Analysis of Aircraft Power Systems, Including System Modeling and Energy Optimization, with Predictions of Future Aircraft Development

Home , Electric aircraft

Thesis

Presented in Partial Fulﬁllment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Richard Alexander, BSECE

Graduate Program in Electrical and Computer Engineering

The Ohio State University

2018

Thesis Committee: Dr. Jiankang Wang, Advisor Dr. Jin Wang

ABSTRACT

The aviation industry has been one of the biggest beneficiaries of the major rise in electric power system technology for the past several decades. With more electric aircraft (MEA) now defining the new industry standard, the new future vision is toward all-electric aircraft, wherein conventional power systems and propulsion sources are electrified. Achieving all-electric aircraft will require optimization of energy source usage onboard existing MEA. By first considering the problem under a simplified environment, such as a hybrid unmanned aerial vehicle (UAV) power system, energy optimization techniques can be explored before being considered for larger applications. This work first presents a method for system modeling and optimizing the usage of energy sources for a hybrid UAV system. The energy optimization is presented through a dynamic programming algorithm, which considers the system under only the fundamental constraints. The system modeling aspect is presented as a virtual UAV model, which considers all of the nonlinear constraints present upon the system. The virtual UAV model validates the results from the algorithm to ensure the anticipated operation is feasible under realistic system constraints. This work then uses the results from the hybrid UAV study, and extends it to a study of larger commercial aircraft with the focus being on the transition to all-electric aircraft. Power system architectures, voltage levels, power requirements, and load characteristics are examined on existing electric vehicle applications, and predictions are made of what

ii the future all-electric aircraft will look like. Additionally, the primary challenges of accomplishing “all-electric” status are discussed, including existing propulsion methods and battery chemistry. Finally, the paper discusses some recent advances toward all-electric aircraft, which further shows the bright future for the all-electric aircraft.

iii Dedicated to my brother, parents, grandparents, and everyone else

who has supported me on this journey

iv ACKNOWLEDGMENTS

I would like to thank my advisors for giving me the opportunity to work on the

Hybrid Unmanned Aerial Vehicle project over the last two years. In addition, their support and guidance over my graduate career has been invaluable, and I am truly thankful.

I also would like to thank my colleagues for their help and advice along the way, and for the countless hours spent in the labs working to meet our project objectives.

Additionally, there are many friends I would like to thank for helping make the college experience fun and memorable, and for their continued support through both undergrad and grad school at The Ohio State University.

Finally, I would like to thank my parents, grandparents and brother for believing in me, for their unfailing love and support, and for lending me their ear and for their emotional and moral support. I am truly grateful.

v VITA

2013 - 2017 ...... BSECE, The Ohio State University

2016 - present ...... Graduate Research Associate, Department of Electrical and Com- puter Engineering, The Ohio State University

PUBLICATIONS

Research Publications

D. Meyer, R. Alexander, J.K. Wang, ”A simple method for energy optimization to enhance durability of hybrid UAV power systems,” 2017 North American Power Symposium (NAPS), Morgantown, WV, 2017, pp. 1-6.

FIELDS OF STUDY

Major Field: Electrical and Computer Engineering

vi TABLE OF CONTENTS

Page

Abstract...... ii

Dedication...... iv

Acknowledgments...... v

Vita...... vi

ListofTables...... ix

ListofFigures ...... x

Chapters:

1. Introduction...... 1

1.1 System Modeling and Energy Optimization ...... 1 1.2 The Future All-Electric Aircraft ...... 5 1.3 Motivation ...... 8 1.4 OrganizationofThesis...... 9

2. Hybrid UAV System Modeling and Energy Optimization ...... 10

2.1 SystemDescription...... 10 2.2 Hybrid UAV Energy Optimization ...... 13 2.3 Hybrid UAV Simulation Model ...... 16 2.3.1 EngineandGenerator ...... 18 2.3.2 Battery Charging Circuit ...... 21 2.3.3 MotorandLoads...... 25 2.3.4 Hybrid Controller ...... 26 2.3.5 Measurements ...... 28

vii 2.4 Simulation Results ...... 29 2.5 Conclusion ...... 32

3. The Future All-Electric Aircraft ...... 33

3.1 TheAll-ElectricPowerSystem ...... 34 3.1.1 PowerSystemArchitectures ...... 36 3.1.2 PowerSystemCharacteristics ...... 43 3.2 Challenges of Becoming All-Electric ...... 46 3.2.1 Current Limitations ...... 46 3.2.2 LookingtotheFuture ...... 51 3.3 Conclusion ...... 52

4. ConclusionsandFutureWork ...... 54

4.1 Conclusion ...... 54 4.2 FutureWork ...... 56

Bibliography ...... 59

viii LIST OF TABLES

Table Page

3.1 CommonMEAPowerSystemVoltages ...... 44

3.2 EV Energy Requirements and Resulting Total Battery Weight . . . . 49

ix LIST OF FIGURES

Figure Page

1.1 Proposedmethodofenergyoptimization...... 4

2.1 Hybrid UAV system block diagram [1]...... 11

2.2 Sample hybrid UAV load proﬁle [1]...... 13

2.3 Full hybrid UAV Simulink model...... 17

2.4 HybridUAVfullenginemodel...... 18

2.5 Hybrid UAV governor and prime mover model...... 19

2.6 HybridUAVgeneratormodel...... 19

2.7 Hybrid UAV controllable voltage source model...... 20

2.8 Hybrid UAV full battery circuit model...... 21

2.9 Hybrid UAV battery charging controller model...... 22

2.10 Hybrid UAV battery discharging controller model...... 24

2.11 HybridUAVmotormodel...... 26

2.12 Hybrid UAV hybrid controller model...... 27

2.13 HybridUAVmeasurementsblock...... 28

2.14 Comparison of the DP and virtual UAV simulation results...... 30

x 3.1 Major sections of the all-electric power system. Adapted from [2]. . . 35

3.2 CentralizedEPDSforMEA.Adaptedfrom[3–5]...... 37

3.3 Semi-Distributed EPDS for MEA. Adapted from [3–5]...... 38

3.4 Fault tolerant EPDS for MEA. Adapted from [3–5]...... 38

3.5 Advanced electric system EPDS for MEA. Adapted from [3–5]. . . .. 39

3.6 MVDC Block Diagram [6]...... 40

3.7 Power system comparison of AEV and AES. Adapted from [7, 8]. . . . 41

3.8 Energy Densities for existing battery chemistries. Adapted from [9–11]. 48

xi CHAPTER 1

Introduction

This chapter gives a brief introduction to aircraft power system analysis, beginning

with hybrid unmanned aerial vehicle (UAV) system modeling and energy optimiza-

tion, and transitioning to an introduction of all-electric aircraft. The chapter then

discusses the motivation for the research, and concludes with the organization of the

thesis.

