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In modern spark-ignited engines the accurate estimation of the amount of fuel to be injected is an important issue, in particular if a specific air-to-fuel ratio is required. The knowledge of the events occurring between the intake duct (injectors) and the exhaust duct (λ-sensor) is thus very important. Among all the systems that play a role, the best studied are the wall-wetting dynamics. Nowadays, the wall-wetting effects are compensated on the basis of simple linear models that are tuned with the help of a large number of measurements. These models are quite effective but they cannot be used universally.Their extrapolation for a non-measured operating point can lead to unsatisfactory results. Other problems arise at operating points where direct measurements are difficult, e.g., at cold start. Complex models already exist, but usually they require a lot of work in the parameterization phase.

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.

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.

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.

The dynamics of the fuel-path subsystem of an SI engine, between fuel injection command signal and measured air-to-fuel ratio, is modeled approximately by a series connection of a first-order low-pass filter and a time delay element. The three parameters involved in this approximation, i.e., the time constant and the gain factor of the low-pass filter as well as the time delay, depend on the operating point of the engine. In order to design a gain-scheduled controller for the entire operating range of the engine, the parameters are identified for a number of operating points. For the automation of the parameter identification of all operating points desired, an on-line identification based on the recursive least-squares method is used. The algorithm for the decision of whether to increase or decrease the integer part of the current estimated time delay, which is a multiple of the sampling period, is based on an estimation of the fractional part of the time delay at each point.

In order to achieve maximum fuel efficiency, the SI engine of the new CVT-equipped hybrid car developed at the Swiss Federal Institute of Technology (ETH) is operated in a high power regime (such as highway driving at speeds above 120 km/h) with its throttle in its 100-percent open position. Whenever an engine power which exceeds 11 kWs is demanded, there exists an equilibrium point between the engine torque and the torque induced by the drag. Any regulation of the vehicle speed has to be performed by altering the gear ratio of the CVT. If any acceleration is required, it is necessary to increase the engine speed. This requires that the vehicle has to be slowed down for a certain short period of time. If this characteristic behaviour of the car (which is typical for a non-minimum-phase system) is not accepted by a driver who demands and expects immediate acceleration, it might lead to critical situations.

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.

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.

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.