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|Title: ||The application of black box models to combustion processes in the internal combustion engine|
|Authors: ||Maass, Bastian|
|Issue Date: ||2011|
|Publisher: ||© Bastian Maass|
|Abstract: ||The internal combustion engine has been under considerable pressure during the last few years.
The publics growing sensitivity for emissions and resource wastage have led to increasingly
stringent legislation. Engine manufacturers need to invest significant monetary funds and
engineering resources in order to meet the designated regulations.
In recent years, reductions in emissions and fuel consumption could be achieved with advanced
engine technologies such as exhaust gas recirculation (EGR), variable geometry turbines
(VGT), variable valve trains (VVT), variable compression ratios (VCR) or extended aftertreatment
systems such as diesel particulate filters (DPF) or NOx traps or selective catalytic
reduction (SCR) implementations.
These approaches are characterised by a highly non-linear behaviour with an increasing demand
for close-loop control. In consequence, successful controller design becomes an important part
of meeting legislation requirements and acceptable standards. At the same time, the close-loop
control requires additional monitoring information and, especially in the field of combustion
control, this is a challenging task. Existing sensors in heavy-duty diesel applications for incylinder
pressure detection enable the feedback of combustion conditions. However, high
maintenance costs and reliability issues currently cancel this method out for mass-production
vehicles. Methods of in-cylinder condition reconstruction for real-time applications have been
presented over the last few decades. The methodical restrictions of these approaches are
Hence, this work presents a method utilising artificial neural networks for the prediction of
combustion-related engine parameters. The application of networks for the prediction of parameters
such as emission formations of NOx and Particulate Matters will be shown initially.
This thesis shows the importance of correct training and validation data choice together with
a comprehensive network input set. In addition, an application of an efficient and accurate
plant model as a support tool for an engine fuel-path controller is presented together with an
efficient test data generation method.
From these findings, an artificial neural network structure is developed for the prediction
of in-cylinder combustion conditions. In-cylinder pressure and temperature provide valuable
information about the combustion efficiency and quality. This work presents a structure that
can predict these parameters from other more simple measurable variables within the engine
auxiliaries. The structure is tested on data generated from a GT-Power simulation model and
with a Caterpillar C6.6 heavy-duty diesel engine.|
|Description: ||A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.|
|Appears in Collections:||PhD Theses (Aeronautical and Automotive Engineering) |
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