1.1 System Modeling and Energy Optimization

Aircraft are classified using many different terms, depending on their size and application. For example, commercial aircraft refer to aircraft used for the transportation of people between destinations. This section discusses hybrid UAVs, an aircraft classification left for very small aircraft often used for reconnaissance and aerial photography. UAVs support various power system designs, mainly purely electric and hybrid. For purely electric UAVs the aircraft has its energy supplied by batteries. For hybrid UAVs the energy comes from two sources, most commonly an engine and a battery. The decision between these two designs often resides in the size of the UAV under discussion. For larger aircraft, those that utilize electric power for all non-propulsive systems can be considered as more electric aircraft (MEA). MEA

1 boast many beneﬁts over traditional aircraft, including reduced weight and increased

eﬃciency. But, MEA are not the end goal in aircraft development. Non-electric

propulsion replacement with all-electric alternatives means reduced dependence on

fossil fuels. To achieve an all-electric aircraft (AEA), work must be done to optimize

existing power systems to the point that the aircraft combustion engines can be re-

placed. Fortunately, hybrid UAVs provide the ideal prototype for this transition to

all-electric aircraft.

There are two main reasons that make the hybrid UAV the ideal prototype for this transition. First, the hybrid UAV size allows for optimization techniques to be applied on a small-scale before being extended to large application cases, such as MEA.

Second, hybrid UAVs have a purely DC power system, similar to those on MEA. This means that the methods of optimization can easily be extended. The optimization of the power system of the hybrid UAV, speciﬁcally the usage of the energy sources, will give large aircraft designers a set of methods which can be extended to those larger applications.

There is a plethora of existing literature discussing the energy optimization of hybrid electric vehicles (HEVs), mainly trucks, buses, and cars. However, these techniques cannot be directly applied to hybrid UAVs due to the significant differences between the two categories of vehicles [12]. Among the significant differences are the size of the system under consideration, operating environment of the UAV, and the required fast decision making of the UAV. HEVs operate on the ground and UAVs operate in the air. Therefore, the aspects of HEVs related to braking are not ap- plicable to UAVs. Related to operating environment is the decision making speed.

Because the UAV is operating in the air, it must be able to adapt quickly in the event

2 of a load change to ensure proper operation. For HEVs, these decisions can be made

a bit slower because a minor delay of a HEV will not result in catastrophic outcomes.

There are a few recent works that proposed methods of optimization among hybrid

sources, but they overlooked two critical requirements of hybrid UAV power system

optimization, including the fast decision making requirement and an accurately mod-

eled system considering all of the physical constraints of the UAV power system.

Having both of these covered will ensure that the optimal solution is representative

of the system. With both requirements considered at a glance, now let us consider

them in detail.

The fast decision making requirement is particularly important for the critical loads onboard the hybrid UAV. Any load change where the system does not respond fast enough could result in a crash of the drone. Additionally, if a fault were to appear in the system, the response must be quick enough so that the fault is isolated and the remaining loads can still maintain operation. The time resolution needed to handle these disturbances is on the order of one second. For a 24-hour mission profile, this is a significant number of decisions. One specific application that has been previously considered is genetic algorithms [13]. The issue with using genetic algorithms is that the decisions for a 24-hour mission cannot be made fast enough. The list of candidates increases exponentially with time, so this is not a feasible optimization approach.

The system must also be modeled accurately, considering all of the nonlinear constraints of the system that are present in a real-world hybrid UAV. The issue with considering an optimization algorithm with all of these constraints is that once again the computation time would be too high. Therefore, a simpliﬁed system must be considered. Some existing methods of optimization include linear and convex

3 programming, which have been applied to HEV and microgrid applications in the past

[14,15]. However, these applications fail to ever consider those additional constraints

that were initially ignored, leading to solutions that are not optimal of the realistic

system.

In light of the shortcomings of applications in past literature, the proposed method in this thesis considers both the fast computation time and the modeling of the realistic system when determining the optimal solution. Figure 1.1 illustrates the high level implementation approach presented in this paper.

Figure 1.1: Proposed method of energy optimization.

There are two separate parts to the solution that work together to yield the optimal result. First, the algorithm, which here is a dynamic programming algorithm, considers the system under the critical basic constraints, including power output limitations, load requirements, and battery constraints. Dynamic programming was the algorithm of choice because of its frequent application in HEV optimization [16–

18]. The virtual UAV model fully characterizes the system, including the nonlinear constraints present. The results of the algorithm are fed into the virtual UAV model,

4 which is responsible for validating whether the real-system would operate the way the original optimization intended. The method of using a validation model has been considered in past optimzal control research [19], but rarely with a focus on UAV systems [20]. The input, H, represents operator input, so that changes can be made to the results should the results of the virtual UAV simulation be diﬀerent than what is expected. This system can either be open loop, where the operator is allowed to intervene, or closed loop where the two systems interact directly with each other.

Implementation using this proposed method allows for the quick computation time of past implementation approaches, all while validating the results under a realistic system model, something the past literature has failed to do. Chapter 2 presents the detailed analysis of the hybrid UAV system modeling and energy optimization process, with a focus on the system modeling aspect. Additionally, simulation results are given and analyzed.

1.2 The Future All-Electric Aircraft

The system modeling research conducted on hybrid UAVs is a small-scale aircraft application that can be extended to larger aircraft applications. As was discussed in

Section 1.1, MEA have similar power systems to hybrid UAVs, albeit much larger and more complex in nature. However, the MEA is only a stepping stone in the progress towards an all-electric power system. This consideration, realized through an extensive literature review, led to the determination that there has been a lack of literature discussing the AEA power system. The AEA is currently a vision of the future and is the ideal implementation of a completely electric power system.

Therefore, Chapter 3 is dedicated to the results of a survey of existing electric vehicles

5 to predict the future all-electric aircraft power system. This section introduces the

topic of “all-electric” and its deﬁnition as it pertains to aircraft.

Over the past several decades, there has been a worldwide push to transition from using gas energy sources within vehicles, ships, and aircraft to a more efficient alternative: electric energy sources. While ideas of more-electric and all-electric applications have been considered since the 1970s, they have only recently begun to reach a global focus [21, 22]. One industry that has worked effortlessly to adapt these changes is the aviation industry. MEA have become the aviation standard, with major aviation companies (e.g., Boeing and Airbus) adapting to the technological changes. The more electric aircraft concept revolves around the replacement of conventional pneumatic, mechanical, and hydraulic systems with electrical power [23]. Specifically, MEA have electric power systems, but do not have electric propulsion, still relying on traditional jet engines to provide the power. Batteries onboard MEA are used for engine start up, transient smoothing, and emergency power situations [24]. Therefore, they do not currently have a role in supplying power to loads for the duration of a flight. The

MEA has been considered an evolutionary implementation of the all-electric vision of the future [25].

