Elbert Hendricks

Mean Value Modelling of Spark Ignition Engines

ed and indexed in the SAE Global Mobility Database. No part of this publication may be reproduced in any form, in an electronic retrieval system or otherwise, without the prior written permission of the publisher. ISSN 0148-7191 Copyright 1990 Society of Automotive Engineers, Inc. Positions and opinions advanced in this paper are those of the author(s) and not necessarily those of SAE. The author is solely responsible for the content of the paper. A process is available by which discussions will be printed with the paper if it is published in SAE Transactions. For permission to publish this paper in full or in part, contact the SAE Publications Group. Persons wishing to submit papers to be considered for presentation or publication through SAE should send the manuscript or a 300 word abstract of a proposed manuscript to: Secretary, Engineering Activity Board, SAE.

Engine Modelling for Control Applications: A Critical Survey

In earlier work published by the author and co-authors, a dynamic enginemodel called a Mean Value Engine Model (MVEM) was developed. This model isphysically based and is intended mainly for control applications. In itsnewer form, it is easy to fit to many different engines and requires littleengine data for this purpose. It is especially well suited to embedded modelapplications in engine controllers, such as nonlinear observer basedair/fuel ratio and advanced idle speed control. After a brief review of thismodel, it will be compared with other similar models which can be found inthe literature. The attempt will be made to point out the differencesbetween the new modified MVEM and those developed elsewhere. In particular,the questions of fitting simplicity and general applicability are to betreated.

Isothermal vs. Adiabatic Mean Value SI Engine Models

Abstract Mean Value Engine Models (MVEMs) are an important paradigm for the study, analysis and control of Spark Ignition (SI) and diesel engines. Such models are simple, physical, compact and contain just enough detail to be statically and dynamically accurate. They are may be analyzed directly using simple techniques. In this paper an analysis is made of a new adiabatic MVEM which more accurately describes the behavior of SI engines with Exhaust Gas Recirculation (EGR) than conventional isothennal MVEM models. This analysis shows that the dynamics of SI engines are dominated by linear and nonlinear elements which may be simply understood.

Compact and Accurate Turbocharger Modelling for Engine Control

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.

A Compact, Comprehensive Model of Large Turbocharged, Two-Stroke Diesel Engines

Modele mathematique complet et compact pour un grand moteur diesel deux temps avec suralimentation par turbocompresseur

Linear Control System Design

In this chapter a review of the design of multivariable feedback controllers for linear systems will be considered. This review treats mainly deterministic control objects with deterministic disturbances. After giving an overview of the type of linear systems to be treated, this chapter will handle the basic control system design method known as pole or eigenvalue placement. First systems where measurements of all the states are available will be treated. For cases when such complete state measurements are not available the concept of deterministic observers to estimate the states which are not measured directly will be introduced. It will also be shown that it is often possible to design reduced order observers where only the unmeasured states are estimated.

SI Engine Observers Realized Using Optimized Integration Algorithms

Abstract In this paper the design of a full order nonlinear observer for a spark ignition (SI) engine is treated. When used together with a nonlinear fuel film compensator, this observer makes possible accurate transient and steady state control of the combustion air/fuel ratio over the entire operating range of the engine. The nonlinear compensator and observer are discretized using continuous integration algorithms and thus are realized as continuous differential equations. Because the observer is for a very stiff system, it has been necessary to design a special integration algorithm to integrate it in real time.

Minimum energy control of a large diesel engine

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. 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.-Burmeister and Wain Diesel A/S, Copenhagen Denmark.

Optimal Observers: Kalman Filters

This chapter has the purpose of reviewing the most important design aspects of Kalman filters as well as some of their most important properties. Heuristic derivations are given of the Kalman filter `equations for both continuous time and discrete time dynamic systems. It is shown that the state mean values propagate according to the same observer equations as given in Chap. 4. Moreover it is shown that the state noise propagates according to the time dependent Lyapunov equation derived in Chap. 6. When measurements are made on the system this equation has to be modified with a term which expresses the decrease of uncertainty which the measurements make possible. The combination of these two results yields the main stochastic design equation for Kalman filters: the Riccati equation. Solving this equation immediately gives the optimal observer gain for a Kalman filter. Combining a Kalman filter with optimal or LQR feedback results in a very robust controller design: the LQG or Linear Quadratic Gaussian regulator.

Adaptive Minimum Energy Control of Large Ship Diesel Engines

Abstract The economics of running large ship diesel engines are such that even small savings (on the order of 0.1%) are of great interest to ship owners. The overall efficiency of a given engine is determined mainly by the design of the engine/propellor combination but small reductions in fuel consumption can be obtained through careful design of automatic controllers for the system. Such regulators require accurate models for the internal states and state functions of the engine and in particular for its thermal efficiency. A new nonlinear model for a large ship diesel engine has recently been obtained by one of the authors. This model gives reasonably accurate estimates of the thermal efficiency that may be expected under the most important operating conditions. On this basis an adaptive energy saving controller has been designed and tested. Depending on sea conditions, simulations suggest that savings on the order of those indicated above should be possible. The project is carried out in cooperation with M.A.N.-B & W Diesel A/S, Copenhagen, Denmark.