energy optimization for flying base station
TRANSCRIPT
UNIVERSITY OF ZIMBABWE
DEPARTMENT OF ELECTRICAL ENGINEERING
FACULTY OF ENGINEERING
ENERGY OPTIMIZATION FOR FLYING BASE STATION
BY
CHAIBVA KELVIN
(R1611702)
SUPERVISOR: Dr M MUNOCHIVEYI
CO-SUPERVISOR: Dr P MANYERE
THESIS TO OBTAIN THE AWARD OF MASTER OF SCIENCE DEGREE IN
COMMUNICATION ENGINEERING
02 December 2018
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Declaration
I, Kelvin Chaibva hereby declare that this research document except where indicated by
referencing or citation, is my own work carried out under supervision in the Department of
Electrical Engineering, Faculty of Engineering, University of Zimbabwe, Harare. I further
declare that this dissertation either in whole or part, has not been presented for another degree
award at this University or elsewhere.
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Student Date
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Supervisor Date
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Co-Supervisor Date
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Abstract
This thesis presents ambient energy harvesting techniques to enhance endurance of a flying base
station mounted on an Unmanned Aerial Vehicle (UAV) as well as to extend flight duration of
the UAV mounted base station. Two techniques are presented here namely, harvesting ambient
energy from flexible thin-film photovoltaic panels mounted on top of a Quadcopter fuselage. The
other approach presents the use of piezoelectric generation wherein vibrations between the
Quadcopter rotors and fuselage are transformed into electrical power and used to provide extra
electric energy for the UAV established base station
Quadcopters, also known as drones, are unmanned aerial automobiles which function without
human intervention or loosely put, they are pilotless airplanes. They operate by and large in
situations in which the presence of an on-board human pilot is both too risky and unnecessary
hence the name UAV. Endurance for these machines is a major cause for concern in order to
achieve their operational goals. Regardless of the on-board battery powering the high-power
consuming motors and all equipment, the flight time is still fairly low. Either the Quadcopters
need to fly to base at the end of each sortie or personnel need to follow the rotorcraft to exchange
the batteries. This notably reduces overall performance and the range of operations. A lot of
research is done on making Quadcopters as autonomous as possible, but to make them truly
autonomous the energy problem needs to be solved.
Most drones are electrically powered and there is a vital impediment on their size, weight and
power hence they cannot carry enormous amount of load (i.e. payload). The energy sources are
normally in the form of batteries and because of the above limitation (payload), the flight
duration is commonly restrained to a few tens of minutes [1].
The purpose of this thesis has been to deal with the energy trouble and make the Quadcopters
self-sustainable over a longer time period. The proposed answer has been to use solar power to
recharge the on-board batteries during flight in addition to out in the field. Unlike all known
earlier attempts to use solar power for rotorcrafts, this is the first known project to modify an
existing commercial quadcopter to use solar power for recharging. The results conclude that the
idea of using solar power is proved to be viable for small commercially available rotorcrafts with
limited or constrained available space for solar panels.
The use of renewable energy sources is growing and will play an important role in the future
power systems. A five parameter model of PV modules has been implemented in
Simulink/Matlab. The parameters of the model are determined by an approximation method
using available data sheet values. Inputs to the model include light intensity and ambient
temperature. The outputs are any measurements of interests in addition to electrical power, cell
temperature and voltage. Effects of varying the model parameters are demonstrated. A maximum
power point tracking algorithm is used to keep the voltage at the maximum power point at all
times. A battery model based on discharge curve fitting is implemented. The model is based on a
fundamental battery cell which can be modified to construct many different module
configurations. Power smoothing algorithms which average the input over a set time, are used to
provide a power reference to the battery system.
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Acknowledgements
I would like to thank Dr. M Munochiveyi and Dr. P Manyere for giving me the opportunity to
work under their supervision. Their priceless support and direction was second to none and they
were always available to help me throughout the entire project. I also want to express my sincere
gratitude to my classmates, whose contribution and support was second to none.
Kelvin Chaibva
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Contents
Declarationβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.β¦β¦β¦β¦β¦β¦β¦β¦β¦ i
Abstract...................................................................................................................... ii
Acknowledgements.................................................................................................... iii
List of Figures............................................................................................................ v
List of Abbreviationsβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..β¦... vi
Chapter 1. Introduction.............................................................................................. 1
1.1 Introductionβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦............................................. 1
1.2 Problem Statement................................................................................... 2
1.3 Motivation β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦................... 3
1.4 Aimsβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦... 4
1.5 Objectivesβ¦β¦β¦β¦β¦............................................................................ 4
1.6 Justification............................................................................................. 6
1.7 Thesis Outline and Organization............................................................ 7
Chapter 2. Literature Review and Background.......................................................... 7
2.1 Introductionβ¦β¦β¦.................................................................................. 7
2.2 Unmanned Aerial Vehicles...................................................................... 7
2.3 Energy Harvesting Techniques................................................................ 10
2.3.1 Vibration Energy Harvesting Technique.................................. 12
2.3.2 Solar Energy Harvesting Technique β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 13
2.3.3 Solar Panelsβ¦β¦β¦β¦β¦β¦.....β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 14
2.3.4 Maximum Power Point Trackingβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 15
2.4 Backgroundβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 15
2.4.1 Vibration Energy Harvesting Techniqueβ¦β¦β¦β¦β¦β¦β¦ 17
2.4.2 Solar Energy Harvesting Technique β¦β¦β¦β¦β¦β¦β¦β¦β¦ 19
2.4.2.1 Solar Energyβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 19
Chapter 3. Methodologyβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 20
3.1 Introductionβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 20
3.2 Electric Quadcopter Adapted to Photovoltaic Energyβ¦β¦β¦β¦ 21
3.3 Energy Metrics of a Quadcopterβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 21
3.4 Solar Irradianceβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 22
3.4.1 Angstrom Modelβ¦β¦...................β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 22
3.5 Global Irradiance Matlab Model Output Graphsβ¦β¦β¦β¦β¦.. 24
3.6 Solar PV System Block Diagramβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 26
3.7 Ambient temperatureβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 32
3.8 Battery Modelingβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 33
3.9 Battery Model Implementationβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 36
3.10 Maximum power point trackingβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 39
3.10.1 Introductionβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 39
v
3.10.2 Perturb and observe methodβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 39 3.11 Quadcopter Framework β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 42
3.11.1 Frameβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 42 3.11.2 Motorsβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 42
3.11.3 Electronic Speed Controllers (ESC)β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 43
3.11.4 Flight Controller Boardβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 43
3.11.5 Transmitter and Receiverβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 44
3.11.6 Lithium Polymer 5000mAh 11.1v Batteryβ¦β¦β¦β¦β¦β¦β¦β¦ 44
3.11.7 Solar Panel Designβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 45
3.11.7 Solar Panel Designβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 46
Chapter 4. Conclusion and Future Workβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦...β¦ 47
4.1 Conclusionβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.β¦.β¦.. 47
4.2 Future Workβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦...β¦.β¦.. 47
4.2.1 Developments in Solar Technologyβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 47
Referencesβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.β¦β¦48
Appendix Aβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..β¦.51
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List of Figures
Fig.2.0 Microwave powered helicopter setupβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦... 11
Fig. 2.1 Shoe mounted energy harvester prototypeβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.... 12
Fig. 2.2 Piezoelectric energy harvesting conceptβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦... 16
Fig. 2.3 Flexible Thin-film Photovoltaic Panelβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 17
Fig. 2.4 Unimorph piezoelectric cantilever beamβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 18
Fig. 2.5 A cymbal-shaped piezoelectric transducerβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 18
Fig.2.6 World Solar Insolation Mapβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 19
Fig. 3.1 Proposed Solar Quadcopter Architectureβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..... 20
Fig. 3.2 Quadcopter Energy Flowβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 21
Fig. 3.3 Peak Sun Hour (PSH) from 24 hour global solar radiation for Gweruβ¦β¦. 24
Fig. 3.4 Peak Sun Hour (PSH) from 24 hour global solar radiation for Mazowe⦠25
Fig. 3.5 Hourly Global Irradiance Model Output Graphsβ¦β¦β¦β¦β¦β¦β¦β¦β¦... 25
Fig. 3.6 Overview of system used for simulationβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 26
Fig.3.7 Practical Equivalent Circuit PV Modelβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 26
Fig.3.8 Simulink Block Implementation of Photocurrent πΌπββ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 28
Fig. 3.9 Simulink Block Implementation of Reverse Saturation Current πΌ0β¦β¦.... 29
Fig. 4.0 PV moduleβ¦β¦β¦β¦β¦..β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 30
Fig. 4.1 PV Module Implementation Blocksβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 30
Fig. 4.2 Temperature Controlled Blockβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 31
Fig. 4.3 Implementation of MPPT and Voltage Controllerβ¦β¦β¦β¦β¦β¦β¦β¦β¦.. 31
Fig.4.4 I-V and P-V curves with different radiation intensitiesβ¦β¦β¦β¦β¦β¦β¦β¦ 32
Fig.4.5 I-V and P-V curves with different ambient temperaturesβ¦β¦β¦β¦β¦β¦β¦. 33
Fig.4.6 Basic Equivalent Circuit for a Batteryβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦...... 33
Fig. 4.7 Battery Model Implementationβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 36
Fig. 4.8 Averaging Algorithm Block Implementationβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 36
Fig. 4.9 Voltage Controlled Battery Module Implementationβ¦β¦β¦β¦β¦β¦β¦β¦β¦ 37
Fig.4.10 Matlab/Simulink Battery Charging and Discharging Modelβ¦β¦β¦β¦β¦β¦. 37
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Fig. 4.11 Battery Charging-Discharging Characteristicsβ¦β¦β¦β¦β¦β¦β¦ 38
Fig.4.12 Typical discharge patternβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 39
Fig.4.12 Flow Diagram of the Perturb and Observe Algorithmβ¦β¦β¦β¦. 40
Fig. 4.13 Graphical Layout of Solar Panels on Quadcopterβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 41
Fig. 4.14 F450 Quadcopterβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 42
Fig.4.15 Electronic Speed Controllers (ESC)β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦. 43
Fig 4.16 Lithium Polymer (Li-Po) batteryβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.. 44
Fig. 4.17 120W 12v Flexible Slim Solar Panelβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦ 45
1
CHAPTER 1
INTRODUCTION
1.1 Introduction
Wireless Cellular Technology relies for its operation completely on antennas established on
constant terrestrial towers called Base Stations for the transmission and reception of
communication alerts between Mobile Stations (MS) and Mobile Switching Centers (MSC).