MEA have numerous beneﬁts over traditional aircraft including reduced emissions, improved fuel consumption, and decreased weight [26,27]. For example, Boeing’s 787

Dreamliner, the state-of-the-art MEA, was able to remove approximately 20 miles of wiring by transitioning to a more-electric power system, ultimately reducing aircraft weight [28]. A transition to all-electric aircraft (AEA) would introduce additional beneﬁts, including zero emissions during travel and cheaper travel costs resulting

6 from the removal of the need for fuel, the primary cost on current ﬂights. Thus, the

industry’s objective is to transition from more electric aircraft to all-electric aircraft.

The term “all-electric” has taken many meanings in past literature depending on

the application. The National Academies of Sciences, Engineering, and Medicine have

deﬁned “all-electric” to deﬁne a system relying solely on battery power as the main

source of propulsion power [29]. Some applications properly adhere to this deﬁnition

of all-electric, i.e., All-Electric Vehicles (AEV), because they operate purely oﬀ of

electric energy sources. Other applications refer to the vehicle as all-electric when the

power system is all-electric, including electric propulsion, but the propulsion energy

sources are not electric, i.e., All-Electric Ships (AES). In this chapter, “all-electric”

takes the deﬁnition as outlined by the National Academies of Sciences, Engineering,

and Medicine. All-electric ships are still referred to as such, but understanding the

true meaning of the term is essential to the overall goal of reaching truly all-electric

aircraft.

Chapter 3 serves as a comparison of existing power systems, from architectures to specific characteristics (e.g., voltage levels, power requirements, and load characteristics) onboard all-electric vehicles, all-electric ships, and MEA with the goal of predicting which form future all-electric aircraft will take. The merit of this comparison used for predicting future AEA is justifiable based on two main aspects: (i) general similarities among the electric vehicle applications and (ii) the small number of differences among the applications. Additionally, Chapter 3 addresses the challenges of transitioning to AEA, which requires significant effort. Propulsion will need to be electrified and the sources providing energy to the power system will need to

7 support high power requirements. Characterizing all of these aspects will help create

a clear picture of the expectations regarding future AEA.

1.3 Motivation

Aircraft power systems have been studied quite extensively over the past several decades. Speciﬁcally, hybrid UAVs have seen a dramatic increase in military applications. MarketsandMarkets has valued the UAV market at $13.22 billion for 2016, and this is expected to grow to $28.27 billion by 2022 [30]. The small size of hybrid

UAVs allows for them to be a test application for larger aircraft changes. By applying optimization techniques and running simulations on hybrid UAVs, trends can be realized and extended to larger aircraft applications. Existing literature exists for optimization and simulation of hybrid electric vehicles of many types; however, these methods must be modified for application because of the inherent differences between hybrid ground electric vehicles and hybrid UAVs [12]. Therefore, this research considers these differences and develops a method for analyzing the hybrid UAVs such that the results accurately reflect the system under consideration.

With the background knowledge gained from studying small aircraft power systems (i.e., hybrid UAVs), the future of aircraft is considered. Section 1.2 discussed the relatively new push towards all-electric aircraft, which has resulted from the rise of the MEA. While the topic of an all-electric aircraft has been discussed in existing literature [27,29,31–34], a comprehensive discussion of what the future all-electric aircraft will look like has yet to be discussed. This thesis considers power system characteristics of various electric vehicle applications to help predict the future trends in all-electric aircraft.

8 1.4 Organization of Thesis

In Chapter 2, aircraft power system discussion begins with focus on small-scale aircraft, hybrid UAVs. This chapter discusses hybrid UAV system modeling and optimization of the energy sources onboard. An optimization algorithm is discussed, and a virtual UAV model is developed for the purpose of validating the baseline results from the algorithm. The results from both the algorithm and the model are compared and conclusions as to the eﬃcacy of the model are given.

In Chapter 3, aircraft power system discussion transitions to the future, considering AEA. Existing electric vehicle power system characteristics are examined, including architectures, voltage levels, power requirements, and load characteristics.

A prediction is made as to what the all-electric aircraft power system of the future will look like based on these existing trends. Finally, limitations of becoming all-electric are discussed and a prototype all-electric aircraft of the future is discussed.

Chapter 4 gives overall conclusions of the aircraft power system study, and future works are discussed.

9 CHAPTER 2

Hybrid UAV System Modeling and Energy Optimization

This section focuses on the modeling and energy optimization of the hybrid UAV system, with emphasis on the system modeling portion. First, some general system characteristics and a provided sample load proﬁle are given. Second, a brief discussion of the energy optimization of the hybrid UAV is given to allow for a complete understanding of the problem being discussed. Third, a Simulink model was developed for the hybrid UAV system. Fourth, a comparison of the simulated results and the energy optimization algorithm results is given to prove the eﬃcacy of the model.

Lastly, the chapter is concluded.

2.1 System Description

The term “hybrid” is deﬁned as having multiple energy sources. The most common implementation includes both gas and electricity. Such has been widely implemented in the auto industry where cars use both engines and batteries. The same is true for the aviation industry. For this case, an internal combustion engine (ICE) and a battery are utilized. Figure 2.1 illustrates the hybrid UAV system represented as a block diagram [1].

10 Figure 2.1: Hybrid UAV system block diagram [1].

Both of the aforementioned sources contribute to the 50 V propulsion bus. De- pending on the power requirements of the mission, either one or both sources can be used. From the 50 V propulsion bus, the power is distributed among the various loads on board the UAV. The primary load is the motor, which is used to propel the UAV.

This load is to remain on at all times, or else the UAV would not be able to sustain

flight. The remaining loads are grouped into two main categories: payloads and flight critical loads. Payloads represent loads that are used under normal operation, but are not critical to flight. Flight critical loads are loads that are critical to flight. The motor is considered to be a flight critical load, but its usage on board the vehicle considers it separate from the remaining flight critical loads, so it is shown by itself.

The aggregation of loads is a common practice in power system analysis [35], and was done for simulation of both the payloads and ﬂight critical loads. Additionally,

11 load shedding is a common practice among certain power system applications [36,37].

Load shedding refers to the ability to shut of loads in the event of a fault that threat-

ens the operation of the system. In this system, the payloads are all sheddable, but

the ﬂight critical loads are not sheddable. An additional 28 V battery is included

on board to provide power to the ﬂight critical loads if a fault occurs. For the pur-

poses of this study, the eﬀects of a fault are not considered, but for intents of fully

characterizing the model it is mentioned.

The energy usage of the hybrid UAV comes from a sample load profile, which was modeled from an existing UAV mission profile. Figure 2.2 illustrates the sample load profile used for this research.

