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In general most engine models for control applications have been constructed using regressions fitting and measured engine data. Such techniques have also been used to model the dynamic performance of engines. Unfortunately regression equation models are very complex and do not show directly the physical reality from which they emerge. This has for example made it impossible to write down explicitly the dymanic equations for, for example, the air exchange process in an SI engine in any form other than as the manifold pressure state equation. In recent a publication a Mean Value Engine Model (MVEM) has been constructed for an SI engine which is physically based and which has a simple physical form which can be immediately understood and manipulated.

Given the rather complicated set of coordinated control inputs which are necessary to control a spark ignition engine, primary control system development and evaluation can be a very difficult task. It is also difficult to develop microprocessor systems which are flexible enough for rapid system reconfiguration. In this paper it is shown that a Personal Computer (PC) provides an excellent solution to this common problem. Possible execution time problems are avoided by the use of a special multitasking environment and simple external hardware. The external hardware takes care of the cycle to cycle fueling and spark advance timing calculations. The PC itself uses its execution time only for calculating new fueling pulse widths and spark advance angles when the operating point of the engine changes. There is also extra computing capacity available for system simulations, condition monitoring, fault detection or perhaps driver information.

One of the most important operating modes for SI engines is in the idle speed region. This is because SI engines spend a large part of their time operating in this mode. Moreover, a large measure of operator satisfaction is dependent on an engine operating smoothly and reliably in and around idle. In particular the operator expects that the idle speed will remain constant in spite of the engine loads due to power steering pumps and air conditioning compressors. In the idle speed region an SI engine is thought to be quite nonlinear because the engine loading can be quite significant, thus forcing the engine to be driven through a reasonably large portion of its lower operating range. Many of the earlier studies of idle speed control systems have dealt with linearized models which in principle have limited validity for the problem at hand. In order to improve this situation, it is necessary to deal with the more general nonlinear control problem.

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.

Because there are no production-type sensors which are able to measure the flow directly at the intake port, it is becoming common practice to use models of varying complexity to infer the port air mass flow from other measurements. Given the tight requirements of modern air/fuel ratio (AFR) control strategies, the accuracy of these models needs to be better than ever, during steady-state of course (though λ feedback strategies are by design very robust), but mainly during transient operation. This paper describes why conventional models might be inaccurate during engine transients.

With the tightening of exhaust emission standards, wide bandwidth control of the air/fuel ratio (AFR) of spark ignition engines has attracted increased interest recently. Unfortunately, time delays associated with engine operation (mainly injection delays and transport delays from intake to exhaust) impose serious limitations to the achievable control bandwidth. With a proper choice of sensors and actuators, these limitations can be minimized provided the port air mass flow can be accurately predicted ahead in time. While the main objective of this work is to propose a complete AFR controller, the main focus is on the problems associated with port air mass flow prediction.

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.

Mean value engine models (MVEMs) seek to predict dynamically the mean values of important SI engine variables such as the crank shaft speed, the manifold pressure and the theoretical air/fuel ratio (lambda). Previous work also shows that such models can be made quite accurate, both for stationary and transient operating modes. Because these models can be made mathematically simple and compact, they are also tractable for direct mathematical and physical analysis. In this paper an analysis of a mean value engine model is carried out which reveals the underlying structure of the problems which face engine control system designers. In particular it is shown that an SI engine is extremely nonlinear and time dependent. Because of this, conventional control strategies using conventional sensors cannot be made to operate correctly in the transient regime. An “ideal” nonlinear compensator is also described for the fueling dynamics which works over a wide operating range.

Long term stoichiometric Air/Fuel Ratio (AFR) control of an SI engine is at the present mainly maintained by table mapping of the engine's fresh air intake as a function of the engine operating point. In order to reduce a stationary error in the AFR to zero the table based control normally works in conjunction with a PI feedback from a HEGO sensor. The effective bandwidth of this feedback loop is quite small and seldom exceeds 2 Hz. This is altogether too small for accurate transient AFR control. This paper presents a new λ (normalized Air/Fuel Ratio) control methodology (H∞ control) which has a somewhat larger bandwidth and can guarantee robustness with respect to selected engine variable and parameter variations.

In an earlier paper one of the authors of this paper (E. Hendricks and co-authors) treated the question of obtaining correct steady state and transient control of the air/fuel (A/F) ratio of an SI engine. This study was based in part on simulations conducted with a dynamic engine model developed earlier and in part on experimental results. The main conclusions were that conventional control strategies (Speed-Throttle, Speed-Density and Mass Air Flow (MAF)) cannot give proper A/F control because of 1. sensor and anti-aliasing filter time constants and 2. improper or lacking compensation for manifold fuel film and (air) filling dynamics. In this paper, the results of a long series of experiments conducted with the control systems above are to be presented. Both central fuel injection (CFI) (or throttle body (TBI)) and electronic fuel injection (EFI) (or multipoint (MPI)) manifolds have been investigated.

Long term control of the AFR (Air/Fuel Ratio) of spark ignition engines is currently accomplished with a selvoscillating PI control loop. Because of the intake/exhaust time delay, the oscillation frequency and hence bandwidth of this loop is small. This paper describes a new approach to the design of this control loop using a novel observer system. In this way the bandwidth of this important loop is increased by a factor of 2 - 6 times, leading to more accurate overall AFR control. Moreover the observer approach is so robust and allows such feedback levels that it reduces significantly the accuracy required in the calibration of the base fuel control system with which it is be used. It can be used with either conventional- or advanced observer based- base fuel strategies.