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The model described here is a dynamic mean value model which is small enough to be realized on a microcomputer. Nevertheless it contains significant and very accurate information on the gross internal variables of an engine (the indicated efficiency, scavenge ratio, scavenge efficiency, etc.). This makes the model useful for control and expert system application on line and/or in parallel with an operating diesel engine. Moreover, because of its simplicity, it gives an overall picture of engine operation which is not possible with more complex single engine cycle models. Comparisons with time dependent experimental data show that the model is very accurate both for static and dynamic predictions of engine performance over a large operating range.

This paper deals with a Mean Value Engine Model (MVEM) approach to dynamically estimating engine load torque. The load torque estimator is made using parameter estimation and an extended Kalman filter. The load torque estimator can be used to simplify the control logic of modern engines as it can detect load changes over time thereby accounting for wear and also differing accessory power consumption. The model can be implemented using a state equation for the crank shaft speed, and a state estimating a constant describing the loading level.

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.

In earlier work it has been shown that a nearly ideal solution to the problem of accurate estimation of the air mass flow to a central fuel injection (CFI) (or throttle body (TBI)) or EFI (or multi-point (MPI)) equipped engine is provided by using a closed loop nonlinear observer for the engine. With proper design this observer was shown to be both accurate and robust with respect to modelling end measurement errors. It is based on a Constant Gain Extended Kalman Filter (CGEKF). Since the publication of this work, another type of observer has emerged in the literature for which claims of great robustness have been made. This observer is based on new developments in the area of nonlinear control theory and is called a Sliding Mode Observer (SMO). In this paper these two types of observers are compared theoretically and experimentally on an engine mounted on a dynamometer. A very aggressive driving scenario is assumed for these tests.

Many modern control strategies for engine control are based on event based sampling. Operating the control strategy in the event domain makes it possible to obtain samples at specific crank shaft angles in the engine cycle, which is often desirable for certain control strategies. One of the biggest disadvantages involved with event based strategies is signal aliasing at low engine speeds or a high computational burden at higher engine speeds. This paper presents an easy solution to the aliasing problem above. If the data between the event based samples is stored using a time based strategy, it is shown here that a subsequent treatment of the sampled data as a time series together with a suitable low pass filter structure can avoid aliasing.

With the current trend towards engine downsizing, the use of turbochargers to obtain extra engine power has become common. A great difficulty in the use of turbochargers is in the modelling of the compressor map. In general this is done by inserting the compressor map directly into the engine ECU (Engine Control Unit) as a table. This method uses a great deal of memory space and often requires on-line interpolation and thus a large amount of CPU time. In this paper a more compact, accurate and rapid method of dealing with the compressor modelling problem is presented. This method is physically based and is applicable to all turbochargers with radial compressors for either Spark Ignition (SI) or diesel engines.

Many existing production engine controllers use event (or constant crank angle increment) based sampling and computation systems. Because the engine events are synchronized to the internal physical processes of an engine, it is widely accepted that this is the most logical approach to engine control. It is the purpose of this paper to deal with this assumption in detail and to illuminate various failures of it in practical systems. The approach of the paper is in terms of overall general control system design. That is to say that the problem of event based engine control is considered as a general control problem with its standard components: 1. modelling (engine plus actuator/sensor), 2. specification of desired performance goals, 3. control system design method selection and 4. experimental testing.

In an earlier paper, some of the authors of this paper pointed out some of the difficulties involved in event based engine control. In particular it was shown that event based (or constant crank angle) sampling is very difficult to carry out without running into aliasing and sensor signal averaging problems. This leads to errors in reading the air mass flow related sensors and hence inaccurate air/fuel ratio control. The purpose of this paper is first to demonstrate that the conjectures about the operator input spectrum in a vehicle do actually obtain during vehicle operation in realistic road situations. A second purpose is to extend earlier modelling work and to present an approximate physical method of predicting the level of engine pumping fluctuations at any given operating point. The physical method given is based on a modification of the Mean Value Engine Model (MVEM) of a Spark Ignition (SI) engine presented previously.

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.

The thermal efficiency of a large ship diesel engine is determined mainly by the design of the engine/propellor combination but small efficiency increments can be obtained through the careful design of automatic controllers for the system. A fuel saving regulator requires an accurate model for the internal states of the engine in order that its thermal efficiency can be maximized. Such a model has been recently obtained by one of the authors (E. Hendricks). This model has been shown to give accurate estimates of the thermal efficiency which can be expected under normal sea conditions. Using the model as a basis an adaptive energy minimizing controller has been designed and tested. Depending upon sea conditions, simulations suggest that fuel savings on the order of 0.5% can be expected. Though small percent-wise, savings on this order could more than pay for the installation costs of the regulator during the first year of use. The project is carried out in cooperation with M.A.N.

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.

A very important component of an accurate steady state and transient air/fuel (A/F) ratio control strategy is the transient fuel compensation (TFC) substrategy. This is the part of an engine control algorithm which cancels the fuel film dynamics and makes it possible to place injected fuel into the intake manifold (or close to the intake ports or valves) of a spark ignition (SI) engine at the correct time and location. This paper presents the results of a very large series of experiments conducted with the same engine with either a throttle body (TBI) (or central fuel injection (CFI)) manifold or with a multi-point port injection (MPI) (or electronic fuel injection (EFI)) manifold. These experiments have shown that in some practical applications it may be necessary to model the intake manifold as a two time constant dynamic system rather than as a single differential equation system.

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.

Open-source software is growing in popularity and becoming a real alternative to proprietary software. This is not only happening in home and office applications, but in embedded, industrial and engineering applications as well. This paper shows how open-source software can be used to develop and test engine models, both in simulations and in laboratory tests with real engines. It begins by explaining what open-source software is, its advantages and disadvantages compared to proprietary software, and what relevant software for engine modelling is already available. The paper describes the experience of the Engine Control Group (ECG) at the Technical University of Denmark (DTU), using a full open-source Linux solution including some software developed by the group itself. Finally, the paper concludes with the pros and cons of using this approach, suggesting tools and features to be developed in the future.

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.