It is important to note that this sample load profile is only one example of a load profile. The only constraints on the load profile are the overall mission duration, duration of “dash” periods, and maximum power output. For this research, the capabilities of the energy sources were the driving factor in determining the mission characteristics. The overall mission duration is 24 hours. Expressed in seconds, the duration is 86,400 seconds. Seconds is a more useful unit for the simulations, which have a one second resolution. The hybrid UAV spends the mission in two main periods: “cruise” and “dash” periods. The “cruise” period is where the UAV spends the majority of the mission. This period has a power requirement of 1.8 kW.

This power can be provided solely by the ICE. The “dash” period represents times where the UAV is using maximum power. For these periods, the UAV has a power requirement of 5 kW. This requires both ICE and battery usage. Occasionally, there are mission periods where the power requirement falls between the 1.8 kW and the 5 kW lower and upper bounds, such as when only a portion of the loads are active. The

12 Sample Load Profile for Full UAV Mission 6000

5500

5000

4500

4000

3500

Load Power (W) 3000

2500

2000

1500 0 1 2 3 4 5 6 7 8 9 Time (s) ×104

Figure 2.2: Sample hybrid UAV load proﬁle [1].

overall objective is to optimize the usage of these two sources (i.e., using the battery in an optimal method to minimize the fuel consumed during the mission). The next two sections will discuss the energy optimization algorithm and the hybrid UAV Simulink model, respectively, which were used to determine the optimal operation strategy.

2.2 Hybrid UAV Energy Optimization

The overall objective is to minimize fuel consumption over the mission’s duration.

Minimizing the fuel consumption can be addressed from two diﬀerent perspectives.

First, the planning perspective where the UAVs load proﬁle is known prior to mission

13 start. Second, the operational perspective where the mission is not known and the

UAV must make decisions in real-time. This chapter focuses on the planning per-

spective where the energy optimization is conducted to determine sizes of the energy

sources needed for the given mission description. The planning perspective focuses

less on computation time than the operational perspective. Nevertheless, the for-

mulation and algorithm must be designed such that the computation time is within

reason. To accomplish this goal, given that the mission has a duration of 86,400 sec-

onds, the nonlinear system constraints could not be considered. The formulation and

algorithm consider the system at a fundamental level and allow the Simulink model

to validate the results under full system characterization. Future work is to extend

the problem to an operational perspective, which is needed for real world hybrid UAV

operation.

Prior to developing the algorithm, the fundamental system constraints were determined including engine output limits, battery charging and discharging limits, and system power requirements. The resulting dynamic program was devoid of complex system constraints in an eﬀort to keep computation time reduced. The algorithm was developed by my colleague, and is included below to help capture the whole concept of the problem being discussed in this chapter. More information regarding the development of this algorithm, starting from the problem formulation, can be found in literature [1].

14 Algorithm 1 Optimal Energy Use Initialize SOC, Load Proﬁle j =1 while j ≤ Total Mission Time do Calculate engine power, SOC, cost if cost(j)= ∞ and strategy(j) = discharge then j ← j − 1 strategy(j) ←do nothing else if cost(j)= ∞ and strategy(j) =do nothing then j ← j − 1 strategy(j) ← charge else if cost(j)= ∞ and strategy(j) = charge then while strategy(j) = charge do j = j − 1 end while strategy(j) ← charge end if end while

The algorithm considers three options at each time step. The conservation of power suggests that the power delivered to the loads, both payloads and ﬂight critical loads, must come from the sum of power provided by the battery and the ICE. This relation is shown below as (2.1)

PF Cloads + Ppayloads = Pbattery + Pengine (2.1)

where PF Cloads represents the power required from the ﬂight critical loads, Ppayloads represents the power required from the normal payloads, Pbattery represents the power going into or coming from the battery, and Pengine represents the power supplied from the engine. The battery is capable of providing power to the bus through discharging, charging by drawing power from the propulsion bus, or doing neither of the previous options. The rates at which the battery can charge and discharge were ﬁxed for initial

15 simulations. A few methods can be used to ﬁnd the minimum fuel consumption. A

simple option is to consider all three options at every time step. While this might

seem like a reasonable, comprehensive option, the computation time for that decision

making is much too long. Instead, discharging of the battery was prioritized ﬁrst.

Discharging of the battery is the most cost eﬀective option because it adds power

to the propulsion bus decreasing the amount of power that must be provided by the

engine. Charging is the least cost eﬀective option because it requires a higher output

from the engine to power the loads and charge the battery. Doing neither is the middle

cost option for the battery. It is important to note that during “dash” periods the

battery must discharge because the max engine output is lower than the needed load

power, so the battery must be charged in time for these periods. To accomplish this,

the algorithm uses backward induction to determine the optimal usage of the energy

sources. The battery decisions for all time steps are stored in MATLAB arrays, one

for charging and the other for discharging, and are passed into the Simulink model

that is discussed in the following section. Each decision in the two arrays is a binary

decision, meaning it takes a value of 1 if charging or discharging and a value of 0 if it

is doing nothing. The battery cannot simultaneously charge and discharge.

2.3 Hybrid UAV Simulation Model

The hybrid UAV Simulink model was developed using the system description information from Figure 2.1 and Figure 2.2. This section analyzes the model in detail, considering each of the individual blocks that contribute to the overall model.

Figure 2.3 illustrates the overall hybrid UAV Simulink model.

16 Figure 2.3: Full hybrid UAV Simulink model.

This model is run in conjunction with the energy optimization algorithm to validate the optimal usage of the energy sources on board the UAV. The algorithm outputs the optimal usage of the energy sources when considering the system at a basic level without all of the nonlinear constraints. This helps improve computation time, an important aspect of the problem at hand. These resulting outputs are fed into the hybrid controller of the model, which then tells the full system how it must operate. The Simulink model helps validate that the determined operation of the

UAV will work under realistic operating constraints not considered in the algorithm.

Depending on the operation of the system, after being run in the Simulink model, the result is considered, which allows for modiﬁcation of the algorithm if necessary. The following subsections analyze the individual blocks of the overall model in detail.

17 2.3.1 Engine and Generator

The engine and generator block, shown in Figure 2.4, is separated into three main components, the governor and prime mover, the generator, and the controllable voltage source.

Figure 2.4: Hybrid UAV full engine model.

The function of the engine and generator block is to generate the output power based on the input power command. The governor and prime mover transforms the power command input into a rotational speed which is then used by the generator to generate the bus voltage. Depending on the load, the power varies from the lower limit of the engine to the upper limit.

Considering the three main components individually, the governor and prime mover block consists of the engine, governor, and inertia transfer functions. This governor and prime mover is responsible for generating the rotational speed, which ultimately is passed onto the generator. This is the mechanical power creator of the system. Figure 2.5 shows the governor and prime mover component.

The transfer functions were initially matched to a 370 cc engine. The desired engine is the 56 cc engine. To properly match this system to the new engine, the

18 Figure 2.5: Hybrid UAV governor and prime mover model.

engine fuel map limit and inertia blocks needed to have their values updated based on the torque plots for the 56 cc engine. When this change was attempted, the system had some stability issues. For now, the 370 cc engine is being used for simulations until the stability issues can be more thoroughly explored.