These base stations are permanently set up within the cells they serve and their quantity is
determined by the approximate range of subscribers within that cell to attain a terrific best of
provider. Each service provider will have a certain number of base stations to cover the targeted
areas. The population density and geographical variations determine the number of base stations
to be installed in any given location or cell. The criterion or technique used to determine the
deployment location and distance of the cell sites is the bandwidth. In an area flooded with a
massive number of subscribers, a certain number of cell sites which are located at close
proximity to subscribers is needed to ensure that the quality of service (QoS) is not affected.
Furthermore, service providers are well aware of particular base stations which they consider as
cash cows that bring good revenue and hence healthy return on investment for the organization.
As an example, NetOne base station in Kuwadzana area brings in more revenue than all other
base stations during peak traffic hours. This service provider cannot afford to have that base
station malfunctioning for any reason or the other. These cell sites are to be always up and
running at all times otherwise a significant loss in revenue is incurred in the event that these sites
are down.
With growing demands for multimedia and data services in next generation cellular networks,
managing network congestion caused by unexpected and temporary events has gained significant
importance. A recently emerging solution is assisting the mobile network by the use of low-
altitude unmanned aerial vehicles (UAV) or Drones equipped with transceivers, commonly
known as UAV-mounted base stations or Flying Base Station in other spheres.
UAV-mounted base stations are moving base stations with wireless backhaul. They are
specifically critical in modern-day metropolitans, because population density ensures revenue,
and proliferates potential applications, such as improving resilience of smart cities [2], or
augmenting and providing additional coverage [3], to name a few. These base stations are
launched in space and hover (Quadcopter) over the affected area providing or augmenting
network connectivity. Subscribers may now temporarily connect to the world via these base
stations.
Quadcopters are multi-rotor helicopters which firstly had been mostly utilized by hobby Radio
Controlled (RC) plane aviators. But pretty quickly it was located that they may also be used for a
number of other important packages. The most apparent application is to attach a digital camera
and get a birdβs eye view. The quadcopter can then be used for surveillance, finding missing
people, crowd manipulation, 3-Dimensional mapping and to get a view over dangerous
environments where it isn't secure for humans to go into. They can also be equipped with a
2
number of instruments and sensors, making it useful for business purposes. As an example they
could use magnetometers for amassing magnetic field facts and accumulate statistics for
prospectus maps for the mining industry. Quadcopters have in recent years even come to be a
topic of studies in area associated programs inclusive of planetary exploration
In this project the energy supply problem is addressed and the aim is to make Quadcopters more
self-sustainable and autonomous. The proposed solution is to use solar power to recharge the
batteries during flight and on site in an open field. The most suitable solar panel layouts and
energy harvesting methods is examined, and an electronic system is optimized to provide
maximal power output for recharging the batteries of the craft. At this point the aim is to prove
that a concept is possible despite the very small area available for possible placement of solar
panels. The benefits of a positive outcome would include savings of human effort, increased
operation range and reduced risk to human life as a quadcopter will be able to operate in
hazardous areas where it is not safe for humans to enter.
1.2 PROBLEM STATEMENT
A major challenge or drawback in electrically powered UAV technology has been endurance or
its ability to remain airborne for longer periods so as to accomplish a designated mission. The
source of power for the UAV is usually the battery which also forms part of the payload and as a
requirement must be of light weight to ensure minimum payload. The payload carried by the
drone determines the flying and operational time of the UAV. Conventional UAVs can fly
typically only tens of minutes (e.g. 15-30 min) [2], which seems to be limiting. Such flying time
is not enough for situations or scenarios where operation of the cell site is supposed to cover
longer traffic hours due to unplanned and temporary events in the network.
Proposals to implement multiple drones that will alternately be deployed as others are being re-
charged at the ground station has proved to be costly and inefficient. To be able to provide
network coverage for a period of say 1 hour, one needs at least three drones which are alternately
launched as the others return home for recharging. Issues of handing over network connectivity
from one drone to another during changeover have also proven to be complex, hence the need to
have a single drone that endures longer operational flight times.
The average flight duration of a battery powered drone with type 3S 2200mAh Lithium Polymer
(Li-Po) battery is about 9-10 minutes [4], which is very low if the drone is to be used in mission
intensive application. Most drones crash land or at worst get lost because they run out of energy
during operational flight, failing to achieve their purpose resulting in damage and property loss
while in the middle of a mission. Therefore a means to improve the UAVs operational flight
duration will be very beneficial to its application.
3
1.3 MOTIVATION
The motivation behind this thesis is to analyze the feasibility of harvesting energy from solar
luminance using thin-film solar panels mounted on top of the UAV, in addition to harvesting
piezoelectric transducer energy from vibrations among the Quadcopter rotors and the rigid
airframe of the drone that allows you to extend the flight duration of the UAV-mounted base
station.
Cellular networks have been around for over forty years, and there has always been a need to
make smarter networks, which might be more dependable with lesser cost. This has led to the
evolution of numerous cellular and wireless technologies and mobile phones. Over the years,
there has also been a dramatic growth in the number of mobile telephone customers, and it is
believed that over 85% of the worldβs population today have access to mobile phones [2].
Currently, the most broadly used mobile technology within the world is the Global System for
Mobile communications (GSM) popular [3], even as the Fourth Generation Long Term
Evolution (4G LTE) general is hastily gaining ground due to the ever-developing need for better
bit rates and decrease latencies needed for high speed data communications. In this regard,
network service providers are fighting to maintain their networks always up and running and
reduce the effects of network congestion as subscribers increase exponentially.
4
1.4 AIM
The purpose of this project is to model and provide a way or means to improve the endurance
and extend flight duration of an UAV mounted base station. The base station will provide
replacement coverage in disaster or crisis situations as well as augment network coverage and
capacity in high demand areas. Endurance in UAVs is problematic because of the limited size of
the energy source (battery) that can be integrated into the Drone and maintain minimum payload.
A large portion of the total mass (payload) of many electric powered UAVs, for example, is the
rechargeable battery power source.
Feasibility of the research will be determined by Matlab/Simulink modelling to prove concept
before the real prototype can be implemented. The main aim is to construct an accurate model of
a photovoltaic system to recharge the onboard UAV energy storage. The models should be as
easily modifiable as possible. It is a goal to make the models so generally applicable so they can
be used as tools in other applications.
1.5 OBJECTIVES
To be able to achieve the above aim, the researcher intends among other objectives to simulate
and perform modelling in Matlab/Simulink to determine the feasibility of:
Designing a Photovoltaic solar energy harvesting model from flexible thin-film
photovoltaic panels fixed to the top of the UAVβs fuselage to harvest energy from
ambient sunlight.
Designing a Piezoelectric energy harvesting model derived from rotor vibrations and the
rigid body motions of the UAV fuselage.
5
1.6 JUSTIFICATION
Although there is an allowable variation for additional subscribers within a particular cell, a
sudden growth in demand for mobile cellular communication services usually occurs resulting in
an overloaded base station and poor grade of service (GOS) due to congestion as everyone
within that cell is intending to use the network services. This will result in dropped or lost calls
due to saturation of the entire network during that overload period.
Typical situations are concerts, large church gatherings, local and international sports events in
stadiums e.g. the World Cup Soccer showcase or Africa Cup Of Nations, political rallies,
national events, e.g. Independence or Heroes day celebrations, Zimbabwe International Trade
Fair exhibitions, Agricultural Shows, Roadshows and major vehicular traffic jams to mention a
few. Peak traffic hours are also a situation which results in sudden high demand for mobile
communication services.
Partial or complete damage to communication infrastructure due to natural disasters e.g. floods,
hurricanes, earthquakes, storms, terror attacks, cyber-attacks e.g. Denial of Service attacks cause
malfunctioning of base station equipment which results in no service at all for our subscribers.
As an example, several wireless base stations and various communication cables were damaged
during Hurricane Katrina. Adding to these woes the remaining sections of the network also failed
to provide satisfactory communication services to first responders and aid workers. In the event
of a natural or man-made disaster, passing information becomes a tedious task. In cases where
the communication devices or infrastructure survive the calamity, the probability of the network
getting congested is certainly high, e.g. New York City World Trade Center attack on 11
September 2001. During the 9/11 attack, the telecommunication systems were overloaded and
phone networks hopelessly congested [5].
A natural disaster in most cases results in power outages, leaving millions in the dark without
internet, mobile communications or landline communications e.g.: Hurricane Sandy of 29
October 2012. Thousands of houses were destroyed and millions were left without electricity and
thus without communication when Hurricane Sandy hit New York City, New Jersey and the
surrounding areas. The hardest hit areas are still experiencing serious power outages to date [5].
In order to mitigate these challenges, there is a need for a rapidly deployable communications
network that is reliable, robust and interoperable with existing cellular infrastructure and that
supports mission critical processes and mobile users. It should be capable of providing coverage
for a wide area, have a small footprint and last long enough to allow for restoration of the regular
commercial communications network. Thus, a prototype of a rapidly deployable cellular network
is a natural choice as a research platform for this purpose. There is need to therefore deploy Ad-
Hoc networks during such scenarios and for that particular situation for rapid service recovery to
augment network coverage.
6
1.7 THESIS OUTLINE AND ORGANIZATION
This thesis is organized as follows. Chapter 1 provides a basic idea of this thesis and the
motivation. The chapter outlines the problem statement, i.e. the reason for the research. The aim
and the respective objectives are spelt out in this chapter. The chapter concludes with
justification of the project as well as the brief outline of the thesis. Chapter 2 presents the
literature review, which outlines previous and ongoing related works done by other researchers.
Background information on UAV mounted base station communication systems and the
architecture of a UAV mounted base station is described in this chapter. The chapter also
presents the background of drone mounted base station and energy harvesting techniques.
Chapter 3 presents the Matlab/Simulink modelling of the energy harvesting techniques in more
detail. Chapter 4 gives results of the thesis. In Chapter 5 conclusions, future work is mentioned.
7
CHAPTER 2
LITERATURE REVIEW AND BACKGROUND
2.1 INTRODUCTION
Currently, network service vendors are embarking on base station offloading when confronted
with sudden upsurge in demand for network services resulting in critical network congestion at
the loaded cell site. However, this technique has proved to be inefficient and usually results in a
poor grade of service to our subscribers.
