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

i

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.

……………………………………………. ………………………………

Student Date

……………………………………………. ………………………………

Supervisor Date

……………………………………………. ………………………………

Co-Supervisor Date

ii

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.

iii

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

iv

Contents

Declaration………………………………………………….……………………… i

Abstract...................................................................................................................... ii

Acknowledgements.................................................................................................... iii

List of Figures............................................................................................................ v

List of Abbreviations…………………………………………………………..…... vi

Chapter 1. Introduction.............................................................................................. 1

1.1 Introduction………………………………............................................. 1

1.2 Problem Statement................................................................................... 2

1.3 Motivation …………………………………………………................... 3

1.4 Aims…………………………………………………………………... 4

1.5 Objectives……………............................................................................ 4

1.6 Justification............................................................................................. 6

1.7 Thesis Outline and Organization............................................................ 7

Chapter 2. Literature Review and Background.......................................................... 7

2.1 Introduction……….................................................................................. 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 ………………………… 13

2.3.3 Solar Panels……………….....……………………………….. 14

2.3.4 Maximum Power Point Tracking…………………………… 15

2.4 Background……………………………………………………………. 15

2.4.1 Vibration Energy Harvesting Technique………………… 17

2.4.2 Solar Energy Harvesting Technique ……………………… 19

2.4.2.1 Solar Energy………………………………………………. 19

Chapter 3. Methodology…………………………………………………………. 20

3.1 Introduction…………………………………………………….. 20

3.2 Electric Quadcopter Adapted to Photovoltaic Energy………… 21

3.3 Energy Metrics of a Quadcopter……………………………… 21

3.4 Solar Irradiance……………………………………………… 22

3.4.1 Angstrom Model……...................………………………… 22

3.5 Global Irradiance Matlab Model Output Graphs…………….. 24

3.6 Solar PV System Block Diagram…………………………….. 26

3.7 Ambient temperature…………………………………………. 32

3.8 Battery Modeling…………………………………………….. 33

3.9 Battery Model Implementation………………………………. 36

3.10 Maximum power point tracking……………………………. 39

3.10.1 Introduction………………………………………………. 39

v

3.10.2 Perturb and observe method……………………………….. 39 3.11 Quadcopter Framework ………………………………………… 42

3.11.1 Frame…………………………………………………………………………………….. 42 3.11.2 Motors……………………………………………………….. 42

3.11.3 Electronic Speed Controllers (ESC)…………………………. 43

3.11.4 Flight Controller Board……………………………………… 43

3.11.5 Transmitter and Receiver…………………………………….. 44

3.11.6 Lithium Polymer 5000mAh 11.1v Battery…………………… 44

3.11.7 Solar Panel Design……………………………………………. 45

3.11.7 Solar Panel Design……………………………………………. 46

Chapter 4. Conclusion and Future Work…………………………………………...… 47

4.1 Conclusion………………………………………………….….….. 47

4.2 Future Work………………………………………………...….….. 47

4.2.1 Developments in Solar Technology……………………………… 47

References………………………………………………………………………….……48

Appendix A…………………………………………………………………………..….51

vi

List of Figures

Fig.2.0 Microwave powered helicopter setup………………………………... 11

Fig. 2.1 Shoe mounted energy harvester prototype………………………….... 12

Fig. 2.2 Piezoelectric energy harvesting concept……………………………... 16

Fig. 2.3 Flexible Thin-film Photovoltaic Panel……………………………….. 17

Fig. 2.4 Unimorph piezoelectric cantilever beam…………………………….. 18

Fig. 2.5 A cymbal-shaped piezoelectric transducer…………………………… 18

Fig.2.6 World Solar Insolation Map…………………………………………. 19

Fig. 3.1 Proposed Solar Quadcopter Architecture…………………………..... 20

Fig. 3.2 Quadcopter Energy Flow…………………………………………….. 21

Fig. 3.3 Peak Sun Hour (PSH) from 24 hour global solar radiation for Gweru……. 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 Graphs………………………... 25