The generator is responsible for taking the rotational speed generated from the

governor and prime mover block and translating that into an electrical power. This

is conducted by using a lookup table. Figure 2.6 shows the generator block in detail.

Figure 2.6: Hybrid UAV generator model.

19 Depending on the range of the rotational speed, the output voltage varies. The

desired output voltage is a consistent 50 V DC, but system disturbances can cause

minor variations to this output voltage.

Lastly, the controllable voltage source is responsible for turning the Simulink nu- merical voltage signal into an actual voltage signal. This is done by utilizing a controllable voltage source with the signal being the output of the generator block. Figure

2.7 illustrate the controllable voltage source block.

Figure 2.7: Hybrid UAV controllable voltage source model.

The load block is used to observe the current to compare the output power to the desired output power. The main output of this block is the voltage signal. This is also the main output of the entire subsystem since this is how the engine subsystem interacts with the rest of the Simulink model. The output current is dependent upon the load connected to the system.

Future modiﬁcations to this model will include fuel mapping, downsizing to 56 cc, and added nonlinearities including slew rates. The intent of the initial design was to

20 create a model that simulates the basic system, with some nonlinearities, that can be

expanded into a model more representative of a real-life engine.

2.3.2 Battery Charging Circuit

The main battery charging circuit is responsible for connecting the battery to the propulsion bus, enabling charging and discharging of the battery depending on which is needed. Figure 2.8 illustrates the full battery charging circuit model.

Figure 2.8: Hybrid UAV full battery circuit model..

The battery is a rechargeable lithium-ion battery with a nominal voltage of 48

V DC. Determination of the capacity of the battery required information from the sample load proﬁle shown in Figure 2.2. The longest “dash” period is 30 minutes.

During this period, if the engine provides maximum power the battery must provide

1000 W. This means the battery must have a watt-hour rating of 500 Wh. The capacity was determined by dividing by the nominal voltage, which is 48 V. This corresponds to a capacity of 10.4 Ah. When sizing batteries it is important to consider

21 the state of charge (SOC) range for the battery. Lithium-ion batteries should typically

avoid operating when near full depletion to avoid battery degradation, so the SOC

lower limit was set at 25%. To account for the region where the battery is not to

be used, the capacity increased to 13.9 Ah. Additionally, the initial state of charge

needed to be considered for the model. Assuming the battery was not charged up

to 100% before each mission, a starting SOC of 50% was chosen. By selecting this

initial SOC, it also means the battery must be charged to 50% before the mission

concludes. The internal resistance of the batteries was selected using datasheets for

the selected battery cell.

The battery charging circuit was modeled using two unidirectional DC/DC converters. One converter is used to charge the battery while the other is used to discharge the battery. This could also have been modeled as one bidirectional DC/DC converter, but for ease of application the former was chosen. Figure 2.9 shows the battery charging circuit in detail.

Figure 2.9: Hybrid UAV battery charging controller model.

22 It was assumed that the battery would both charge and discharge at the same rate.

This was an easy assumption to make initially, but will be changed as the model con-

tinues to develop. In order to simplify initial design, the converter was modeled using

controllable current sources. At its most basic level, a DC/DC converter converts

one voltage level to another voltage level by changing the currents at each side to

provide a constant power on each side. Eﬃciency was assumed to be 100% for initial

simulations. The current at the low voltage side (battery side) was assigned to 20.83

A. This current value was chosen based on the power requirements of Figure 2.2. The

battery voltage and current set-point deﬁned the battery side power. The bus side

power was conserved and the necessary current draw from the engine was established.

The bus voltage was used on the bus side to also assist in deciding the current draw

to make this circuit model work. Because the bus voltage and the battery voltage

are nearly identical, this is not speciﬁcally a buck or boost converter. The focus is on

conserving power and keeping the current at the battery side constant, even as the

battery voltage varies. Further versions of the model explore higher voltage batteries,

which would allow for speciﬁc converter applications. This is not discussed in this

thesis as the research has yet to be conducted.

It should be noted that the reason the charging and discharging circuits were not modeled using inductors, capacitors, resistors, and FETs is because of the time resolution needed to simulate those components. Common power electronics will operate with a switching frequency of 10 kHz. In order to simulate a mission with a duration of 1 day within reasonable simulation time, the ﬁner switching transients must be ignored. For this reason, the choice to model the converters using constant current sources was made. Future modiﬁcations to the model can easily consider loss

23 in the conversion process by introducing line impedance or a constant block dropping

the overall output by a certain percentage.

The discharging circuit works very similarly to the charging controller. The direction of operation is the opposite. Figure 2.10 shows the discharging circuit model.

Figure 2.10: Hybrid UAV battery discharging controller model.

The ﬁrst controllable current source draws current from the battery. Using power

conservation, shown in equation (2.1), and the bus voltage, the bus current is de-

termined. The circuit is enabled/disabled from the signal coming from the hybrid

controller. Constant current is not technically a correct approximation of the system,

but for initial simulations, this is more than acceptable because the current is nearly

constant for a majority of this operation range. Future research will look at making

these charging and discharging circuits more accurate to better represent the system.

24 As previously mentioned, the charging and discharging circuits currently have identi-

cal rates of charging and discharging. This is an approximation but serves its purpose

for initial simulations.

2.3.3 Motor and Loads

The motor, payloads, and flight critical loads are all modeled using the same approach. Each load was modeled as a constant power load (CPL). A CPL is a load that works to maintain a constant power draw. This is realized by considering the voltage and current of the load. An increase in the applied voltage results in a decrease in the current to the load, and vice versa. The loads were modeled as such because the only information provided for the onboard loads was the power requirements for various times during the mission. Initially, the CPLs are considered ideal. No cable impedance is included, but, similar to the battery circuits, this addition will be made for future models to more fully characterize the system. Figure 2.11 illustrates the model for the motor. The models for the payloads and flight critical loads simply had a different power requirement input.

One important point of discussion is the resolution of the simulation. As was previously mentioned, the time resolution is one second. Due to the lengthy mission duration, individual loads and converters were not observable on millisecond or mi- crosecond resolutions that are common to power electronic simulations. Therefore the majority of transients are not visible in great detail in the simulations. The impact on power consumption, speciﬁcally related to the battery, is negligible in these instances.

25 Figure 2.11: Hybrid UAV motor model.

Additionally, the loads were not aggregated here like they are in the optimization algorithm. By separating the loads, the model has more versatility in its application.

This is one of the major beneﬁts of having this model.

For this initial system model, the propulsion bus and payload bus are at the same potential. Because each load is modeled as a CPL, the actual voltage and current values do not have much importance. As this system is expanded into a more accurate model in the future, the voltage and current values will be important. In future applications, a DC/DC converter will be added to step down the 50 V propulsion bus to 28 V for the payload bus, which was not speciﬁcally shown on Figure 2.1.