However in such eventualities mentioned in [6], UAV-aided cellular offloading affords a
promising alternative strategy to deal with the mobile network congestion difficulty, of which the
principle fees involved which includes the micro-base station and the airborne system may be
decreased than building new ground infrastructure. Furthermore, UAV-aided cell offloading
offers promising benefits compared to the traditional cellular network with constant ground base
stations, such as the capability for on-call for and swift deployment, more flexibility for network
reconfiguration, and higher conversation channels among the UAV and ground cell terminals
because of the dominant line-of-sight links.
Moreover, the UAV mobility gives additional layout tiers of freedom through trajectory
optimization as discussed in [7]. In [8] the writer highlights that, aerial base stations are able to
delivering wireless coverage for catastrophe relief in the course of or after a disaster strikes.
They are gaining popularity as a key source of communication for rapid deployment in areas of
search and rescue and relief works. These base stations can be set up even in faraway regions cut
off from the outdoor world in which the installation of a traditional cellular tower is uneconomic
and unjustifiable.
Published work relevant to this project is reviewed during this segment with the intention to
perceive opportunities and barriers posed by means of existing technologies. The overview
manner is also undertaken to perceive gaps which may be addressed. System requirements and
research questions are then in turn derived from this accumulated knowledge.
2.2 UNMANNED AERIAL VEHICLES (DRONES)
Drones are unmanned aerial motors flown through either faraway control or autonomously using
embedded mobility control software and sensors. Historically, drones were used particularly in
navy for reconnaissance functions, but with latest developments in light-weight battery powered
drones, many civilian programs are emerging. One of the most essential programs is to enhance
the insurance of the cellular communications networks [9]
Quadcopters, as opposed to fixed-wing aerial vehicles, are categorized as a type of unmanned
aerial vehicle (UAVs) and have been in lots of packages, inclusive of environmental monitoring,
communique, delivery carrier, and many others due to their more maneuverability, hovering
functionality, and low cost [1], [2].
8
While Quadcopters have their obvious advantages in a wide area of applications, present
quadcopter platforms are subject to limited flight duration. A small to medium sized quadcopter,
e.g., DJI Phantom 4 PRO, can barely achieve 30 minutes flight time [3]. Although new
techniques for autonomous battery swapping has been advanced to resume the UAV flight, it
nevertheless has flight duration barriers and significantly reduces the mobility of UAVs due to
common recalls of battery swapping [4]. In this work, we investigate the feasibility of integrating
renewable energy harvesting capabilities skills right into a quadcopter to permit for lengthy
persistence missions with payload necessities
If base stations might be miniaturized to fit within the drone payload, they could be flown to any
difficult-to-attain areas to offer coverage to particularly congested areas or where infrastructure
has been destroyed through natural disasters (e.g. Floods) or wherein it's far difficult or costly to
install conventional terrestrial towers. Such drone-mounted flying base stations, can also be used
to provide substitute coverage in disaster areas or augment coverage and capacity in temporary
or unexpected high demand situations. In fact, given the rising site rental charges for the growing
number of small mobile deployments, drone-installed flying base stations may be an appealing
opportunity to conventional roof or pole mounted base stations.
Next generation cellular networks have excessive reliability and availability demands [10].
Situations like natural disasters, intense densities of customers in a place, or presenting
connectivity in rural regions, the mobile network needs to fulfill certain quality of service
necessities. However, these situations are either unexpected or temporary. As a result, it is not
viable to invest in an infrastructure that will provide revenue for a relatively short time. A viable
strategy to those problems may be helping the terrestrial cellular network through low-altitude
unmanned aerial vehicles (UAV) that may provide mobile cells of any size, and provide a quick
deployment possibility. However, one in all the biggest challenges is to optimize or amplify the
flight duration of the UAV mounted base station so that the network can gain the maximum [11].
Similar research had been carried out to attempt to hold drones aloft for an extended period thru
using hydrogen gasoline-cells. A British company Intelligent Energy announced a hydrogen-
gasoline-mobile electricity extender mainly designed for drones [12]. The corporation unveiled
the prototype and indicated that the extender will provide twice the cutting-edge flight time for
drones rather than the standard range of 15-30 minutes for plenty UAV models.
A comparable study to increase flight time of drones turned into the use of many drones that will
be launched alternately as the other machines go back home for recharging their batteries.
However the network handover process proved to be very complicated. In addition research
continues to be underway to set up whether or not it'll be efficient and low-cost for the drones to
handover the mobile network hardware device or each drone carries its very own hardware on
deployment.
Attempts to extend an aerial automobileβs operational time have, in large part, focused on the
selection of efficient components [5], [6], energy optimized device design, and using energy-
efficient flight path plans [7]. For instance, Verbeke et al. confirmed a modified configuration for
narrow corridors, which leads to doubtlessly 60% increased patience [8]. More recently, Pang et
9
al. incorporated variable pitch rotors into a gasoline-engine to increase flight duration
approaching 2 to 3 hours [9]. Other researchers proposed using hybrid energy sources, consisting
of integrating rotational electricity harvesting, laser power beaming, and solar energy, to increase
flight periods [10]β[12].
Although the concept of UAV mounted base station is still in its infancy, the research hobby on
this destiny generation is developing rapidly. Many academic researchers are actually actively
working in the area [12], [13], while enterprise players also are beginning to sign up for the game
Nokia has lately developed an ultra-miniaturized 4G base station weighing most effective 2kg,
which was effectively hooked up on a commercial quad-copter to offer coverage over a remote
place in Scotland [14]. This successful demonstration proves that the underlying hardware era for
UAV established base station has matured. Recent studies [15], [16] on UAV established base
station specifically targeted on locating the optimum region for the drones to float or hover so
that the coverage is maximized. This study work makes a specialty of optimizing the flight
endurance power of the UAV hooked up base station in order that the network is energetic until
normal service is restored.
In [17], Y Zeng and R Zhang have a look at the energy-efficient designs for UAV
communication, where a UAV is employed to communicate with a ground terminal for a finite
time horizon. Their objective changed into maximizing the energy performance in bits/Joule
through optimizing the UAVβs trajectory, which is a brand new design framework that desires to
jointly take into account the communication throughput and the UAVβs propulsion energy
consumption. Intuitively, from the throughput maximization attitude, the UAV must stay
stationery at the nearest viable area from the ground terminal that allows you to hold the first-
class channel condition and maintain a clean line-of-sight for dependable communication.
The authors in [18] table a Drone Mounted Base Stations (DMBSs) that could provide wireless
insurance at the ground. In their letter, they propose an energy efficient placement algorithm for
a DMBS that serves a set of ground users, with the use of minimal required transmit power. They
additionally recommend an optimum DMBS placement approach to serve a fixed number of
ground users, the usage of minimal required transmit power will increase the flight time for the
drone mounted base station.
On the other hand, authors in [19] argue that future cellular networks count on ultra-dense
deployments of mostly static base stations (BSs), represented typically by small cells, to meet
future communication demands by a soaring number and diversity of user gadgets. Thus, they
introduce a framework for self-organizing flying radio access network with ultra-low altitude
soaring BSs which are automatically located in real-time in keeping with the usersβ necessities
and mobility.
In this paper [19], the authors provided a framework and an architecture of destiny radio access
community more desirable with the flying BSs serving moving users. The proposed concept
allows network optimization in real-time primarily based on the usersβ throughput requirements
and mobility. Furthermore, because of proximity between the flying BS and the UEs, the
proposed idea enables big exploitation of higher frequency bands for communication. Their
10
simulated results show that the flying BS introduces a significant gain in channel quality for
users moving in crowd and has a potential to replace many static BSs in terms of throughput.
They have shown that more than 10 BSs deployed along the street in the scenario with inter-site
distance of 45 meters can be replaced with a single fling BS while throughput is kept the same.
Moreover, the power performance on the UE aspect may be accelerated via extra than five times,
if the static BSs deployment is substituted with the aid of the flying BS. This proves excessive
ability of the flying BSs for integration and implementation in future cell networks as opposed to
wide ultra-dense deployment of small cells. All the aforementioned recent works assume static
scenarios (i.e. fixed position of users), where the flying BSs can be beneficial from Capital
Expenditure (CAPEX) and/or Operational Expenditure (OPEX) reduction on the operatorβs point
of view in some specific cases [19].
2.3 ENERGY HARVESTING TECHNIQUES
Energy harvesting is an appealing era for UAV mounted base stations because it offers the
capacity to boom flight duration or persistence without adding huge weight (payload) to the
system. This generation has become a topic of interest for plenty researchers mainly for
applications in electrically powered UAVs, in this situation the Quadcopter that is getting used to
carry the cellular (cell) base station. Scavenging electricity from ambient assets has seen to be
nice because this energy is in abundance and can be inexhaustible mainly solar energy from the
sun.
Electronic gadgets are broadly speaking powered externally via batteries. The dependency on the
recharging manner limits the usage of those gadgets to work in specified time frame. This study
highlights the functionality of piezoelectric and solar energy harvesting strategies to generate
sufficient power to power up drone mounted cellular base station electronic circuits, without
relying on the droneβs principal power system (in the form of batteries).
Some of the greatest troubles for Quadcopters are energy-related, as the complete gadget may
additionally close down if energy resources are depleted [6]. High power consuming
automobiles, flight structures, onboard computer systems and external equipment are all powered
by way of the main battery and consequently flight times and range of operation are restrained. It
is vital not to forget that the physical layout, inclusive of weight and any action taken by way of
the Quadcopter will increase strength consumption as said through Siegfried et al. In [7]. It is
therefore vital to ensure that each the physical design of the quadcopter and any maneuvers taken
by the quadcopter are energy efficient as shown in [8]. A requirement for an independent
unmanned aerial vehicle (UAV) is the replenishment of its power supply as said through Paulo
Kemper et al. [9].
Automated energy recharging structures should be advanced to fulfill the preference of
absolutely self-reliant structures. Such approaches are regularly overseen and efficiently lessen
the operating range of the system.
11
While it is unlikely that all ground based activities can be automated, the energy source is a
possible target for automation. One research angle would be to try to harvest energy from the
environment as stated by [10]: βThe replenishment of the on-board energy resources must not be
performed manually; ideally the rotorcraft harvest energy from the environment.β Extracting
energy from the environment is possible according to Gungor & Hancke [11] who suggests that
energy-harvesting techniques may even be better to use, as using batteries for the primary power
source for rotorcraft can be troublesome due to their limited lifetime.
As early as 1964 the Spencer Laboratory had built a helicopter that became powered only
through microwave energy as proven in [19]. At the same time it turned into a breakthrough in
the field of wireless power transmission. A microwave beam was directed with a focusing
antenna toward the helicopter and the rectifying antenna at the helicopter captures the beam and
converts it into DC energy this is used to run the motors. The setup is shown in Figure 2.0. While
it is a pretty efficient manner to transmit energy to as an example a rotorcraft, it has to stay on
the same point in the sky, which might make it good for surveillance of a small area however
isn't always relevant to different rotorcrafts.