Fig. 3.6 Overview of system used for simulation………………………………… 26

Fig.3.7 Practical Equivalent Circuit PV Model………………………………….. 26

Fig.3.8 Simulink Block Implementation of Photocurrent 𝐼𝑝ℎ………………………….. 28

Fig. 3.9 Simulink Block Implementation of Reverse Saturation Current 𝐼0…….... 29

Fig. 4.0 PV module……………..…………………………………………… 30

Fig. 4.1 PV Module Implementation Blocks……………………………………… 30

Fig. 4.2 Temperature Controlled Block…………………………………………… 31

Fig. 4.3 Implementation of MPPT and Voltage Controller……………………….. 31

Fig.4.4 I-V and P-V curves with different radiation intensities…………………… 32

Fig.4.5 I-V and P-V curves with different ambient temperatures…………………. 33

Fig.4.6 Basic Equivalent Circuit for a Battery…………………………………...... 33

Fig. 4.7 Battery Model Implementation……………………………………………. 36

Fig. 4.8 Averaging Algorithm Block Implementation……………………………… 36

Fig. 4.9 Voltage Controlled Battery Module Implementation……………………… 37

Fig.4.10 Matlab/Simulink Battery Charging and Discharging Model………………. 37

vii

Fig. 4.11 Battery Charging-Discharging Characteristics………………… 38

Fig.4.12 Typical discharge pattern………………………………………. 39

Fig.4.12 Flow Diagram of the Perturb and Observe Algorithm…………. 40

Fig. 4.13 Graphical Layout of Solar Panels on Quadcopter…………………………. 41

Fig. 4.14 F450 Quadcopter……………………………………………….. 42

Fig.4.15 Electronic Speed Controllers (ESC)……………………………. 43

Fig 4.16 Lithium Polymer (Li-Po) battery……………………………….. 44

Fig. 4.17 120W 12v Flexible Slim Solar Panel…………………………… 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 bird’s 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 world’s 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 UAV’s 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 automobile’s 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]–[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 UAV’s trajectory, which is a brand new design framework that desires to

jointly take into account the communication throughput and the UAV’s 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 users’ 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 users’ 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 operator’s 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 drone’s 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]: “The replenishment of the on-board energy resources must not be

performed manually; ideally the rotorcraft harvest energy from the environment.” 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 – 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 1-200μW/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 69.7° 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],

𝐹𝐹 =𝑉𝑚𝑝𝑥𝐼𝑚𝑝

𝑉𝑜𝑐𝑥𝐼𝑠𝑐…………………………………………………..(1)

16

and has a power rating given by,

𝑃𝑟𝑎𝑡𝑖𝑛𝑔 =1

𝐴𝑟𝑒𝑎𝑝𝑎𝑛𝑒𝑙𝑥𝑊𝑎𝑡𝑡𝑎𝑔𝑒𝑝𝑎𝑛𝑒𝑙…………………………(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, it’ll generate a voltage at its ends (Fig 2.2a) which can be tapped and directed

to the machine’s 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 sun’s power

available at the earth’s 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 sun’s 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 station’s 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 UAV’s

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):

𝑃𝑝𝑟𝑜𝑝𝑢𝑙𝑠𝑖𝑜𝑛 = 𝑇𝑉 =1

2𝜌𝑉2𝑆𝐶𝐷 ∗ 𝑉

=1

2𝜌𝑉3𝑆𝐶𝐷……………………………………(3)

𝑊 = 𝐿 =1

2𝜌𝑉2𝑆𝐶𝐿………………………………(4)

It follows that, 𝑐𝐿 =2𝑊

𝜌𝑉2𝑆……………………………………………………………………......(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:

𝑃𝑝𝑟𝑜𝑝𝑢𝑙𝑠𝑖𝑜𝑛 =1

2𝜌𝑉3𝑆(𝐶𝐷0 + 1/(𝜋𝐴𝑅𝑒(

2𝑊

𝜌𝑉2𝑆)))