2.3.4 Hybrid Controller

The hybrid controller is the central control center of the hybrid UAV simulation model. Its purpose is to control when each source in the system is active. Figure 2.12 shows the hybrid controller block in detail.

26 Figure 2.12: Hybrid UAV hybrid controller model.

There are two main components to this block. First, the engine power required.

This is the power that must be provided by the engine to keep the system operating properly. The engine power and the battery power sum up to equal the total system available power. This follows the conservation of power which says the sum of the power into the system equals the sum of the power out of the system. The battery power can be either positive or negative depending on whether the battery is discharging or charging, respectively.

The other component of this block is the charging and discharging circuitry section responsible for enabling and disabling the battery. Using arrays stored in MATLAB, whose data comes from the optimization algorithm, the battery is controlled to either

27 charge, discharge, or do nothing. . If the battery SOC is at 100% then it is prohibited

from charging, and if the battery is at 25% then it is prohibited from discharging based

on the limitations previously deﬁned. The ﬂight critical battery is always disabled

because fault considerations are not currently being considered with this model.

2.3.5 Measurements

The measurement block is an important aspect of the model because it provides a single location for observation of all of the critical signals present in the system.

Figure 2.13 is shown below.

Figure 2.13: Hybrid UAV measurements block.

28 In order for this system to work in unison properly, each and every component

must be functioning according to its theoretical operation. This centralized location

helps ensure proper functionality.

The next section explores the initial results of the virtual UAV simulation and compares them to the baseline data from the optimization algorithm.

2.4 Simulation Results

The dynamic programming algorithm, shown in Section 2.2, was implemented using the load proﬁle shown in Figure 2.2. As previously mentioned, the outputs of this algorithm are the binary charging and discharging decisions which are passed into the virtual UAV. The virtual UAV then runs for the entire mission duration and the realistic system results are stored. These two resulting data sets are plotted together, as shown on Figure 2.14. The dashed, blue curves represents the results from the algorithm and the solid, orange curves represent the results from the virtual UAV simulation.

From the blue curve of the battery SOC plot of Figure 2.14 it can be seen that the optimal decisions are to charge the battery just before a dash period begins to meet the upcoming power requirements. The battery is not used during cruise periods because it is not necessary to use the battery for these periods because the engine can supply suﬃcient power. Depleting the battery would result in insuﬃcient energy remaining to get through the dash periods where using the battery is necessary. The battery charging and discharging have been initially modeled with a constant charging and discharging rate. This can be realized by considering the slope of the line of the

29 Figure 2.14: Comparison of the DP and virtual UAV simulation results.

battery SOC. The physical realization of a constant charging or discharging rate is a

ﬁxed C-rate for the battery.

One other important consideration is the duration of the dash period that the UAV is preparing for. A longer dash period requires more battery usage. For the sample load proﬁle shown in Figure 2.2 the longest dash starts at 31,500 seconds and lasts for

1800 seconds. With this information, it can be seen on Figure 2.14 that the battery charges to 100% SOC just prior to this dash so that it can provide suﬃcient power for this dash. The other two dash periods, which have shorter durations, charge to lower

SOC percentages. The battery charging can be seen by referencing the engine power plot where the engine power increased to charge the battery, then again to provide some additional power, in addition to the battery, to power the dash period.

30 It should also be noted that the load proﬁle plot of Figure 2.14 shows a brief load

increase at 30,000 seconds. The battery was not used for this minor change because

the engine was capable of picking up the power increase for the short duration to

ensure the battery was charged for the upcoming dash period.

Using these DP results, the results of the virtual UAV simulation can be considered. The results, shown as the orange curve on Figure 2.14, illustrate that the nonlinear system model closely followed the predicted results from the simulation.

The load power curves for the DP simulation and the UAV model simulation are identical, which is expected because they both used the same load proﬁle. Similarly, the battery SOC plot is nearly identical. The DP formulation considered a constant charging rate, as did the battery model in the virtual UAV. Therefore these should be similar. The big diﬀerences occur with the engine power plot. As was previously noted Section 2.3.1, the engine was modeled according to a real-world 56 cc engine.

Therefore, the nonlinear engine characteristics are properly modeled. For the DP algorithm, these charactistics cannot be fully captured. Therefore, on the engine power plot it can be seen that there are some power spikes and minor diﬀerences in power levels. This is a result of the accurate modeling from the UAV model. Nevertheless, the virtual UAV model results are close to the algorithm results. Therefore, the virtual UAV model has validated that the algorithm results are close to what can be expected when applying the results to a real-world system.

31 2.5 Conclusion

This chapter presented an energy optimization algorithm and a virtual UAV model. The algorithm was responsible for minimizing the fuel consumption of the engine for a given load profile, and the virtual UAV model was responsible for validating the baseline results from the algorithm. The algorithm is a dynamic programming algorithm that prioritizes discharging of the battery when possible because it is the most cost-effective option. The results, shown on Figure 2.14, illustrated that the optimal usage of the battery was to charge just before an upcoming dash period to ensure enough power was provided to the loads. The algorithm explored the problem from the planning perspective where the load profile was known.

The primary focus of the chapter was on the virtual UAV model, which was

developed to validate the results from the algorithm in a realistic system subject to

nonlinear constraints that would be present onboard a real-world hybrid UAV. Each

aspect of the hybrid UAV model was considered in detail, with emphasis given to the

engine model and the battery charging and discharging circuits. The results of the

simulation showed that the virtual UAV model results were very similar to the baseline

results from the algorithm with some minor power spikes occurring in the engine

power plot. These spikes can be attributed to the engine model, which considers real-

world engine parameters that were not characterized in the algorithm, in an eﬀort

to decrease the computation time of the system. The virtual UAV model can be

extended to further applications, such as stochastic load proﬁles. These potential

future applications will be discussed in Chapter 5.

32 CHAPTER 3

The Future All-Electric Aircraft

In light of the research conducted on hybrid UAVs and larger aircraft, discussed in Chapter 2, it was determined that there has been a lack of literature discussing the all-electric aircraft. The all-electric aircraft is currently a vision of the future.

Therefore, this chapter gives an in-depth analysis and comparison of existing electric vehicle power systems to predict architectures, voltage levels, power requirements, and load characteristics of the future all-electric aircraft. This research serves as an informative study that will be beneﬁcial to researchers to come. The content of this chapter has been accepted for publication in ITEC 2018 [38]. Reference the full conference paper for a more comprehensive discussion on the following topic.

First, the all-electric power system is analyzed. Power system architectures and characteristics are explored to make predictions of the future all-electric aircraft.

Second, the challenges of becoming all-electric are considered, including propulsion methods and battery chemistry. This chapter concludes with a look to the future to see what types of technological improvements are in the works.

