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In modern engine control applications, there is a distinct trend towards model-based control schemes. There are various reasons for this trend: Physical models allow deeper insights compared to heuristic functions, controllers can be designed faster and more accurately, and the possibility of obtaining an automated application scheme for the final engine to be controlled is a significant advantage. Another reason is that if physical effects can be separated, higher order models can be applied for different subsystems. This is in contrast to heuristic functions where the determination of the various maps poses large problems and is thus only feasible for low order models. One of the most important parts of an engine management system is the air-to-fuel control. The catalytic converter requires the mean air-to-fuel ratio to be very accurate in order to reach its optimal conversion rate. Disturbances from the active carbon filter and other additional devices have to be compensated.

Coincident 3-d velocity measurements in the flat combustion chamber of a motored single cylinder engine have been performed using Laser-Doppler-Velocimetry. The 3-d LDV System consisted of three beampairs (514nm, 488nm and 476.5nm) and two fiberoptic probes operated in 90° cross-scatter mode obtaining high spatial and temporal resolution as well as high signal quality. Burst Spectrum Analyzers have been thereby used for signal processing. The time histories of the three velocity components have been acquired for moderate engine speeds (600, 1000 and 1500RPM). The swirling motion in the cylinder was also varied by choosing different fixed positions of a shrouded intake valve relative to the intake port. Several measuring locations in the combustion chamber have been studied in order to investigate homogeneity. Mean velocities and fluctuation intensities of the turbulent field were evaluated using ensemble averaging.

Modern engine management systems increasingly rely on on-line identification schemes. These are used either for self-tuning regulators or the rapid parametrization of controllers. In this paper the on-line parameter identification of the wall-wetting dynamics is studied in detail. The identification is performed by exciting the fuel path dynamics of the engine at a constant operating point. The amount of fuel injected serves as input and the air-to-fuel ratio, which is measured with a linear oxygen sensor, as output. In order to gain precise information about the amount of fuel in the cylinder, a new measurement concept is used. For one, the placement of the lambda sensor close to the exhaust valve minimizes the effects of gas mixing on the measurements. Additionally, by an appropriate collection of the data, the sensor dynamics are bypassed. This is also illustrated by a measurement with a very fast NOx sensor.

Today's engine management systems for SI engines consist of static and dynamic control algorithms. The static functions of the engine management guarantee the correct stationary operation of the engine in all the possible operating points. The static functions are contained mainly in two lookup tables, one for the spark advance and one for the metered depending on engine speed and load. Usually these lookup tables are determined with experiments on the engine test bench. In this paper, a model-based method for the evaluation of the fuel-optimal maps for spark advance and metered fuel is described. The method can be divided into several steps: 1. Measurement and identification of all the engine parameters in a reference point (including the pressure in one cylinder) Calculation of the burn-through function (progress of the combustion) Iterative calculation of the amount of residual exhaust gas Approximation of the definitive burn-through function with the Vibe equation 2.

This paper introduces a model-based adaptive controller designed to compensate mixture ratio dynamics in an SI engine. In the basic model the combined dynamics of wall-wetting and oxygen sensor have to be considered because the only information about process dynamics originates from measuring exhaust λ. The controller design is based on the principles of indirect Model Reference Adaptive Control (MRAC). The indirect approach connotes that explicit identification of the system parameters is required for the determination of the controller parameters. Due to nonlinearities and delays inherent in the process dynamics, an adaptive extended Kalman filter is used for identification purposes. The Kalman filter method has already been described in detail within an earlier paper [1]. It proves to be ideally suited to deal with nonlinear identification problems. The estimated parameters are further used to tune an adaptive observer for wall-wetting dynamics.