𝑃𝑝𝑟𝑜𝑝𝑢𝑙𝑠𝑖𝑜𝑛 =1

2𝜌𝑉2𝑆𝐶𝑑0 +

2𝑊2

𝜋𝐴𝑅𝑒𝜌𝑉𝑆………………………………………………………..(6)

𝜌 is air density,

V is the steady flight airspeed,

S is the fuselage surface area to be covered by solar panels,

𝐶𝑑0 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,

𝑃𝑛𝑒𝑡 = 𝑃𝑝𝑟𝑜𝑝𝑢𝑙𝑠𝑖𝑜𝑛

1

ɳ𝑝𝑟𝑜𝑝𝑒𝑙𝑙𝑒𝑟ɳ𝑚𝑜𝑡𝑜𝑟ɳ𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑙𝑒𝑟……………………………………………………….(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]:

𝐻 = 𝐻0[𝑎 + 𝑏 (

𝑛𝑠

𝑁)] = 𝐻0𝑘𝑡……………………………………………………………...(8)

Where,

H=monthly average daily global radiation on the horizontal surface

𝐻0=monthly average daily extra-terrestrial radiation

𝑛𝑠=monthly average daily number of hours of bright sunshine

N=monthly average daily number of hours of possible sunshine (daylight between sunrise and

sunset), ‘a’ and ‘b’ =regression constants

25

𝐻0 =24

𝜋 𝐼𝑠𝑐[1 + 0,33 𝑐𝑜𝑠(

360𝑛

365)][𝑐𝑜𝑠𝜑 𝑐𝑜𝑠𝛿 𝑠𝑖𝑛𝜔𝑠 + (

𝜋𝜔

180)𝑠𝑖𝑛𝜑𝑠𝑖𝑛𝛿] ………………………(9)

Where,

𝐼𝑠𝑐 = solar constant = 1367W/𝑚2

𝜔𝑠 = hour angle of sunset or sunrisefor the typical day n of each month

𝑛 = day of the year

φ = latitude angle of the month (degree)

𝛿 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,

𝛿 = 23.45 sin [𝑁−80

370∗ 360]……………………………………………………………….…..(10)

𝜔𝑠 = 𝑐𝑜𝑠−1(−𝑡𝑎𝑛𝜑𝑡𝑎𝑛𝛿)………………………………………………………..………...….(11)

𝑁 = (2

5) 𝑐𝑜𝑠−1(−𝑡𝑎𝑛𝜑𝑡𝑎𝑛𝛿) =

2𝜔𝑠

15………………………………………....……………...…(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

Kirchhoff’s Current theorem,

𝐼 = 𝐼𝑝ℎ − 𝐼𝑑 − 𝐼𝑝……………………………………………………(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;

𝐼𝑑 = 𝐼0[exp (𝑉

𝐴𝑁𝑠𝑉𝑡) − 1………………………………………………….(14)

Where V is the voltage imposed on the diode.

𝑉𝑇 = 𝑘𝑇𝑐

𝑞……………………………………………………….(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 ‘𝑎’ is given by

𝑎 =𝑁𝑠𝐴𝑘𝑇𝑐

𝑞

= 𝑁𝑠𝐴𝑉𝑇………………………………(16)

‘𝑎’ is called the modified ideality factor (A is the diode ideality factor).

