Open Access Repository

SI engine modeling using artificial neural networks as virtual sensors


Downloads per month over past year

Fu, Si Hua 2007 , 'SI engine modeling using artificial neural networks as virtual sensors', Research Master thesis, University of Tasmania.

[img] PDF (Whole thesis)
whole_FuSiHua20...pdf | Request a copy
Full text restricted
Available under University of Tasmania Standard License.


Spark ignition (SI) engines have been developed for more than one century. In today's
modern vehicles, the SI engine has been demonstrated to be efficient and reliable.
However, automotive engineers are still seeking to improve and optimize SI engine
performance. The key to optimize the SI performance is the setup of the engine control
unit (ECU). The setup includes various engine parameters, such as engine speed, brake
torque, and air intake mass flow etc. Problems can arise in how to acquire the data of
desired engine parameters for constructing the SI engine control system. Engine sensors
and some special equipment are employed to acquire the desired data. The equipment
includes the engine dynamometer and gas analyzer that are much more expensive than
engine sensors. In addition, the setup process of the ECU is a time consuming procedure
by testing the engine on the dynamometer.
The engine system is highly non-linear, as simple mathematical equations have
difficulty expressing the relationship between each engine parameter. Assumptions and
simplifications must be made in conventional engine models. Artificial neural networks
have the ability to model and optimize non-linear dynamic systems. Thus, the neural
network may be an alternative approach for engine modeling if there is a lack of
understanding of the engine system.
This study aims to apply of artificial neural networks as virtual sensors in SI engine
modeling. A Holden Vectra engine was employed for this study. The selected engine
input parameters depend on the availability of the engine sensors. Power, fuel
consumption and emissions are the output parameters which must be measured by the
engine dynamometer and the gas analyzer. A total number of 8 inputs and 9 outputs were
selected in the engine modelling. Two different artificial neural network structures
namely: BackPropagation (BP) and Optimization Layer by Layer (OLL) neural networks
were compared based on output parameters prediction. Two data sets were obtained for
model training and prediction respectively. This work is a step towards establishing
application of artificial neural networks as virtual sensors for IC engine modeling.

Item Type: Thesis - Research Master
Authors/Creators:Fu, Si Hua
Copyright Holders: The Author
Copyright Information:

Copyright 2007 the Author - The University is continuing to endeavour to trace the copyright
owner(s) and in the meantime this item has been reproduced here in good faith. We
would be pleased to hear from the copyright owner(s).

Additional Information:

No access or viewing until written permission from the University of Tasmania is obtained. Thesis (MEngSc)--University of Tasmania, 2007. Includes bibliographical references

Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page