12
Fig. 2.0 β Microwave powered helicopter setup [19].
2.3.1 Piezoelectric Energy Harvesting Technique
With technology advancements over the previous few many years, the sizeable reduction in size
and power consumption of electronic circuitry has caused a super research attempt towards
energy harvesting devices (EHDs) for the improvement of wireless sensors and ubiquitous
wireless networks of communication nodes [21-24]. Significant progress has been made and a
large number of vibration-primarily based EHDs have been proposed and tested through the use
13
of numerous mechanisms, inclusive of electromagnetic, electro-static and piezoelectric [21-25].
Piezoelectric EHDs have received special interest due to their self-contained power without
requiring an external voltage source, highest energy density, and proper dynamic responses, and
ability to scavenge energy in the range of 1-200ΞΌW/cm3 from ambient vibration energy sources
[24, 26].
Fig. 2.1 Shoe mounted energy harvester prototype [27].
Significant advancements had been made in this region of research of piezoelectric transducers
[27]. MIT Media Lab investigated the feasibility of implementing piezoelectric generation into
shoe mounted energy harvesters. The prototype is proven in diagram above. Energy is extracted
from this innovation by using forces exerted at the shoe while walking. Further, Starner (1996)
tested the feasible locations for power harvesting gadgets around the human body and seemed
carefully and thoroughly on the energy available from resources of mechanical energy inclusive
of blood pressure, walking, and higher limb movement of an individual. The writer claims 8.4
watts of useable power may be achieved from a Lead Zirconate Titanate Chemical compound
(PZT) mounted in a shoe [27].
In a later paper, Umeda et al (1997) researched the energy storage characteristics of a power
harvesting system comprising of a PZT, bridge rectifier and a capacitor. Their work mentioned
the effect of various parameters on the effectiveness of the storage circuit. Following their
logical examination a model created and expressed to have a proficiency of greater than 35%, in
excess of 3 times more prominent than a solar cell.
14
Kymissis et al (1998) looks at utilizing a Piezo-film in addition to a Thunder actuator, to charge a
capacitor and power a radio frequency identification (RFID) transmitter from the energy lost to
the shoe whilst walking.
The Polyvinylidene fluoride (PVDF) stave was located in the sole to soak up the bending energy
of the shoe, and the Piezo-ceramic thunder actuator became located inside the foot sole region to
harvest the impact energy. Their work proved that the power generated by means of the
piezoelectric gadgets became adequate for driving beneficial wireless gadgets and could transmit
a 12-bit signal five to six times every few seconds. Following the work aided by Kymssis et al
(1998), the exploration concerning wireless sensors began to develop, and in 1998, Kimura were
given a US Patent that targeted on the usage of a vibrating piezoelectric plate to generate
electricity adequate to run a little transmitter fixed to migratory birds for the purpose of
transmitting their identification code and location. The viability of the power harvesting machine
is also similar with present battery innovation.
Goldfarb et al (1999) exhibited a linearized version of a PZT stack and examined its proficiency
as an energy technology device. It was tested that the maximum intense productivity happens in
a low recurrence region, much lower than the structural resonance of the stack. It is also said that
the efficiency is likewise associated with the amplitude of the input force due to hysteresis of the
PZT
2.3.2 Solar Energy Harvesting Technique
Sunshine and daylight hours in Harare, Zimbabwe are estimated as follows. The longest day of
the year is 13.4 hours long and the shortest day is 10.55 hours long. The longest day is 2.85
hours longer than the shortest day. There is an average of 2871 hours of sunlight per year (from a
possible 4383) with an average of 7:51 of sunlight per day. It is sunny 65.5% of daylight hours.
The remaining 34.5% of daylight hours are likely cloudy or with shade, haze or low sun
intensity. At midday the sun is on average 69.7Β° above the horizon at Harare [20]. This leaves us
with plenty of sunshine (solar) energy source for our drone mounted base station.
The concept of using harvested energy to power an unmanned flying system is not new.
Actually, the first absolutely solar based flight occurred on November 4, 1974 while the Sunrise
1 unmanned flying machine flew over Camp Irwin, California, powered just with the aid of the
sun powered cells embedded on its wings. Since the Sunrise 1 flew in 1974, numerous other sun
powered air ships have flown in different skies around the arena. Over the past few decades,
advances in photovoltaic cell technology have given rise to lighter and thinner solar cells. With
those advances has come ongoing studies in the subject of solar powered aircraft, together with
the possibility of which include light weight solar modules in UAVs.
Conventional solar panels contain crystalline silicon, which is the energetic fabric in solar cells,
encapsulated into character cells which might be housed in a metal frame and protected with a
tumbler cover. These conventional solar panels are rigid and heavy. Today, thin film solar cell
innovation exists in which amorphous silicon may be painted or rolled onto a totally skinny
15
substrate to accomplish mild weight, bendy solar cells. A generally new thin film solar based
module is taken into consideration in this assignment and its execution attached with UAVs is
researched.
2.3.3 Solar Panels
There are essentially two technologies utilized to fabricate the majority of photovoltaic (PV)
cells, crystalline silicon and thin films as proven in [27]. The PV enterprise is unexpectedly
growing and it's far difficult to expect which technology can be the better one in the future. One
of the most promising new technology is organic or natural PV cells. Table 1 shows the
performance values of PV cell technologies in 2009 [28]. Table 1: Efficiency values of PV
Technologies [28]
Technology Efficiency
Crystalline Silicon Monocrystalline Silicon 16-23%
Polycrystalline or Multi-crystalline Silicon 15-20%
Thin Film Amorphous Silicon 5-12%
Cadmium Telluride 8-12%
CIGS 10-14%
Multi-junction 6-30%
Organic Standard Organic Cells ~5%
Nanostructured material cells 3-5%
Dye-sensitized ~5%
It is secure to mention that the technologies has advanced since 2009, but it can still be virtually
visible that the thin film cells are lagging behind, especially the amorphous silicon cells.
Lightweight thin flexible panels are being evolved at a fast rate nowadays, however nearly
exclusively with supposed use for camper vans and boats, excluding RC avionics. If there's strict
constraints in weight, light-weight flexible panels can be the most effective viable preference. At
the same time it additionally makes it tough to achieve the output needed to recharge the
batteries taking into account the small area on a quadcopter where it is possible to position solar
panels.
If there is strict constraints in weight, light-weight flexible panels can be the only viable choice.
At the same time it additionally makes it difficult to achieve the output needed to recharge the
batteries considering the small location on a quadcopter wherein it is possible to place solar
panels. To further show this point the fill factor of a thin film panel is calculated as [30],
πΉπΉ =ππππ₯πΌππ
ππππ₯πΌπ πβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..(1)
16
and has a power rating given by,
ππππ‘πππ =1
π΄πππππππππ₯πππ‘π‘ππππππππβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦(2)
In crystalline silicon cells the fill factor is normally above 0.7, or even among amorphous silicon
cells it is most effectively a moderately value. The power rating is likewise fairly low on this
context. At illumination angle displacements and partial shading the power output drops
exponentially so it is preferable if there is no partial shading and as low illumination angle
displacements as possible.
2.3.4 Maximum Power Point Tracking
As the current-voltage relationship of a PV module has a non-linear characteristic it is necessary
to track the maximum power point to achieve the maximum efficiency as shown by [31]. One
commonplace implementation of MPPT algorithm is by means of setting a DC-DC converter
between power supply and load/battery, and using a MPPT controller to manipulate the duty
cycle of the converter. By varying the duty cycle of converter, the ratio of input and output
voltage can be adjusted correctly. Basically the PV voltage is increased or decreased to locate the
maximum power point. Electric power generated by PV cells is obviously dependent on the
climate situations. Conventional MPPT strategies are not so powerful in realistic partial shade
conditions. Power loss induced by way of MPPT failure can be excessive as numerous tenths of
the total output underneath partial shade situations as said by Qi et al. In [32]. Many of the
proposed MPPT methods are customized for high-power systems. In [33], a description of a
MPPT approach for low-energy PV panels wherein the energy consumption of the MPPT control
circuit, in comparison to excessive-power applications, can make a contribution substantially to
the very low power performance. The approach is to set the usage of a MPPT controller that is
suitable for low-power PV panels by having low power consumption. As no current have to be
measured and no energy calculated the control circuit is less complicated and the energy intake is
decrease. As a result very excessive energy efficiency can be executed, each in the element of
tracking and average efficiency even for low-power sources as the current-voltage dating of a PV
module has a non-linear characteristic it is necessary to tune the most strength point to reap the
most efficiency as proven through [31]. Many of the proposed MPPT techniques are customized
for high-power systems.
There are a few conclusions to be crafted from the research accomplished in this region. The
research about MPPT for low-power PV panels is extra interesting as it has a direct bearing in
cases of limited power output. The choice of circuit components for the MPPT controller should
be carefully considered in terms of functions and power consumptions. As the step-down
converter usually used in MPPT controllers needs 2-3V higher input voltage than the battery
17
float voltage, the preferable total output voltage from the solar panels should be 5-6V higher than
the battery float voltage to give a safety margin.
2.4 Background
The improvement of small electrically powered drones has won superb interest in the research
area. Of precise concern and interest is the introduction of innovative strategies to increase the
flight duration or endurance of UAV mounted base station so that the drone is kept aloft for
some time to make sure it accomplishes its undertaking.
The idea of energy harvesting may be categorized into kinetic and non-kinetic techniques with
examples being vibration harvesting and photovoltaic energy harvesting respectively. Vibration
harvesting consisting of piezoelectric transducers are based at the belief that when a material is
caused to vibrate, itβll generate a voltage at its ends (Fig 2.2a) which can be tapped and directed
to the machineβs electricity elements bus. Conversely if an electric current is passed through a
certain material it will generate vibrations (Fig 2.2b). However our focus is on the former
concept.
.
18
Fig. 2.2 Piezoelectric energy harvesting concept [32]
The non-kinetic energy harvesting class includes Solar Energy Harvesting Techniques using
flexible thin-film photovoltaic solar panels (Fig 2.3) that convert ambient sunshine into electrical
energy which will also be directed to the UAV power supply bus to augment the main battery
power source. Flexible thin-film solar energy harvesters are allowing drones to be kept aloft for
additional hours than is possible with batteries alone. The panels are produced on thin plastic
sheets that can be stuck on top of the drone airframe. The basic drone on the market today can
stay aloft for several minutes but the addition of the solar panels will prolong or extend this time.