Output current of a module containing Ns cells in series will be;

𝐼 = 𝐼𝑝ℎ − 𝐼0 [exp (𝑉+𝐼𝑅𝑠

𝑎) − 1] −

𝑉+𝐼𝑅𝑠

𝑅𝑝……………………………………(17)

Evaluating 𝐼𝑝ℎ

The photocurrent of a PV system depends on both radiation (Irradiance) and Ambient

Temperature. The relationship between the variants is given by the equation:

𝐼𝑝ℎ =𝐺

𝐺𝑟𝑒𝑓(𝐼𝑝ℎ,𝑟𝑒𝑓+𝜇𝑠𝑐.∆𝑇)………………………………………(18)

G is irradiance (W/m2), 𝐺𝑟𝑒𝑓 is the Irradiance at STC=1000Wm-2,

∆𝑇 = 𝑇𝑐 − 𝑇𝑐,𝑟𝑒𝑓(Kelvin),

𝜇𝑠𝑐 = short circuit current coefficient (A/K) provided by the manufacturer

28

𝐼𝑝ℎ,𝑟𝑒𝑓 = photocurrent (A) at STC

Fig.3.9 Simulink Block Implementation of Photocurrent 𝐼𝑝ℎ

Evaluating 𝐼0

𝐼𝑠𝑐,𝑟𝑒𝑓 = 𝑖𝑝ℎ,𝑟𝑒𝑓 − 𝐼0,𝑟𝑒𝑓 [exp (𝐼𝑠𝑐,𝑟𝑒𝑓 .𝑅𝑠

𝑎𝑟𝑒𝑓) − 1]

At open circuit conditions (I=0, V=Voc,ref)

0 = 𝐼𝑝ℎ,𝑟𝑒𝑓 − 𝐼0,𝑟𝑒𝑓 [exp (𝑉𝑜𝑐

𝑎𝑟𝑒𝑓) − 1]

𝐼𝑝𝑚,𝑟𝑒𝑓 = 𝐼𝑝ℎ,𝑟𝑒𝑓 − 𝐼0,𝑟𝑒𝑓 [exp((𝑉𝑝𝑚,𝑟𝑒𝑓 + 𝐼𝑝𝑚,𝑟𝑒𝑓𝑅𝑠)

𝑎𝑟𝑒𝑓) − 1]

𝐼0,𝑟𝑒𝑓 = 𝐼𝑠𝑐,𝑟𝑒𝑓 exp (−𝑉𝑜𝑐,𝑟𝑒𝑓

𝑎)

The reverse saturation current is defined by;

𝐼0 = 𝐷𝑇𝑐3 exp (−

𝑞휀𝑎

𝐴𝑘)

Then 𝐼0 becomes,

𝐼0 = 𝐼𝑠𝑐,𝑟𝑒𝑓exp (−𝑉𝑜𝑐,𝑟𝑒𝑓

𝑎) (

𝑇𝑐

𝑇𝑐,𝑟𝑒𝑓)

3

𝑋 exp [(𝑞𝜖𝑎

𝐴𝑘) (

1

𝑇𝑐,𝑟𝑒𝑓−

1

𝑇𝑐)]………………………………..(19)

29

𝑅𝑝 = (𝑉𝑚𝑝,𝑟𝑒𝑓 + 𝐼𝑚𝑝.𝑟𝑒𝑓𝑅𝑠)/(𝐼𝑠𝑐,𝑟𝑒𝑓 − 𝐼𝑠𝑐,𝑟𝑒𝑓 {exp[𝑉𝑚𝑝.𝑟𝑒𝑓+𝑅𝑠𝐼𝑚𝑝,𝑟𝑒𝑓−𝑉𝑜𝑐,𝑟𝑒𝑓]

𝑎} +

𝐼𝑠𝑐,𝑟𝑒𝑓 {exp (−𝑉𝑜𝑐,𝑟𝑒𝑓

𝑎)} − (

𝑃𝑚𝑎𝑥,𝑒𝑥

𝑉𝑚𝑝,𝑟𝑒𝑓)…………….(20)

Fig. 3.10 Simulink Block Implementation of Reverse Saturation Current 𝐼0

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.

