Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi-
nent aviation concept due to the advantageous such as stealth operation and
zero emission. In addition, fuel cell powered electric UAVs are more attrac-
tive as a result of the long endurance capability of the propulsion system.
This dissertation investigates novel power management architecture for fuel
cell and battery powered unmanned aerial vehicle propulsion application.
The research work focused on the development of a power management
system to control the hybrid electric propulsion system whilst optimizing
the fuel cell air supplying system performances.
The multiple power sources hybridization is a control challenge associated
with the power management decisions and their implementation in the power
electronic interface. In most applications, the propulsion power distribu-
tion is controlled by using the regulated power converting devices such as
unidirectional and bidirectional converters. The amount of power shared
with the each power source is depended on the power and energy capacities
of the device. In this research, a power management system is developed
for polymer exchange membrane fuel cell and Lithium-Ion battery based
hybrid electric propulsion system for an UAV propulsion application. Ini-
tially, the UAV propulsion power requirements during the take-off, climb,
endurance, cruising and maximum velocity are determined. A power man-
agement algorithm is developed based on the UAV propulsion power re-
quirement and the battery power capacity. Three power states are intro-
duced in the power management system called Start-up power state, High
power state and Charging power state. The each power state consists of
the power management sequences to distribute the load power between the
battery and the fuel cell system. A power electronic interface is developed Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi-
nent aviation concept due to the advantageous such as stealth operation and
zero emission. In addition, fuel cell powered electric UAVs are more attrac-
tive as a result of the long endurance capability of the propulsion system.
This dissertation investigates novel power management architecture for fuel
cell and battery powered unmanned aerial vehicle propulsion application.
The research work focused on the development of a power management
system to control the hybrid electric propulsion system whilst optimizing
the fuel cell air supplying system performances.
The multiple power sources hybridization is a control challenge associated
with the power management decisions and their implementation in the power
electronic interface. In most applications, the propulsion power distribu-
tion is controlled by using the regulated power converting devices such as
unidirectional and bidirectional converters. The amount of power shared
with the each power source is depended on the power and energy capacities
of the device. In this research, a power management system is developed
for polymer exchange membrane fuel cell and Lithium-Ion battery based
hybrid electric propulsion system for an UAV propulsion application. Ini-
tially, the UAV propulsion power requirements during the take-off, climb,
endurance, cruising and maximum velocity are determined. A power man-
agement algorithm is developed based on the UAV propulsion power re-
quirement and the battery power capacity. Three power states are intro-
duced in the power management system called Start-up power state, High
power state and Charging power state. The each power state consists of
the power management sequences to distribute the load power between the
battery and the fuel cell system. A power electronic interface is developed with a unidirectional converter and a bidirectional converter to integrate the
fuel cell system and the battery into the propulsion motor drive. The main
objective of the power management system is to obtain the controlled fuel
cell current profile as a performance variable. The relationship between the
fuel cell current and the fuel cell air supplying system compressor power
is investigated and a referenced model is developed to obtain the optimum
compressor power as a function of the fuel cell current. An adaptive controller
is introduced to optimize the fuel cell air supplying system performances
based on the referenced model. The adaptive neuro-fuzzy inference
system based controller dynamically adapts the actual compressor operating
power into the optimum value defined in the reference model. The online
learning and training capabilities of the adaptive controller identify the
nonlinear variations of the fuel cell current and generate a control signal for
the compressor motor voltage to optimize the fuel cell air supplying system
performances.
The hybrid electric power system and the power management system were
developed in real time environment and practical tests were conducted to
validate the simulation results.
| Identifer | oai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/8038 |
| Date | 08 October 2013 |
| Creators | Karunarathne, L |
| Contributors | Economou, J |
| Source Sets | CRANFIELD1 |
| Detected Language | English |
| Type | Thesis or dissertation, Doctoral, PhD |
Page generated in 0.0024 seconds