19
Fig. 2.3 Flexible Thin-film Photovoltaic Panel (30)
2.4.1 Vibration Energy Harvesting Technique
A piezoelectric transducer is a cantilever beam made from a layer of deposited piezoelectric
material which includes Lead Zirconate Titanate also known as PZT (unimorph and bimorph
while there exist one layer and two layers respectively). One end of a beam is made stationery
and the other free. A mass is then connected to the loose end of the beam [31]. The harvester
operates with the aid of making use of a mechanical pressure on a PZT device, therefore
inducing electric charge on the piezoelectric capacitance and voltage is inspired across the
terminals of the device [32]. The conversion of mechanical power into electric power depends on
the piezoelectric coupling coefficient, kij, and the capacitance of the material, cp. The subscripts
i and j in the coupling coefficient constitute the polarization of the fabric in 3- dimensional area
[33].
20
Fig. 2.4 Unimorph piezoelectric cantilever beam [31]
The piezoelectric transduction mechanism is employed which will harvest vibration power from
the surrounding. Kim et al in [34] posted a 1mm thick cymbal transducer made up of ten PZT
layers stacked below a metallic cymbal fashioned enclosure used to reap vibrational strength as
proven in Figure 2.3. When subjected to rigorous vibration an out of 250V is recorded as output
voltage from its terminals. A DC-DC greenback converter is used to step down the 250V, and
with the aid of matching the impedance of the buck converter to that of the transducer a
maximum of 25V was received. The output voltage was used to power eighty-four LED (Light
Emitting Diode) organized in a mixture of series and parallel. A general power consumption of
53mW was recorded from the LEDs. This work did not enforce a means of storage as an
auxiliary power supply in the event the harvester goes off.
Fig. 2.5: A cymbal-shaped piezoelectric transducer reproduced from [31].
21
2.4.2 Solar Energy Harvesting Technology
2.4.2.1 Solar Energy
Sunlight is a tremendous renewable energy source. Thus, the usage of solar energy for programs
such as electricity generation, powering of automobiles, powering of cell base stations is turning
out to be common. The area of power generation using solar electricity is done through the use of
photovoltaic generation [36].
The solar photovoltaic cell operates primarily based on the percepts of conversion of sunlight
energy into electricity. In order to generate electricity in huge amounts, an array of solar PV cells
are connected in parallel, series or a mixture thereof. Irradiance is a degree of the sunβs power
available at the earthβs surface and it averages about 1000 watts per square meter. With common
crystalline solar cell efficiencies of around 14-16%, we are able to assume generation of about
140-160W per square meter if solar cells are exposed to complete sunshine.
Insolation is a degree of the available energy from the sunβs irradiation and is expressed in terms
of total complete sun hours (i.e. 4 full sun hours = 4 hours of sunlight at an irradiance level of
1000 watts per square meter). Obviously different parts of the world receive more sunshine than
others and will have more full sun hours per day than others. The solar insolation zone map
(below) will give you a general idea of the full sun hours per day for your location. This solar
insolation map shows the amount of solar energy in hours (peak sun hours), received each day on
an optimally tilted surface during the worst month of the year [37]. The solar insolation map is
shown in Fig.2.6. From the map it can be seen that tropical countries like Zimbabwe can benefit
from the use of solar energy as a viable source of energy.
Fig.2.6 World Solar Insolation Map [37]
22
CHAPTER 3
METHODOLOGY
3.1 Introduction
The focus of our research is to develop an energy harvesting model for the Quadcopter
established base station, in which the principle design is to model a solar energy harvester with a
view to harness energy from the drone sorroundings and turn it into usable electrical energy for
the UAV mounted base stationβs electrical circuits as well as to recharge the onboard battery in
the course of the flight. In addition a model to harness the vibration energy of the UAVβs
sorrroundings and turn that wasted energy into electricity to power micro circuits will also be
realised in simulations. With growing circuit demands for a power supply that is smaller and
light weight which can offer improved performance capabilities, micro scale piezoelectric and
flexible thin-film photovoltaic solar panel energy harvesters offer a solution.
Fig.3.1 Proposed Solar Quadcopter Architecture
SOLAR PV MODULE
MPPT
ALGORITHM
M
POWER BUS
MOTOR
CONTROLLER POWER CONVERTER
MATTERY
MANAGEMENT SYSTEM
BATTERY PAYLOAD ELECTRIC
MOTOR
23
3.2 Electric Quadcopter Adapted to Photovoltaic Energy
Architecture above is an Electric UAV tailored to photovoltaic energy to improve its endurance
due to increase of available energy on-board, complementing battery power. The power required
by the entire system is determined by the following subsequent computations. Diagram
underneath shows the energy metrics of a Quadcopter and it reveals the energy flow from the
two sources namely the on-board battery and solar panel(s) mounted on the Quadcopter fuselage.
Fig. 3.2 Quadcopter Energy Flow
3.3 Energy Metrics of a Quadcopter
To determine the propulsion net power required by the rotorcraft, we need to compute the power
required by the system and the weight considerations as well as the power obtained from the
ambient energy sources [27].
Power required by the Quadcopter (Watts):
ππππππ’ππ πππ = ππ =1
2ππ2ππΆπ· β π
=1
2ππ3ππΆπ·β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦(3)
π = πΏ =1
2ππ2ππΆπΏβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦(4)
It follows that, ππΏ =2π
ππ2πβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦......(5)
BATTERY POWER
PHOTOVOLTAIC AND
PIEZOELECTRIC
POWER SOURCE
ELECTRIC UAV NET PROPULSION
POWER
24
From this the power required for steady flight can be rewritten to:
ππππππ’ππ πππ =1
2ππ3π(πΆπ·0 + 1/(ππ΄π π(
2π
ππ2π)))
ππππππ’ππ πππ =1
2ππ2ππΆπ0 +
2π2
ππ΄π ππππβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..(6)
π is air density,
V is the steady flight airspeed,
S is the fuselage surface area to be covered by solar panels,
πΆπ0 is the parasitic drag coefficient,
AR is the aspect ratio,
e is the Oswald coefficient and
W is the Electrical UAV total weight.
Net propulsion power is therefore given by,
ππππ‘ = ππππππ’ππ πππ
1
Ι³πππππππππΙ³πππ‘ππΙ³ππππ‘ππππππβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.(7)
3.4 Solar Irradiance
3.4.1 Angstrom Model
From a number of sunshine hours, it is possible to predict Global Solar radiation for a specific
area through various models and Angstrom equations are mostly used and are very popular and
are given by the following equation [16]:
π» = π»0[π + π (
ππ
π)] = π»0ππ‘β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦...(8)
Where,
H=monthly average daily global radiation on the horizontal surface
π»0=monthly average daily extra-terrestrial radiation
ππ =monthly average daily number of hours of bright sunshine
N=monthly average daily number of hours of possible sunshine (daylight between sunrise and
sunset), βaβ and βbβ =regression constants
25
π»0 =24
π πΌπ π[1 + 0,33 πππ (
360π
365)][πππ π πππ πΏ π ππππ + (
ππ
180)π ππππ πππΏ] β¦β¦β¦β¦β¦β¦β¦β¦β¦(9)
Where,
πΌπ π = solar constant = 1367W/π2
ππ = hour angle of sunset or sunrisefor the typical day n of each month
π = day of the year
Ο = latitude angle of the month (degree)
πΏ is declination angle of the month (degree)which varies from 23.450 to 23.450 in the course of
the year. it is considered positive when the sun is in the northern latitude and negative when
in the southern latitude,
πΏ = 23.45 sin [πβ80
370β 360]β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.β¦..(10)
ππ = πππ β1(βπ‘ππππ‘πππΏ)β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..β¦β¦β¦...β¦.(11)
π = (2
5) πππ β1(βπ‘ππππ‘πππΏ) =
2ππ
15β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦....β¦β¦β¦β¦β¦...β¦(12)
3.5 Solar PV System Block Diagram
Fig. 3.6 Overview of system used for simulation. The red lines indicate power flow and
blue lines indicates signals. The DC-DC converter boost the output from the PV module.
26
3.6 Modeling of Photovoltaic (PV) Module
Fig.3.8 MATLAB/Simulink Photovoltaic (PV) Module showing output voltage (V)
The goal is to present the effects of irradiance and temperature on the parameters of the PV
module. The PV module is realized by a constant current source, Iph in parallel with a diode, D, a
shunt resistance, Rp and a series resistance, Rs.
Fig.3.7 Practical Equivalent Circuit PV Model
Figure above shows the practical equivalent circuit of the PV module which consists of several
PV cells. It includes a current source generating photo current which depends on the irradiation,
27
a big diode equivalent to the p-n transition area of the solar cell, the voltage losses represented by
series resistance and parallel resistance indicating the leakage current. The output current and
voltage relationship for PV module can be expressed by the following equations. By using
Kirchhoffβs Current theorem,
πΌ = πΌπβ β πΌπ β πΌπβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦(13)
Where Ip is the current leak in parallel resistor, Iph is the photocurrent and Id is the diode current
which is proportional to the saturation current and is given by the equation;
πΌπ = πΌ0[exp (π
π΄ππ ππ‘) β 1β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.(14)
Where V is the voltage imposed on the diode.
ππ = πππ
πβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.(15)
I0 is the reverse saturation or leakage current of the diode (A),
VT = 26mV at 300K for silicon cell, Tc is the actual cell temperature (K), k is the Boltzmann
constant = 1.38 x 10-23 J/K, q is the electron charge = 1.602 x 10-19 C. Ns is the number of PV
cells connected in series, A is the ideality factor which depends on cell design technology.
Thermal voltage βπβ is given by
π =ππ π΄πππ
π
= ππ π΄ππβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦(16)
βπβ is called the modified ideality factor (A is the diode ideality factor).