Modern model-based control schemes and their application on different engines need mathematical models for the various dynamic subsystems of interest. Here, the fuel path of an SI engine is investigated. When the engine speed and the throttle angle are kept constant, the fuel path is excited only by the fuel injected. Taking the NO concentration of the exhaust gas as a measure for the air/fuel ratio, models are derived for the wall-wetting dynamics, the gas mixture, as well as for the air/fuel ratio sensor. When only the spark advance is excited, the gas flow dynamics can be studied. A very fast NO measurement device is used as reference. Its time constant is below the segment time of one single cylinder (180° crank angle for a 4-cylinder engine), therefore its dynamics are much faster than the time constants of the systems investigated. A model structure considering the muliplexing effects of the discrete operation of an engine is given for the fuel path of a BMW 1.8 liter engine.

In this paper the fuel path of a sequentially injected gasoline engine is discussed. Since a fraction of the injected fuel suffers a delay due to the wall-wetting phenomenon, in transient phases a significant deviation of the air-to-fuel ratio from its setpoint can arise. The amount of fuel on the manifold wall and its rate of evaporation cannot be measured directly. Therefore, the effects of the wall-wetting on exhaust lambda and engine torque have to be considered for the identification of the dynamics. The dynamics of the exhaust-gas-oxygen (EGO) sensor is not negligible for the interpretation of the lambda measurement. Since both the dynamics and the statics of a ZrO2 Sensor are very nonlinear, a normal EGO-sensor is not suitable for these investigations. On the other hand, the engine torque is a good measure for the cylinder lambda when all other effects which lead to torque changes can be eliminated.

The burned gas remaining in the cylinder after the exhaust stroke of an SI engine, i.e. the residual gas fraction, has a significant influence on both the torque production and the composition of the exhaust gas. This work investigates the behavior of the residual gas fraction over the entire operating range of the engine. A combined discrete-continuous linear model is identified, which describes the dynamic effects of the gas composition from when the gases enter the cylinder up to the measurement with a specific sensor. In this investigation, that sensor is a fast NO measurement device. The system is modelled by three elements in series: the in-cylinder mixing, the transport delay, and the exhaust mixing. The resulting model contains three elements in series connection: the in cylinder mixing, the transport delay, and the exhaust gas mixing. The model is able to calculate the fuel mass entering the cylinder during a fuel injection transient.

A KIVA-based CFD tool has been utilized to simulate the effect of a Common-Rail injection system applied to a large, uniflow-scavenged, two-stroke diesel engine. In particular, predictions for variations of injection pressure and injection duration have been validated with experimental data. The computational models have been evaluated according to their predictive capabilities of the combustion behavior reflected by the pressure and heat release rate history and the effects on nitric oxide formation and wall temperature trends. In general, the predicted trends are in good agreement with the experimental observations, thus demonstrating the potential of CFD as a design tool for the development of large diesel engines equipped with Common-Rail injection. Existing deficiencies are identified and can be explained in terms of model limitations, specifically with respect to the description of turbulence and combustion chemistry.

In this paper we introduce an improved model for the fuel supply dynamics in an SI engine. First, we briefly investigate all the thermodynamic phenomena which are assumed to have a significant impact on fuel flow into the cylinder (i.e., fuel atomization, droplet decay, wall-wetting, film evaporation, and mixture flow back). This theoretical analysis results in a basic set of dynamic equations. Unfortunately, these equations are not convenient to use for control purposes. Therefore, we proceed to a simplified formulation. Several unknown parameters remain, describing phenomena which are difficult to quantify, such as heat and material transfer characteristics. These parameters are subject to operating conditions and are not discussed further. In order to validate the model dynamics, we refer to frequency and step response measurements performed on a 4-cylinder, 1.8 liter BMW engine with sequential fuel injection.

The measurement of the various pollutant species (HC, CO, NO, etc.) has become one of the main issues in internal combustion engine research. This interest concerns not only their quantitative measurement but also the study of the mechanism of their formation. In fact, pollutant species concentration can be used as an indicator for the combustion characteristics. For instance, it enables the determination of a lean or a rich combustion, the percentage of EGR, etc. The purpose of this research is to investigate the behavior of pollutant gas particles in the first part of an engine exhaust system through a detailed study of the unsteady flow in the exhaust pipe. The results are intended to designate the appropriate sensor positions which ensure accurate measurement results. This investigation wants to track an inert component in the exhaust system, namely the NO gas.