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Name : Soo-Youl Park
Title : Development of mean value engine model using ANN
Objective : To develop a fast-running engine model with
sufficient accuracy over wide
range of operating conditions
Motivation :
In case of automotive engine simulation model, there are two
requirements. First, ithas high accuracy, second, it should run
fast. Especially demands for fast running model
become increasing. Modern engines have sophisticated control
device so that the enginemodel and control model should be
connected to each other and both engine simulation
and control simulation should be performed simultaneously.
Furthermore, for theapplication of HIL(Hardware in the loop), the
engine model should run at real time.(In
case of 2000 rpm of engine operating, actual time for one engine
cycle is 0.06s, butcalculation time for one cycle with typical
engine model is 120s, which is 2000 times
more than real time.)The engine simulation is a kind of physical
simulation which calculates the gas
dynamics, combustion phenomena and chemical reaction within the
engine. Therefore, inorder to guarantee high accuracy results, the
model should be complicated. It means that
these two requirements, higher accuracy and fast running,
conflict with each other.However, recent researches show that this
conflicting problem can be solved by
applying ANN to the engine model and this engine model supported
by ANN is called asmean value engine model. In this study, I will
show the methodology how to build this
mean value engine model by minimizing sacrifice of accuracy of
results.
Approach :With commercial engine simulation software, detailed
engine model will be
developed. Based on this model, the model regression will be
done by applying ANN.This work will be composed of following
steps
(1) Identify important variables which can represent the
results;(2) Conduct simulations using a detailed engine model, and
train neural networks
with simulation results;(3) Build a mean value engine model
using trained neural networks;
Reference :
He, Y., Development and Validation of a Mean Value Engine Model
for IntegratedEngine and Control System, SAE Paper 2007-01-1304,
2007.
Papadimitriou, I., Warner, M., Silvestri, J., Lennblad, J., and
Tabar, S., Neural Network
Based Fast Running Engine Models for Control-Oriented
Applications, SAE Paper2005-01-0072.
The Mathworks Inc., Model-based Calibration Toolbox, Version 3,
2006.
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