Output current of a module containing Ns cells in series will be;
πΌ = πΌπβ β πΌ0 [exp (π+πΌπ π
π) β 1] β
π+πΌπ π
π πβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦(17)
Evaluating πΌπβ
The photocurrent of a PV system depends on both radiation (Irradiance) and Ambient
Temperature. The relationship between the variants is given by the equation:
πΌπβ =πΊ
πΊπππ(πΌπβ,πππ+ππ π.βπ)β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦(18)
G is irradiance (W/m2), πΊπππ is the Irradiance at STC=1000Wm-2,
βπ = ππ β ππ,πππ(Kelvin),
ππ π = short circuit current coefficient (A/K) provided by the manufacturer
28
πΌπβ,πππ = photocurrent (A) at STC
Fig.3.9 Simulink Block Implementation of Photocurrent πΌπβ
Evaluating πΌ0
πΌπ π,πππ = ππβ,πππ β πΌ0,πππ [exp (πΌπ π,πππ .π π
ππππ) β 1]
At open circuit conditions (I=0, V=Voc,ref)
0 = πΌπβ,πππ β πΌ0,πππ [exp (πππ
ππππ) β 1]
πΌππ,πππ = πΌπβ,πππ β πΌ0,πππ [exp((πππ,πππ + πΌππ,ππππ π )
ππππ) β 1]
πΌ0,πππ = πΌπ π,πππ exp (βπππ,πππ
π)
The reverse saturation current is defined by;
πΌ0 = π·ππ3 exp (β
πνπ
π΄π)
Then πΌ0 becomes,
πΌ0 = πΌπ π,πππexp (βπππ,πππ
π) (
ππ
ππ,πππ)
3
π exp [(πππ
π΄π) (
1
ππ,πππβ
1
ππ)]β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..(19)
29
π π = (πππ,πππ + πΌππ.ππππ π )/(πΌπ π,πππ β πΌπ π,πππ {exp[πππ.πππ+π π πΌππ,πππβπππ,πππ]
π} +
πΌπ π,πππ {exp (βπππ,πππ
π)} β (
ππππ₯,ππ₯
πππ,πππ)β¦β¦β¦β¦β¦.(20)
Fig. 3.10 Simulink Block Implementation of Reverse Saturation Current πΌ0
3.7 Photovoltaic Module Structure
Fig. 3.11 PV Module Simulink Block
This is the PV module/array model that contains all the other subsystems. The inputs are
Irradiance and Ambient Temperature and outputs are voltage and current. The MPPT algorithm
30
and converter are under the MPPT and Voltage Controller subsystem and all the equations
modelling the behavior of a PV module are under the PV Module subsystem.
Fig. 3.12 PV Module Implementation Blocks
This is the "PV Module" subsystem. The model is has a temperature controlled subsystem
Temperature Controlled and a subsystem "Single Diode Model Equation". The parameter
Number of Modules is used to set the module voltage.
Fig. 3.13 Temperature Controlled Block
This is the subsystem "Temperature Controlled". All the blocks here are dependent on the
temperature and the resulting calculations are fed into the single diode equation block. The block
31
named "Cell Temperature Approximation" calculates the cell temperature, which is then fed into
the other blocks.
Fig. 3.14 Implementation of MPPT and Voltage Controller
Implementation of the MPPT algorithm. There is a Matlab code (Appendix 1) inside the block
"MPPT Algorithm". This is the subsystem labelled "MPPT and Voltage Controller".
3.8 Battery Modeling
Fig.3.16 Basic Equivalent Circuit for a Battery
32
The equivalent circuit shows the internal parameters of the battery.
ππππ‘π‘ = ππ + πΌπ π β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..(21)
where ππ = π(πππΆ)
π π = π(πΌ, πππΆ, π)
The voltage source Vg represents the voltage at open circuit between the battery terminals. It is
due to energy stored into the battery through the electrochemical reactions. Rg represents the
resistance offered by battery to energy flow.
The State of Charge varies between 0 and 1 i.e. 0<SOC<1.
If πΆπ‘is the battery capacity, ππ the discharge efficiency, T(t) is the current through the battery,
Cnominal is rated capacity, Ct coeff, Acap and Bcap are model constraints, ΞT is the temperature
variation from the reference value of 25Β°, Inominal is the discharge current corresponding to
Cnominal rated capacity, n is the time in hours and Ξ±, Ξ² are the temperature coefficients;
πππΆ(π‘π) β‘1
πΆ(π‘π)β« Ι³π(π‘)
π‘π
ββπΌ(π‘)πΏπ‘β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..(22)
πΆ(π‘) = (πΆππππππππ πΆπ‘ ππππ)/(1 + π΄πππ (πΌ(π‘)
πΌπππππππ)
π΅πππ
(1 + πΌππ₯π(π‘) + π½ππ₯π(π‘)2)β¦β¦β¦β¦(23)
πΌπππππππ =πΆπππππππ
Ι³
SOC considering the voltage of the battery can be calculated as:
πππΆ(π‘) = πππΆ(π‘ β 1) + (1
πΆ) β« πΌ(π‘)
π‘
0
ππ‘
πππΆ(π‘) = πππΆ(π‘ β 1) + β«1
πΆπππ‘π‘
π‘
0ππ‘β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..(24)
Where,
SOC (t) is battery state of charge at time t (%)
SOC (t-1) is battery initial state-of-charge (%)
I charge or discharge current (A)
π‘ is time in hours and ,
πΆπππ‘π‘ is battery capacity (Ah)
The power balance can be expressed as:
ππππ‘π‘ = ππππ‘ β ππ ππππβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.(25)
33
ππππ‘π‘= Battery Required Power
π‘ =π π‘
ππ(
πΆ
π π‘)
π
Therefore,
π =πΆ
π π‘(
π π‘
π‘)
1
πβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.(26)
t is the time in hours, π is the discharge current (A), C is the battery capacity in Ampere-hour
(Ah), n is the Peukertβs exponent being temperature and battery type dependent and Rt is the
battery discharge time in hours.
But,
ππππ‘π‘ = ππππ‘ β ππ ππππ
ππππ‘π‘π = ππππ‘ β ππ ππππ,
hence,
π =ππππ‘βππ ππππ
ππππ‘π‘,
and,
π
π π‘(
π π‘
π‘)
1/π
=ππππ‘βππ ππππ
ππππ‘π‘,
Therefore,
π‘ = π π‘1βπ[ππππ‘π‘.πΆ
ππππ‘βππ ππππβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦..(27)
This represents the ENDURANCE and can be used to estimate endurance of a Battery-Powered
Electric Quadcopter adapted with Photovoltaic Cells.
34
3.9 Battery Model Implementation
Fig. 3.17 MATLAB/Simulink Battery Model Implementation
This is the implementation of the battery model. The input ("PV Power") is from the Simulink
model of the PV model. This is a way to reduce the runtime of the model.
Fig. 3.18 Averaging Algorithm Block Implementation
This is the subsystem under the block "Averaging Algorithm". There are two blocks containing
the same algorithm where the output of the first one is fed to the input of the next. This is how
the double averaging algorithm is implemented. The inputs are Power and steps, where steps
gives the length of the averaging window.
35
Fig. 3.19 Voltage Controlled Battery Module Implementation
This is an implementation of the charge level and power of the battery and it is the subsystem
"Voltage Controlled Battery Module".
Fig.3.20 Matlab/Simulink Battery Charging and Discharging Model
The power of the battery is dissipating through the resistor (representing the Quadcopter load)
and simultaneously it is being charged by a DC voltage source (representing a PV module). The
value of state of charge vary as the value of the DC voltage source is changed (i.e. variation in
solar irradiance).
36
If the DC voltage source is more than the nominal voltage of the battery then SOC will remain
the same i.e. at value 1. If the DC voltage is less than the nominal voltage of battery then SOC
will decrease.
Fig.3.21 Typical discharge pattern
Typical discharge characteristics of a generic battery system plotted with voltage vs. depth of
discharge. Figure taken from [8].
3.10 Maximum power point tracking
3.10.1 Introduction
It is economic to extract the maximum quantity of power feasible from photovoltaic arrays. At
any time, there is an intersecting point between voltage and current that will deliver maximum
power from the solar modules. To ensure that the system always operates at this voltage/current
level, a DC-DC converter controlled by maximum power point tracking (MPPT) algorithms is
inserted after the PV modules to ensure optimal operating conditions.
MPPT techniques can be direct and indirect methods [24]. Direct methods of obtaining the MPP
do not require any prior information of the system traits. The algorithms use measurements of
voltage and/or current and takes into consideration the variation of these state variables The
foremost disadvantage of these techniques are that they may be greater complex and that
unwanted errors can affect the tracker accuracy.
The following methods/algorithms are included under the "direct method" category: Perturb and
observe (P&O), incremental conductance, differentiation, feedback voltage (current), auto
oscillation, fuzzy logic and others [25]. The Perturb and Observe method is selected for this
work because of itβs easy of implementation.
37
3.10.2 Perturb and observe method
The Perturb and observe method of maximum power point tracking is the most broadly
employed algorithm because of the easy practical implementation [27]. The output voltage (Vpv)
is perturbed and the PV output power is then compared with power from the preceding
perturbation. If the power is higher, then the voltage is perturbed in the positive direction. If the
power was lower, then the voltage is perturbed in the negative direction. A flow diagram of the
method is shown in figure below.
NO YES
NO YES NO YES
Fig.3.22 Flow Diagram of the Perturb and Observe MPPT Algorithm
START
Measure Vpv(t) and Ipv(t)
Calculate
Ppv(t)=Vpv(t) Ipv(t)
Ppv(t)>
Ppv(t-1)
Vpv(t)>
Vpv(t-1)
Vpv(t)>
Vpv(t-1)
Vref=Vpv(t)
+ΞV Vref=Vpv(t)
+ΞV
Vref=Vpv(t)
-ΞV
Vref=Vpv(t)
-ΞV
38
Fig.3.23 MPPT Module Implementation
Fig. 3.24 Complete MPPT with Perturb and Observe Algorithm
39
Fig. 3.25 Modeling of Bi-Directional Converter Module
3.11 Modeling of Piezoelectric Module
Fig. 3.26 Mass-Spring-Damper model of piezoelectric harvester. [31]
3.11.0 Piezoelectric Energy Harvester
A common piezoelectric power harvester is a cantilever structure with one or two piezoelectric
layers and the generated power is due to the vibration of the host structure (in this situation the
Quadcopter frame). The energy harvester generates most electricity whilst the supply frequency
matches the natural frequency and ultimate load is connected.
40
A lumped parameter model for the analysis of a vibration energy harvester consisting of a
bimorph piezoelectric cantilever with end mass is analyzed. An expression for the electrically
induced damping coefficient due to electromechanical effect of the piezoelectric material has
been derived. Mathematical expressions for natural frequency, displacement of end mass and
generated voltage for parallel and series configurations are shown below.
3.11.1 Parallel connection of the piezoelectric layers
When the two piezoelectric layers are connected in parallel configuration, the equivalent
capacitance doubles, thus the following equations are obtained as [28],
β¦β¦β¦β¦β¦β¦β¦β¦(28)
Solving equations above for z (t) and v (t), the dynamic response can be obtained. If the input
base excitation has harmonic motion of angular frequency Ο, the motion of the end mass and the
output voltage are assumed to be harmonic of the form z(t) = Z ejΟt and v(t) = V ejΟt, where Z and
V are the peak amplitudes [28]
β¦β¦β¦.(29)
Once v (t) is calculated, the power supplied to the load is calculated as v (t)2 /R.