𝑉𝑏𝑎𝑡𝑡 = 𝑉𝑔 + 𝐼𝑅𝑠………………………………………………..(21)

where 𝑉𝑔 = 𝑓(𝑆𝑂𝐶)

𝑅𝑠 = 𝑓(𝐼, 𝑆𝑂𝐶, 𝑇)

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 𝐶𝑡is the battery capacity, 𝜂𝑐 the discharge efficiency, T(t) is the current through the battery,

Cnominal is rated capacity, Ct coeff, Acap and Bcap are model constraints, ΔT is the temperature

variation from the reference value of 25°, Inominal is the discharge current corresponding to

Cnominal rated capacity, n is the time in hours and α, β are the temperature coefficients;

𝑆𝑂𝐶(𝑡𝑖) ≡1

𝐶(𝑡𝑖)∫ ɳ𝑐(𝑡)

𝑡𝑖

−∞𝐼(𝑡)𝛿𝑡…………………………………..(22)

𝐶(𝑡) = (𝐶𝑛𝑜𝑟𝑚𝑖𝑛𝑎𝑙 𝐶𝑡 𝑐𝑜𝑒𝑓)/(1 + 𝐴𝑐𝑎𝑝 (𝐼(𝑡)

𝐼𝑛𝑜𝑚𝑖𝑛𝑎𝑙)

𝐵𝑐𝑎𝑝

(1 + 𝛼𝑐𝛥𝑇(𝑡) + 𝛽𝑐𝛥𝑇(𝑡)2)…………(23)

𝐼𝑛𝑜𝑚𝑖𝑛𝑎𝑙 =𝐶𝑛𝑜𝑚𝑖𝑛𝑎𝑙

ɳ

SOC considering the voltage of the battery can be calculated as:

𝑆𝑂𝐶(𝑡) = 𝑆𝑂𝐶(𝑡 − 1) + (1

𝐶) ∫ 𝐼(𝑡)

𝑡

0

𝑑𝑡

𝑆𝑂𝐶(𝑡) = 𝑆𝑂𝐶(𝑡 − 1) + ∫1

𝐶𝑏𝑎𝑡𝑡

𝑡

0𝑑𝑡……………………………..(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)

𝑡 is time in hours and ,

𝐶𝑏𝑎𝑡𝑡 is battery capacity (Ah)

The power balance can be expressed as:

𝑃𝑏𝑎𝑡𝑡 = 𝑃𝑛𝑒𝑡 − 𝑃𝑠𝑜𝑙𝑎𝑟……………………………….(25)

33

𝑃𝑏𝑎𝑡𝑡= Battery Required Power

𝑡 =𝑅𝑡

𝑖𝑛(

𝐶

𝑅𝑡)

𝑛

Therefore,

𝑖 =𝐶

𝑅𝑡(

𝑅𝑡

𝑡)

1

𝑛………………………………………….(26)

t is the time in hours, 𝑖 is the discharge current (A), C is the battery capacity in Ampere-hour

(Ah), n is the Peukert’s exponent being temperature and battery type dependent and Rt is the

battery discharge time in hours.

But,

𝑃𝑏𝑎𝑡𝑡 = 𝑃𝑛𝑒𝑡 − 𝑃𝑠𝑜𝑙𝑎𝑟

𝑉𝑏𝑎𝑡𝑡𝑖 = 𝑃𝑛𝑒𝑡 − 𝑃𝑠𝑜𝑙𝑎𝑟,

hence,

𝑖 =𝑃𝑛𝑒𝑡−𝑃𝑠𝑜𝑙𝑎𝑟

𝑉𝑏𝑎𝑡𝑡,

and,

𝑐

𝑅𝑡(

𝑅𝑡

𝑡)

1/𝑛

=𝑃𝑛𝑒𝑡−𝑃𝑠𝑜𝑙𝑎𝑟

𝑉𝑏𝑎𝑡𝑡,

Therefore,

𝑡 = 𝑅𝑡1−𝑛[𝑉𝑏𝑎𝑡𝑡.𝐶

𝑃𝑛𝑒𝑡−𝑃𝑠𝑜𝑙𝑎𝑟…………………………..(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 it’s 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)

+ΔV Vref=Vpv(t)

+ΔV

Vref=Vpv(t)

-ΔV

Vref=Vpv(t)