33 3.1 The All-Electric Power System

Advancements experienced by existing all-electric power systems can be attributed to the growth, development, and integration of power electronics components into previously existing power systems [8,36,37,39]. The mid-1950s to mid-1980s experienced a power electronics revolution, wherein solid-state technology enabled new energy conversion within electrical power systems [22] and transistors and thyristors allowed for power conversion in high power applications. Additionally, power electronics enabled control of electric motors requiring a variable input with respect to voltage and frequency [37]. Ultimately, this revolution enabled the electrical systems of aircraft to be used for actuation systems, wing ice protection, environmental control, and fuel pumping on board aircraft [34].

Analyzing existing all-electric power systems provides insight into what to expect from the power systems on board future AEA. Typical all-electric power systems contains ﬁve major sections: power generation, primary distribution, conversion, secondary distribution, and management [2, 40, 41].

The power generation section provides the electrical power to the rest of the power system. This section is represented by batteries when considering an all-electric application. The primary distribution section takes the generated power and distributes it to the converters onboard. The conversion section converts the voltage levels through the use of DC/DC converters and DC/AC inverters. The secondary distribution section distributes the converted power to loads, consisting of propulsion loads and accessory loads. Lastly, the management section is responsible manages the other four sections of the power system, ensuring proper operation of all the interconnected

34 Figure 3.1: Major sections of the all-electric power system. Adapted from [2].

components. These major sections are shown in Figure 3.1. The arrows represent

direction of energy ﬂow.

All-electric power systems have several benefits over traditional and more electric power systems, where propulsion is not electrified, e.g, reduced fuel consumption, improved efficiency, lower maintenance costs, improved system reliability, survivability, and affordability [25,27,36,37]. Electrification of the entire power system removes fossil fuels from the vehicle, making all-electric power systems the economical long-term solution.

Yet, the implementation of all-electric power systems does not come without some accompanying drawbacks. The most common issues that arise are related to voltage stability [42–44], increased system complexity [8], additional necessary fault tolerance awareness [3, 45], and increased weight. It should be noted that weight is only an issue assuming implementation is with current battery chemistry. An improvement

35 in battery chemistry would eliminate this issue, which is further discussed in Section

3.2.

In the following sections, 3.1.1 and 3.1.2, power system architectures, voltage levels, power requirements, and load characteristics for various electric vehicles are discussed to provide insight for AEA.

3.1.1 Power System Architectures

Power system architectures of all-electric vehicles, all-electric ships, hybrid unmanned aerial vehicles, and more electric aircraft share many commonalities. Ana- lyzing and comparing these architectures is critical to achieve the goal of predicting future AEA.

More Electric Aircraft

The Electrical Power Distribution Systems (EPDS) of MEA consist of four main architectures: the centralized EPDS, semi-distributed EPDS, advanced electric system, and fault-tolerant EPDS [5]. The most common architecture used is the centralized EPDS, a radial distribution connection. Figure 3.2 diagrams the high-level connections of this architecture. Its main beneﬁt is ease of maintenance, but the large quantity of wire needed leaves alternative architectures more desirable [3].

The remaining architectures for MEA were designed to resolve the shortcomings of the centralized EPDS. Figure 3.3 diagrams the semi-distributed EPDS, which utilizes distribution centers to drastically reduce wiring.

The reduction in wiring decreases the overall cost by 40%, weight by more than

40%, after accounting for the additional protection devices needed, and increases eﬃciency [4]. Additionally, this architecture allows for easy upgrades, a major beneﬁt.

36 Figure 3.2: Centralized EPDS for MEA. Adapted from [3–5].

The fault-tolerant EPDS, shown in Figure 3.4, consists of switch matrices for the sources and loads. The architecture is highly redundant, but fails operationally when there is a fault in the switch matrices.

The advanced electric system, shown in Figure 3.5, is considered to be a replacement for the previous three architectures and retains all of their beneﬁts. It is a microprocessor-based system containing load management units and relay switching units, with which all of the sources and loads interact. Utilizing this architecture requires caution due to its design as a single unit, which lends itself to fault issues [5].

37 Figure 3.3: Semi-Distributed EPDS for MEA. Adapted from [3–5].

Figure 3.4: Fault tolerant EPDS for MEA. Adapted from [3–5].

38 Figure 3.5: Advanced electric system EPDS for MEA. Adapted from [3–5].

All-Electric Ships

Power system architectures seen in all-electric ships are very similar to those in

MEA with one distinguishing factor: power system complexity. AES have more complex power systems than MEA due to the sheer number of components that need integrated. Widely varying requirements between individual ships make it challenging to specify architectures. Thus, ships require more of a focus on the design process compared to aircraft, which allow for a higher focus on specific architectures. For this reason, AES design considers many design methodologies, rather than specific architectures [46,47]. Nevertheless, there are specific power system architectures that exist and are worth discussing.

39 Figure 3.6: MVDC Block Diagram [6].

AES development is pointing towards Medium Voltage DC (MVDC) power systems, with two common architectures: radial and zonal [6, 48]. Figure 3.6 is a block diagram representation of the radial distribution for a MVDC all-electric ship. Radial distribution is the preferred architecture as it is very similar to the commonly used architectures of the past, allowing for easy transition. Radial is also known for its simplistic application and lower total implementation cost [49]. Similarities can be drawn between the centralized and semi-distributed EPDS architectures of the MEA, shown in Figures 3.2 and 3.3, and the radial distribution of the all-electric ship, as all three are point-to-point architectures. Should the complexity of the aircraft power system

40 (a)

(b)

Figure 3.7: Power system comparison of AEV and AES. Adapted from [7, 8].

increase with the transition to all-electric aircraft, referencing the radial all-electric

ship power system will prove invaluable.

On the contrary, the zonal distribution is known for its continuity of service, meaning that any faults or errors in the system do not have a signiﬁcant impact on the system’s operation [36, 49]. Zonal distribution has been particularly eﬀective in low voltage ship service power systems [50]. The decision between radial and zonal comes down to the intended application, i.e., the requirements of the AES being designed are highly important.

41 All-Electric Vehicle and Hybrid UAV

AEV power system architectures play an important role in better understanding the larger power systems of MEA and all-electric ships. Common AEV power system architectures are similar to those seen on AES, albeit much smaller and less complex

[51]. A comparison between AEV power systems and AES power systems is shown in

Figure 3.7. It should be noted that the AES represented in Figure 3.6 is essentially the same as the one shown in Figure 3.7(b), with the major diﬀerence being the loads shown.

Similarly, hybrid UAV power systems share many similarities with the MEA power system, e.g., non-electric propulsion. Examining small-scaled power systems is helpful for understanding concepts before shifting focus to larger applications [1]. In addition, many of the benefits of MEA and AES are shared with AEV, including increased efficiency and decreased fuel consumption [52, 53]. Beyond understanding purposes, these smaller applications are less helpful in predicting the future of all-electric aircraft. The drastic size difference means there are different limitations and challenges that must be considered, further discussed in Section 3.2.