3.11.2. Series connection of the piezoelectric layers
When the piezoelectric layers are connected in series, the equivalent capacitance of the harvester
is divided by two, then the equations can be rewritten as [28],
41
β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦(30)
After solving the displacement and voltage equations for series connection they are respectively
obtained as [28],
β¦β¦β¦(31)
The mathematical model is simulated in MATLAB to obtain resonant frequency, displacement
of the end mass and generated power.
3.11.3 Natural Frequency of Energy Harvester
The fundamental natural frequency of the energy harvester is calculated using Rayleighβs method
[28] which requires expressions for maximum potential energy and maximum kinetic energy.
The deflection curve of the beam as a function of distance x from the base for a force F, applied
at the tip is given by [28]
β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦(32)
The maximum potential energy of the beam is written as
β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦(33)
42
The maximum kinetic energy of the energy harvester vibrating at frequency Οn is given by [28];
β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.(34)
where Ο is the mass per unit length of the beam, ΟnZ represents maximum velocity of the end
mass and Mt is its mass [28].
Equating equations (33) and (34), the natural frequency can be expressed as,
β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦.β¦.(35)
Fig.3.27 Integrated Modeling of a Piezoelectric with PZT Bender and Bridge Rectifier
43
Fig.3.28 Physical Locations of the Piezoelectric Transducers
Fig. 3.29 F450 Quadcopter [43]
TWO PIEZOELECTRIC PATCHES PLACED
HERE
CANTILEVERED
PIEZOELECTRIC BEAM
44
Fig. 3.30 Graphical Layout Showing Proposed Positions of Solar Panels on Quadcopter
3.11 Quadcopter Framework
3.11.1 Frame
The F-450 quadcopter frame is used as it is desirable for the propellers and payloads which must
be lifted by the quadcopter. Quadcopter requires a light as well as rigid frame to host a LIPO
battery, 4 BLDC motors, 4 ESCs, a controller and the Software Defined Radio Base Station.
Arms are made up of 5/8 hollow square aluminum bars and uses makes use of common nuts and
bolts to preserve the body collectively.
3.11.2 Motors
Brushless DC Motors, also known as electronically commutated motors (ECM). BLDC motor
are synchronous motors powered by DC electricity. Rated in KV, where it rotates 1000 rpm in
line with 1 volt supplied to it (if its rating is 1 KV). It offers several benefits over brushed DC
motors like more reliability, low noise, reduction in EM Interference (EMI), high torque per
watt. Motor choice will be based on the payload or in other words the overall weight of the
45
aircraft prototype. For instance, for a quadcopter weight of 1kg, each motor selected must be
capable of producing a thrust of 500g on the rotors to provide an overall thrust of 2kg so as to
generate the needed lift to take the aircraft airborne. Brushless fixed-magnet motors with the
magnets in the bell housings are a desirable choice for this application.
3.11.3 Electronic Speed Controllers (ESC)
Four 30A ESCs are utilized in proposed Quadcopter. They are used to control the voltage applied
to the BLDC motor as per the PWM signals it receives from Microcontroller digital pins. It
convert the PWM signal received from flight controller or radio receiver and then drives the
brushless motor by supplying the required electrical power. Thus ESC is an electric circuit that
control the speed and direction of electric motor by varying the magnetic forces created by the
windings and magnets within the motor. 30A ESC can handle a maximum current of 23A.
Fig.3.31 Electronic Speed Controllers (ESC) [43]
3.11.4 Flight Controller Board
Flight controller is the primary functioning body of our plane. It is a circuit board ready with
sensors that detect upon any variation in orientation of the craft. It can acquire unique
instructions sent by the operator to manipulate pace of vehicles so that quadcopter could be
stable in fly mode.
46
ESCs and Flight Controller board work collectively in the following manner:
ESCs receive command from microcontroller circuit board and further give command to
the motors for rotation.
FCB generates various instructions for ESC and motors according to the need of operator.
The complete device is managed through this controller board.
3.11.5 Transmitter and Receiver
Radio transmitter uses radio signals to remotely manipulate quadcopter. The instructions given
by transmitter are received by a radio receiver connected to the flight controller. The number of
channels in transmitter decide how many actions of aircraft can be controlled by operator.
Minimum of four channels are needed to manipulate a quadcopter (which includes pitch. Roll,
throttle, yaw).
3.11.6 Lithium Polymer 5000mAh 11.1v Battery
The battery provides the primary power source. In this section the power source of the UAV is
discussed. Lithium Polymer (Li-Po) rechargeable battery is selected for UAVβs primary power,
because it is having low weight and high voltage capacity compared to other types of batteries. It
is shown in Fig.3.32 below. Specifications of the battery selected are 11.1V, 3-cell Lithium
Polymer (Li-Po) rechargeable battery with 2200mAh. Li-Po (Lithium Polymer battery) is a
rechargeable battery of lithium ion technology. They provide higher specific energy and are
being used where weight is a vital element. It also provide high voltage and long run
time as they hold huge power in small package and have high discharge rates required to meet
the need of powering Quadcopters. A Li-Po cell has a standard voltage of 3.7V per cell. The
11.1V battery pack has three (3) cells in series. That is in a "3S" battery pack there are 3 cells in
Series and so on.
Fig. 3.32 Lithium Polymer (Li-Po) battery [43]
47
For the Quadcopter, the Li-Po battery is the best choice. The battery should be selected based on
maximum ampere rating of the motors selected. If the motor is, say 15A, then all motors will
draw 15A * 4 = 60A, so the battery should support more than 60A. Factors to consider when
selecting the battery are:
mAh rating: the higher the ampere-hour rating the longer the flight time.
Capacity (C): This gives the electric current discharge rate of the battery. It gives the
maximum current at which battery can be discharged at a particular time. For example: If
battery is 2200mAh it means it can continuously deliver to the load, 2.2A for 1 hour.
Fig.3.33 Comprehensive List if Quadcopter Kit Accessories
48
3.11.7 Solar Panel Design
Within the device of the solar-powered quadcopter, the two most important components that can
be optimized through component selection are the solar cells and the MPPT. Currently, there are
a wide range of cells that can be used such as mono crystalline silicon (15-20%), polycrystalline
silicon (13-16%), thin-film (7-13%), and amorphous silicon (6-8%) [20]. In terms of choosing a
solar cell for a quadcopter, the key aspect is efficiency. For this motive, this design opted to use
SunPower C60 solar cells. Each cell has the electrical properties mentioned in Table II under
Standard Testing Conditions (STC), which are a cell temperature of 25o C and an irradiance of
1000 W/m2 with an air mass 1.5 (AM1.5), where AM is a measure of the length of the
surrounding solar radiation encounters before reaching the surface. These conditions are defined
by ASTM G173 [21].
Table 2: Electrical properties of SunPower C60 cells [22]
Pmpp (WP) Efficiency (%) Vmpp (V) Impp (A) Voc (V) Isc (A)
3.34 21.8 0.574 5.83 0.682 6.24
To charge the onboard battery (22.2 V nominal), 44 pieces of SunPower C60 solar cells with
each one at the dimension of 61.2 x 61.2 cm are placed in series to create a single solar panel.
Due to the power requirements and goal of extending the flight duration, it was selected to
incorporate two sets of these panels for a total of 88 pieces of SunPower C60 solar cells, which
require an area of 0.375 m2 and provide 293.92W of power in STC.
Fig. 3.34 120W 12v Flexible Slim Solar Panel [43]
49
3.11.8 Flexible Slim Solar Panel Features
Constructed with exceptional high quality fabric and advanced Mono-crystalline Solar cells for
efficient energy harvest. Ideal for curved and choppy surfaces, boats, bus, vans, utilities, golf
carts, sewn to heavy duty material such as sun shades. Pre Drilled grommet holes aids in diverse
installation methods such as zip ties, Velcro, straps for non-permanent applications. Lightweight
and super thin allowing use of a strong adhesive or industrial thread for everlasting mounting
applications
It will be determined that energy harvesting and efficient aerodynamic layout of the UAVs for
functions of the UAV mounted base station can similarly prolong the flying time and permit
exploitation of the UAV mounted base station in scenarios requiring longer operational flight
time.
50
CHAPTER 4
ANALYSIS OF RESULTS
4.1 Ambient temperature
Figures below demonstrates the impact the ambient temperature has on the I-V and P-V curves.
There are two effects that are certainly proven in the figures. First, the open circuit voltage is
higher with lower temperature. This is given as an open circuit voltage coefficient (Kv). The
other impact is the slight reduction of the short circuit current with lower temperature that is
determined by the short circuit current coefficient (Ki).The most essential effect of the ambient
temperature is that the power output is affected to a larger extent. Power output decreases
considerably with increasing temperature. As may be visible in figure 4.13, the power is reduced
by approximately 5 Watts by a 10Β°C temperature increase. That is a pretty significant reduction
in power output for a module rated at 85 Watts at STC.
Fig.4.1 I-V and P-V curves with different radiation intensities
Figures shows the effect that different levels of solar irradiance have on the I-V and P-V curves.
The main effect at work here is the reduction of photo current (Iph) that is calculated by equation
in the previous section. The photo current varies linearly with irradiance level.
51
Fig.4.2 I-V and P-V curves with different ambient temperatures
Figures shows the effect that different ambient temperature values have on the I-V and P-V
curves. The main effect at work here is the reduction of photo current (Iph) that is calculated by
equation in Fig.3.5. The photo current varies linearly with irradiance level.
52
4.2 Irradiance
Fig. 4.3 Hourly Global Irradiance Model Output Graphs
53
4.3 Global Irradiance Matlab Model Output Graph
Fig.4.4 Peak Sun Hour (PSH) from 24 hour global solar radiation for Gweru
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 230
200
400
600
800
1000
1200
1400Peak Sun Hour = 6.88
Time (Hour)
Glo
bal S
ola
r R
adia
tion (
w/m
2)
PSH = 6.88
Solar Insolation
6.88 kWh/m2/Day
54
Fig.4.5 Peak Sun Hour (PSH) from 24 hour global solar radiation for Mazowe
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 230
200
400
600
800
1000
1200Peak Sun Hour = 4.32
Time (Hour)
Glo
bal S
ola
r R
adia
tion (
w/m
2)
PSH = 4.32
Solar Insolation
4.32 kWh/m2/Day
55
Fig. 4.6 Current-Voltage Output Waveform from a PV Module
Fig, 4.7 Power-Voltage Output Waveform from a PV Module
56
Fig. 4.8 Current-Voltage-SOC Output Waveform from a Battery Charging-
Discharging Module
57
CHAPTER 5
CONCLUSION AND FUTURE WORK
4.1 Conclusion
The use of solar and piezoelectric energy to increase the flight duration or endurance in Drone
Mounted Base Station operations continues to be in its infancy. Very little research or practical
application has been achieved thus far to enhance energy optimization for flying base station. We
have confirmed that this technology is necessary and sufficient for extending operational times
for our drone mounted base station.