-ΔV

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],

……………………(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 ω, the motion of the end mass and the

output voltage are assumed to be harmonic of the form z(t) = Z ejωt and v(t) = V ejωt, where Z and

V are the peak amplitudes [28]

……….(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

…………………………(30)

After solving the displacement and voltage equations for series connection they are respectively

obtained as [28],

………(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 Rayleigh’s 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]

…………………………………………………(32)

The maximum potential energy of the beam is written as

……………………………………………………………(33)

42

The maximum kinetic energy of the energy harvester vibrating at frequency ωn is given by [28];

……………………………………………….(34)

where ρ is the mass per unit length of the beam, ωnZ 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,

………………………….….(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 UAV’s 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 10°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 isn’t 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

REFERENCES

[1] S. Chandrasekharan, et al, "Designing and Implementing Future Aerial Communication

Networks," IEEE Communications Magazine, May 2016.

[2] A. M. Hayajneh, S. A. R. Zaidi, D. C. McLernon, and M. Ghogho, “Drone empowered small

cellular disaster recovery networks for resilient smart cities,” in IEEE International Conference

on Sensing, Communication and Networking (SECON Workshops), Dec 2016, pp. 1–6.

[3] Y. Zeng, R. Zhang, T.J. Lim, "Wireless Communications with Unmanned Aerial Vehicles:

Opportunities and Challenges," IEEE Communications Magazine, May 2016.

[4] Lei Wang, Bo Hu, Shanzhi Chen, “Energy Efficient Placement of a Drone Base Station for

Minimum Required Transmit Power.”

[5] [Online]. Available: https://en.wikipedia.org/wiki/Emergency_communication_system

[6] J. Lyu, Y. Zeng, and R. Zhang, “Spectrum sharing and cyclical multiple access in UAV-aided

cellular offloading,” in Proc. IEEE GLOBECOM, Singapore, Dec. 2017, pp. 1–6.

[7] Y. Zeng and R. Zhang, “Energy-efficient UAV communication with trajectory optimization,”

IEEE Trans. Wireless Commun., vol. 16, no. 6, pp. 3747–3760, June 2017.

[8] H. Shakhatreh, A. Khreishah, and B. Ji, “Providing wireless coverage to high-rise buildings

using UAVs,” in IEEE International Conference on Communications (ICC), May 2017..

[9] Yong Zeng and Rui Zhang. “Energy-Efficient UAV Communication with Trajectory

Optimization.”

[10] V. Sharma, K. Srinivasan, H.-C. Chao, K.-L. Hua, and W.-H Cheng, “Intelligent

deployment of UAVs in 5G heterogeneous communication environment for improved coverage,”

Journal of Network and Computer Applications, vol. 85, pp. 94 – 105, 2017.

[11] D. Takaishi, H. Nishiyama, N. Kato, and R. Miura, “A dynamic trajectory control algorithm

for improving the probability of end-to-end link connection in unmanned aerial vehicle

networks,” in 2014 International Conference on Next-Generation Satellite Networking and

Communication Systems, May 2014, pp. 94–105.

[12] I. B. Times, “Nokia and EE trial mobile base stations floating on drones to revolutionize

rural 4G coverage,” url: http://www.ibtimes.co.uk/nokia-ee-trial-mobile-base-stations floating

drones-revolutionise-rural-4g-coverage-1575795, 2016.

[13] A. Al-Hourani, S. Kandeepan, and S. Lardner, “Optimal LAP altitude for maximum

coverage,” Wireless Communications Letters, IEEE, vol. 3, no. 6, pp. 569–572, Dec 2014.

[14] R. I. Bor-Yaliniz, A. El-Keyi, and H. Yanikomeroglu, “Efficient 3D placement of an aerial

base station in next generation cellular networks,” in 2016 IEEE International Conference on

Communications (ICC), May 2016, pp. 1–5 [12]. S. Roundy, P. K. Wright and J. Rabaey, “A

study of low level vibrations as a power source for wireless sensor nodes,” Comput. Commun,

vol. 26, pp. 1131-1144, 2003.

