesirable dynamic characteristics. One of the main reasons for the characteristics of these strategies has been the lack of a precise, systems oriented, equation based, dynamic engine model. Recently a compact dynamic mean value engine model (MVEM) has been presented by the authors which displays good global accuracy. A mean value model is one which predicts the mean value of the gross internal and external engine variables. This paper shows how the engine model can be applied to the systematic design and analysis of classical electronic engine control systems. One of the main aims of the paper is to eliminate the use of cut and try methods in designing dynamic engine controls.

The electronic injection and control of gasoline engines has caused a revolution of the conventional four-stroke and two-stroke SI engines. Now, this technique is also playing a more and more important role in the Diesel engine area. In this paper, we will focus on the electronic injection and control of Diesel engines to discuss injection systems, dynamic models and advanced controls. First, the typical R&D results on first generation and second generation of the electronic Diesel injection systems will be reported. Different design approaches will be described. Then, dynamic submodels of the Diesel fuel injection systems and dynamic models for the whole Diesel engine will be summarized. The main dynamic processes and modelling philosophy will be analyzed. Finally, the advanced Diesel governing techniques and Diesel management systems will be reviewed.