Dokument 1.pdf - Universität Siegen

Abstract
In the third chapter the dynamic system of the air and fuel path of an SI-engine is
modeled and the necessary mathematical descriptions for the identification strategy in
chapter seven will be discussed. Furthermore, influences and disturbances in the air and
fuel path will be discussed, for example tank bleeding and air fuel ratio adaptation, and
possibilities to minimize the later failure during the identification process will be shown.
The estimation procedures for linear plant behaviour are shown in the fifth chapter.
Pure parameter estimators and parameter and state estimators are analysed for the
linear problem representation. The linear regression problem for parameter estimators
is realised with a Weighted Recursive Least Squares Algorithm, a Recursive Maximum
Likelihood approach and a linear Kalman Filter. The presented procedure is similar to
the ordinary parameter identification process of LTI 3 systems in the control technology.
The results of the calibration process are the identified coefficients of the transfer function.
Furthermore, parameter and state estimators with an extended and an adaptive Kalman
Filter are presented. The benefit of using a parameter and state estimators is the possibility
to directly estimate the physical parameters of a given state space representation.
The sixth chapter discusses the nonlinear estimation procedures. The use of parameter
estimators is only possible after linearization of the nonlinear model and results in the
same estimation procedures as for the linear case. The parameter and state estimators
of the linear case are extended to the realization for the nonlinear problem representation.
The result is an Extended Kalman Filter and an adaptive Extended Kalman Filter
approach.
Following the representation of mathematical models, the model of the plant and the
estimation procedures for linear and nonlinear model representation, the application of
calibrating the air and fuel path of an SI-engine with real data demonstrates the effectiveness
of the described proceedings.
The first step is to estimate the manifold time constant and the variable delay time from
the moment the outlet valve opens until the exhaust gas reaches the air-fuel-ratio sensor
using a linear adaptive Kalman Filter.
The second step is to identify the fuel path, especially the wall wetting parameters, using
a linear Kalman Filter approach to solve the regression problem. The two step calibration
procedure is used to identify the operating range of the used SI-engine with real data.
The results for the calibration of the dynamic of the air and fuel path are very good. Therefore,
the algorithms shown in this work are particularly useful for automated schemes
for future dynamic calibration applications.
3 Linear Time Invariant
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