[15] Yong Zeng and Rui Zhang. “Energy-Efficient UAV Communication with Trajectory

Optimization.”

[16] Lei Wang, Bo Hu, Shanzhi Chen, “Energy Efficient Placement of a Drone Base Station for

Minimum Required Transmit Power.”

[17] Zdenek Becvar, Michal Vondra, Pavel Mach, Jan Plachy, Dpt. of Telecommunication

Engineering, FEE Czech Technical University in Prague, Czech Republic and David Gesbert,

Communication Systems Department EURECOM Sophia Antipolis, France, “Performance of

Mobile Networks with UAVs. Can Flying Base Stations Substitute Ultra-Dense Small Cells?”

59

[18]. P. D. Mitchson, E. M. Yeatman, G. K. Rao, A. S. Holmes, and T.C. Green, “Energy

harvesting from Human and machine motion for wireless electronic devcies,” Proceeding of the

IEEE, vol.96, No.9, pp. 1457-1486, September, 2008.

[19]. C. Kompis and S. Aliwell, “Energy harvesting technologies to enable romote and wireless

sensing,” Sensors and Instrumentation-Knowledge Transfer Network, June 2008.

[20]. S. Priya, “Advances in energy harvesting using low profile piezoelectric transducerds”,

Journal of Electroceramics, 19, 165-182, March 2007.

[21]. Spies, P., Pollak, M. and Rohmer, G., “Power management for energy harvesting

applications”, available on line:

http://www.iis.fraunhofer.de/Images/power_management_for_energy_harvesting_applications_t

cm278-91133.pdf (Accessed 13th Mar. 2009).

[22]. S. P. Beeby, M. J. Tudor and N. M. White, "Energy harvesting vibration sources for

microsystems applications," Measur. Sci. Technol., vol. 17, pp. 175, 2006.

[23]. Y. C. Shu and I. C. Lien, "Analysis of power output for piezoelectric energy harvesting

systems," Smart Mater Struct, vol. 15, pp. 1499, 2006.

[24] N. S. Shenck and J. A. Paradiso, “Energy Scavenging With Shoe-Mounted Piezoelectrics,”

IEEE Micro, vol. 21, no. 3, pp. 30–42, 2001.

[25] Anton, S. R., and Sodano, H. A., “A Review of Power Harvesting Using Piezoelectric

Materials (2003–2006),” Smart Materials and Structures, Vol. 16, No. 3, 2007, pp. R1–R21.

doi:10.1088/0964-1726/16/3/R01

[26] S. S. Rao and M. Sunar, “Analysis of Distributed Thermo piezoelectric Sensors And

Actuators In Advanced Intelligent Sensors,” AIAA J., vol. 31, no. 7, pp. 1280–1286, 1993.

[27] Cook-Chennault, K. A., Thambi, N., and Sastry, A. M., “Powering MEMS Portable

Devices: A Review of Non-Regenerative and Regenerative Power Supply Systems with Special

Emphasis on Piezoelectric Energy Harvesting Systems,” Smart Materials and Structures, Vol.17,

No. 4, 2008, Paper 043001. doi:10.1088/0964-1726/17/4/043001

[28] H. W. Kim, A. Batra, and S. Priya, “Energy Harvesting Using A Piezoelectric ‘Cymbal’

Transducer In Dynamic Environment,” Jpn. J. Appl. Phys., vol. 43, no. 9A, pp. 6178–6183,,

2003.

[29] S. Roundy and P. K. Wright, “A Piezoelectric Vibration Based Generator for Wireless

Electronics,” Smart Mater. Struct., vol. 13, pp. 1131–1142, 2004.

[30]. Wind and Solar Power Systems Design, Analysis, and Operation, Mukund R. Patel.

