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Artificial neural network control strategies for fuel cell hybrid system

The greening of air transport is the driver for developing technologies to reduce the
environmental impact of aviation with the aim of halving the amount of carbon dioxide
(COଶ) emitted by air transport, cutting specific emissions of nitrogen oxides (NO୶) by 80%
and halving perceived noise by the year 2020. Fuel Cells (FC) play an important role in the
new power generation field as inherently clean, efficient and reliable source of power
especially when comparing with the traditional fossil-fuel based technologies.
The project investigates the feasibility of using an electric hybrid system consisting of a fuel
cell and battery to power a small model aircraft (PiperCub J3). In order to meet the desired
power requirements at different phases of flight efficiently, a simulation model of the
complete system was first developed, consisting of a Proton Exchange Membrane hybrid fuel
cell system, 6DoF aircraft model and neural network based controller. The system was then
integrated in one simulation environment to run in real-time and finally was also tested in
hardware-in-the-loop with real-time control.
The control strategy developed is based on a neural network model identification technique;
specifically Model Reference Control (MRC), since neural network is well suited to nonlinear
systems. To meet the power demands at different phases of flight, the controller controls the
battery current and rate of charging/discharging.
Three case studies were used to validate and assess the performance of the hybrid system:
battery fully charged (high SOC), worst case scenario and taking into account the external
factors such as wind speeds and wind direction. In addition, the performance of the Artificial
Neural Network Controller was compared to that of a Fuzzy Logic controller. In all cases the
fuel cell act as the main power source for the PiperCub J3 aircraft. The tests were carried-out
in both simulation and hardware-in-the-loop.

Identiferoai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/7964
Date05 1900
CreatorsOheda, Hakim
ContributorsSavvaris, Al
PublisherCranfield University
Source SetsCRANFIELD1
LanguageEnglish
Detected LanguageEnglish
TypeThesis or dissertation, Doctoral, PhD
Rights© Cranfield University 2013. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright owner

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