4.2 Future Work
4.2.1 Developments in Solar Technology
A lot of research is ongoing, aimed towards developing new approaches to make solar power
increasingly competitive with traditional energy sources. The economic effectiveness of
photovoltaic electricity depends on the conversion efficiency and capital cost. Single crystalline
silicon isnβt the best material used to make photovoltaic cells. In an attempt to reduce
manufacturing cost, polycrystalline silicon is used, even though it has a lesser performance.
Second generation solar cell technology are known as thin-film solar cells. They are easy and
cheaper to provide, even though still much less efficient. The thin film solar cells may be made
from a variety of substances, including gallium arsenide, cadmium telluride, copper indium
diselenide and amorphous silicon.
A method for growing efficiency is to use two or more layers of different materials with distinct
energy band gaps. Depending on the substance, photons of varying energies are absorbed. So by
stacking higher band gap material on the surface to take in high-energy photons (while allowing
lower energy photons to be absorbed by the lower band gap material beneath), much higher
efficiencies can result. Such cells, referred to as multi-junction cells, can possess more than one
electric field [39]. Another promising field of solar energy improvement is the use of
concentrating photo-voltaic technology. Instead of simply collecting and converting a portion of
sunlight to electricity, concentrating photo-voltaic systems use optical equipment like mirrors,
lens, and so on, to focus higher amount of solar energy onto efficient solar cells. Research is
currently ongoing on the use of organic material and nano-particles (Quantum Dots) as materials
for solar cells.
58
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61
Appendix A
MATLAB SCRIPTS
A.1 Script for plotting global solar irradiation using meteorological data.
% This script computes the Peak Sun Hour from 24 hour global solar radiation
% data saved in .csv format.
% Data are prepare in 2 column format starting from row 7.
% Column 1 is date/time and column 2 is global solar radiation data in w/m^2
% The 24 hours data start from 0 hour to 23 hour for a given day.
% There are 24 data points for hourly sampled data or 1440 data points for
% per minute sampled data.
% Column 1 and row 1 to 4 are meteorology station information.
% Please refer to the sample Gweru2016.csv file on how the data was prepared.
function PeakSunHourPlotMain
figure('Resize','off','NumberTitle','off',...
'Name','Peak Sun Hour Plot');
title('Peak Sun Hour');
xlim([0 23]);
ylim([0 1000]);
xlabel('Time (Hour)');
ylabel('Global Solar Radiation (w/m^2)');
hLocation = uicontrol('Style', 'text',...
'HorizontalAlignment','left','BackgroundColor','w',...
'Position', [85 330 125 15]);
hDate = uicontrol('Style', 'text',...
'HorizontalAlignment','left','BackgroundColor','w',...
'Position', [85 315 125 15]);
hLat = uicontrol('Style', 'text',...
'HorizontalAlignment','left','BackgroundColor','w',...
'Position', [85 300 125 15]);
hLon = uicontrol('Style', 'text',...
'HorizontalAlignment','left','BackgroundColor','w',...
'Position', [85 285 125 15]);
hAlt = uicontrol('Style', 'text',...
'HorizontalAlignment','left','BackgroundColor','w',...
'Position', [85 270 125 15]);
uicontrol('Style', 'pushbutton', 'String', 'Open Data File',...
'Position', [85 350 100 25],...
'Callback', @Open);
function Open(hObj,event) %#ok<INUSD>
[filename, pathname] = uigetfile('*.csv');
62
if isequal(filename,0) % Handling Cancel button pressed
return;
end
[num,txt]=xlsread([pathname, filename]);
solar = num(:,1);
if length(solar)>24
ts=60; % Time sample per minute
else
ts=1; % Time sample per hour
end
PSH = (sum(solar)/ts)/1000; % Calculate Peak Sun Hours
cdf = cumsum(solar)/ts; % Calculate Cummulative Distribution
PSHFirst = find(cdf>=1000,1,'first'); % PSH First hour
PSHLast = PSHFirst+((floor(PSH)*ts)-1); % PSH Last hour
PSHt = zeros(length(solar),1);
PSHt(PSHFirst:PSHLast) = 1000; % Set PSH First to Last hour
PSHEndValue = (PSH - floor(PSH))*1000;
PSHt(PSHLast+1:PSHLast+ts) = PSHEndValue;
% Plot Peak Sun Hour and Solar Radiation
cla;
bar(PSHt,'FaceColor','y','EdgeColor','y');
hold on;
plot(solar,'Color','r');
title(['Peak Sun Hour = ',sprintf('%0.2f',PSH)]);
legend(['PSH = ',sprintf('%0.2f',PSH)],...
['Solar Insolation',sprintf('\n%0.2f',PSH),' kWh/m^2/Day']);
set(hLocation,'string',txt{1,1});
set(hDate,'string',txt{7,1});
set(hLat,'string',txt{2,1});
set(hLon,'string',txt{3,1});
set(hAlt,'string',txt{4,1});
xlim([1 length(solar)]);
set(gca,'XTick',1:ts:length(solar));
set(gca,'XTickLabel',{0:23});
xlabel('Time (Hour)');
ylabel('Global Solar Radiation (w/m^2)');
end
end
A.2 MATLAB Script to plot I-V and P-V Characteristics of a PV Module with different
Temperatures and Irradiance.
63
%% Solar electrical model based on Shockley diode equation
clear all
clc
Va=0:.01:22;
Suns=1;
% TaC=30;
TaC=25:10:65;
lva=length(Va);
%lsuns=length(Suns);
lT=length(TaC);
Ipv=zeros(size(Va));
% for s=1:1:lsuns
for s=1:1:lT
for i=1:1:lva
k=1.38e-23;
q=1.6e-19;
A=1.2;
Vg=1.12;
Ns=36;
T1=273+25;
Voc_T1=21.06/Ns;
Isc_T1=3.80;
T2=273+75;
Voc_T2=17.05/Ns;
Isc_T2=3.92;
TarK=273+TaC(s);
Tref=273+25;
Iph_T1=Isc_T1*Suns;
a=(Isc_T2-Isc_T1)/Isc_T1*1/(T2-T1);
Iph=Iph_T1*(1+a*(TarK-T1));
Vt_T1=k*T1/q;
Ir_T1=Isc_T1/(exp(Voc_T1/(A*Vt_T1))-1);
Ir_T2=Isc_T2/(exp(Voc_T2/(A*Vt_T1))-1);
b=Vg*q/(A*k);
Ir=Ir_T1*(TarK/T1).^(3/A).*exp(-b.*(1./TarK-1/T1));
X2v=Ir_T1/(A*Vt_T1)*exp(Voc_T1/(A*Vt_T1));
dVdI_Voc=-1.15/Ns/2;
Rs=-dVdI_Voc-1/X2v;
Vt_Ta=A*k*TarK/q;
Vc=Va(i)/Ns;
Ia=zeros(size(Vc));
for j=1:1:100
Ia=Ia-(Iph-Ia-Ir*(exp((Vc+Ia*Rs)/Vt_Ta)-1))./(-1-Ir*(exp((Vc+Ia*Rs)/Vt_Ta)-1).*Rs/Vt_Ta);
end
Ipv(s,i)=Ia;
64
Ppv(s,i)=Va(i)*Ia;
end
end
axes1 = axes('Parent',figure,'OuterPosition',[0 0.5 1 0.5]);
xlim(axes1,[0 23]);
ylim(axes1,[0 5]);
box(axes1,'on');
grid(axes1,'on');
hold(axes1,'all');
title('I-V charateristics at 25 C');
xlabel('V_p_v (V)');
ylabel('I_p_v (A)');
plot1 = plot(Va(1,:),Ipv(:,:),'Parent',axes1,'LineWidth',1.5);
set(plot1(1),'DisplayName','25C T');
set(plot1(2),'DisplayName','35C T');
set(plot1(3),'DisplayName','45C T');
set(plot1(4),'DisplayName','55C T');
set(plot1(5),'DisplayName','65C T');
axes2 = axes('OuterPosition',[0 0 1 0.5]);
xlim(axes2,[0 23]);
ylim(axes2,[0 70]);
box(axes2,'on');
grid(axes2,'on');
hold(axes2,'all');
title('P-V charateristics at 25 C');
xlabel('V_p_v (V)');
ylabel('P_p_v (W)');
plot2 = plot(Va(1,:),Ppv(:,:),'Parent',axes2,'LineWidth',1.5);
set(plot2(1),'DisplayName','25C T');
set(plot2(2),'DisplayName','35C T');
set(plot2(3),'DisplayName','45C T');
set(plot2(4),'DisplayName','55C T');
set(plot2(5),'DisplayName','65C T');
legend1 = legend(axes2,'show');
set(legend1,...
'Position',[0.142649065260064 0.288888888888888 0.106317411402157
0.151937984496124]);
legend2 = legend(axes1,'show');
set(legend2,...
'Position',[0.140359086340159 0.603617571059427 0.101694915254237
0.151937984496124]);
65
A.3 MATLAB code inside the block "MPPT Algorithm".
function DutyCycle=MPPT(Ipv,VpvInn,Vdc,Vstep)
%#codegen
%% Algoritm for setting the voltage at Vmpp
persistent Pold Change Dold %Defines two persistent variables so that they
%can be stored between call to this function
if isempty(Pold) %Testing if the variable Pold has been defined
Pold=0;
end
if isempty(Change) %Testing if the variable Change has been defined
Change=1;
end
if isempty(Dold) %Testing if the variable Dold has been defined
Dold=0.7;
end
%% Setting voltage step size and change variable
DutyStep=Vstep/Vdc;
%% Calculate Power as a result of the previous iteration
P=VpvInn*Ipv;
%% Increase or decrease Voltage based on the conditions
if P>0
if abs(P-Pold)>0
if (P>Pold) %Test if new power is higher than old
if Change > 0
Change=1;
else
Change=-1;
end
else
if Change > 0
Change=-1;
else
Change=1;
end
end
DutyCycle=Dold+(Change*DutyStep);
else
DutyCycle=Dold;
end
else
DutyCycle=0.9; %In case the algoritm makes the power
end % negative, the voltage is set at a value
Pold=P;
Dold=DutyCycle;
end