[31]. Red, ―Will Solar Panel Work at My Location―, retrieved from http://www.applied-

solar.info/solar-energy/will-solar-panels-work-at-my-location/

[32]. Marshall Brain, ―How solar yard light works‖, retrieved from

http://home.howstuffworks.com/solar-light2.htm.

[33]. Jessika Toothman and Scott Aldous, Developments in Solar Cell Technology‖, retrieved

from http://science.howstuffworks.com/environmental/energy/solar-cell8.htm.

[34] M. A. Green, K. Emery, Y. Hishikawa, W. Warta, and E. D. Dunlop, “Solar cell efficiency

tables (version 45),” Progress in photovoltaics: research and applications, vol. 23, no. 1, pp. 1–9,

2015.

[35] G. Committee et al., “Tables for reference solar spectral irradiances: Direct normal and

hemispherical on 37 tilted surface,” ASTM International, 2012.

[36] SunPower. (2017) C60 solar cell mono crystalline silicon. [Online]. Available:

http://us.sunpowercorp.com/

60

Goldfarb, M. and Jones, L.D., 1999, “On the Efficiency of Electric Power Generation with

Piezoelectric Ceramic,” ASME Journal of Dynamic Systems, Measurement, and Control,

Vol.121, September, pp. 566-571.

[37] S. Gupte, P. I. T. Mohandas, and J. M. Conrad, “A survey of quadrotor unmanned aerial

vehicles,” in Southeastcon, 2012 Proceedings of IEEE, 2012, pp. 1–6.

[38] D. J. Pines and F. Bohorquez, “Challenges facing future micro-airvehicle development,”

Journal of aircraft, vol. 43, no. 2, pp. 290–305, 2006.

[39] N. Bezzo, K. Mohta, C. Nowzari, I. Lee, V. Kumar, and G. Pappas, “Online planning for

energy-efficient and disturbance-aware uav operations,” in Intelligent Robots and Systems

(IROS), 2016 IEEE/RSJ International Conference on. IEEE, 2016, pp. 5027–5033.

[40] N. K. Ure, G. Chowdhary, T. Toksoz, J. P. How, M. A. Vavrina, and J. Vian, “An

automated battery management system to enable persistent missions with multiple aerial

vehicles,” IEEE/ASME Transactions on Mechatronics, vol. 20, no. 1, pp. 275–286, 2015.

[41] S. Nebiker, A. Annen, M. Scherrer, and D. Oesch, “A light-weight multispectral sensor for

micro uav opportunities for very high resolution airborne remote sensing,” The international

archives of the photogrammetry, remote sensing and spatial information sciences, vol. 37, no. vi,

pp. 1193–1200, 2008.

[42] J. S. Gadda and R. D. Patil, “Quadcopter (UAVs) for border security with gui system,”

International Journal of Engineering Research and Technology, vol. 2, no. 12, pp. 620–624,

2013.

[43] (2016) Dji phantom 4 pro. [Online]. Available: http://www.dji.com/phantom-4-pro [44] J.

Verbeke, D. Hulens, H. Ramon, T. Goedeme, and J. De Schutter, “The design and construction

of a high endurance hexacopter suited for narrow corridors,” in International Conference on

Unmanned Aircraft Systems (ICUAS), 2014.

[45] T. Pang, K. Peng, F. Lin, and B. M. Chen, “Towards long-endurance flight: Design and

implementation of a variable-pitch gasoline-engine quadrotor,” in 12th IEEE International

Conference on Control and Automation (ICCA), 2016.

[46] R. Sowah, M. A. Acquah, A. R. Ofoli, G. A. Mills, and K. M. Koumadi, “Rotational energy

harvesting to prolong flight duration of quadcopters,” in Industry Applications Society Annual

Meeting. IEEE, 2015.

[47] M. C. Achtelik, J. Stumpf, D. Gurdan, and K.-M. Doth, “Design of a flexible high

performance quadcopter platform breaking the mav endurance record with laser power

beaming,” in International Conference on Intelligent Robots and Systems (IROS), 2011.

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