Future All-Electric Aircraft

Considering power system architectures for existing all-electric and more electric vehicles of all types, predictions can be made for AEA. Both MEA and AES have their own versions of radial and zonal distributions. Beneﬁts of radial distribution, including simplistic application and ease of maintenance, suggest it will be the chosen architecture on AEA. Of the radial distributions previously discussed, a centralized

EPDS, semi-distributed EPDS, or advanced electric system is the logical choice for

42 AEA, due to their current application on aircraft. However, depending on the com-

plexity of the power system, a radial distribution closer to the one seen on MVDC

ships may be necessary.

Additionally, there has been a recent trend towards distributed propulsion. Dis- tributed propulsion is not an architecture itself, rather a feature of some power system architectures, focusing on the division of main sources of thrust to reduce fuel consumption [32,54]. It can be anticipated that AEA architectures in the future will utilize distributed propulsion to improve eﬃciency and decrease the amount of energy that must be provided by the batteries. More information on distributed propulsion can be found in Section 3.2.

3.1.2 Power System Characteristics

In addition to the general power system characteristics addressed in the introduction to Section 3.1, there are some speciﬁc power system characteristics relevant to

AES and MEA that can help predict the characteristics of the AEA power systems.

This section addresses the similarities and diﬀerences between AEA and MEA with regards to voltage levels, power requirements, and load characteristics.

Voltage Levels

AES and MEA have utilized a wide array of voltage levels over the course of their lifetimes. AES have experienced a trend towards MVDC because of the lower overall performance of high voltage AC applications [55]. The term medium voltage is taken from IEEE Std. 1709 to mean voltages between 1-35 kV [6]. Loads requiring lower

DC voltages or AC voltages are converted accordingly. For MEA, both AC and DC voltages are used. Military aircraft tend to use 270 V DC [23]. The new generation of

43 civilian aircraft power systems commonly use a wide array of voltages [3, 56]. Table

3.1 outlines the most frequently applied voltages on board these MEA. Overall, the

MEA is much more application dependent than the AES when it comes to selecting

voltage levels.

Table 3.1: Common MEA Power System Voltages Bus Voltage (DC) Bus Voltage (AC) Large Loads Avionics Loads

270,350,540V 230V 115VAC 28 VDC

It is important to consider why MEA have not moved toward higher voltages in the kilovolt range similar to what is seen in all-electric ships. According to the

Paschen curve for air, corona discharge (i.e., the partial breakdown of the air around the conductor) occurs anywhere from 1 kV RMS to 5 kV RMS at 30,000 ft, depending on gap spacing [57]. For this reason, power system voltages must be chosen carefully for aircraft. The expectation for AEA in the future is to adhere to the common practices of the more electric aircraft, limiting the voltages to less than 1 kV RMS to avoid issues with corona discharge.

Power Requirements

Increasing load demands on board AES and MEA have driven up power requirements. The load demand increase, results from both an increase in the quantity of loads and the need for converters to interface with the loads. Large MEA have up to twice as many loads as traditional aircraft [58]. The same will be true for commercial size AEA. Power requirements, currently in the 1-10 MW range for AES and the 1-5

44 MW range MEA, will only continue to rise with the electriﬁcation of the main energy

sources [22, 34, 58].

Load Characteristics

The load characteristics of both AES and MEA help predict the characteristics of

AEA. Speciﬁcally, the role load shedding plays on the power system. Load shedding is a source of aircraft protection that refers to the ability of a power management system to shut oﬀ a load in the event of a fault to avoid a full system failure [36, 37]. For

AES, the loads are divided into two major categories: essential users and non-essential users [55]. The essential users (e.g., propulsion systems, motors, communication systems, etc.) cannot be shed at any point during operation. Their continuous operation is critical to the power system. Non-essential users are loads that are nice to have during normal operation, but can be shed in the event of a serious issue. A few non-essential users are air conditioning, toilets, and lighting.

Similarly, for MEA, there are ﬂight critical and non-ﬂight critical loads [27]. The

ﬂight critical loads are loads that must be operational at all times during a ﬂight

(i.e., safety lighting systems, de-icing systems, control systems, etc.) [58]. Non-ﬂight critical loads are sheddable in the event of a fault (i.e., overhead cabin lighting) [41,59].

Load shedding is a characteristic that will be present in AEA power systems, as it is an essential feature in the event that a serious fault occurs. Referencing the methods of load shedding on existing electric vehicles will help make implementation easier.

45 3.2 Challenges of Becoming All-Electric

In an eﬀort to transition from MEA to commercial size AEA, there are some technical hurdles that must be overcome. This section considers the current limitations, including battery chemistry and propulsion methods, and looks ahead to predict AEA.

3.2.1 Current Limitations

In the previous sections some predictions were made for AEA power systems, speciﬁcally related to architecture, voltage level, power requirements, and load characteristics. Reaching technological feasibility for AEA requires two main limitations be overcome. First, the propulsion sources on aircraft must become electric. Next, the energy sources used to power the electric propulsion must improve. The following two subsections consider these limitations in detail.

Propulsion Methods

Existing electric vehicles use an assortment of propulsion methods. The two main methods of propulsion are traditional propulsion and electric propulsion. Traditional propulsion refers to what is found on hybrid UAVs and MEA, where internal combustion engines and jet engines are used, respectively. While these methods of propulsion function well in their respective applications, attaining aircraft that are all-electric will require their replacement with electrical alternatives. Electric propulsion refers to the use of electric motors for propulsion [37]. The sources used to provide energy to the electric motors do not have to be electric for electric propulsion. As addressed

46 in Section 1.2, this is the common misconception with the term ’all-electric ship’,

where electric propulsion is used, but diesel engines generate the necessary energy.

The main limitation with respect to propulsion is the technical capabilities of the

electric motors needed to ﬂy the aircraft. Motor power requirements for single-isle

all-electric aircraft are estimated to be 1 MW and even higher for twin-isle aircraft

[29]. Weight remains a critical issue when selecting motors for these applications and

current industrial solutions are too heavy [32]. For this reason, distributed propulsion

will enable the splitting up of the thrust sources, allowing for smaller motors on board,

which is more technologically feasible [32]. Even in the face of electriﬁed propulsion,

the provision of electrical energy to these motors is a critical questions. Thus, battery

improvements are also needed to make AEA a reality.

Battery Chemistry

Three main limitations related to batteries are inﬂuence the transition to AEA, including energy density, weight, and cost. Each limitation is related to the battery being the primary vehicle energy source. Increasing power requirements, due to the electriﬁcation of primary vehicle energy sources, has driven the required energy storage capabilities of batteries higher [31, 36]. The ability of batteries to provide the necessary energy requirements lies in the energy density of the battery chemistry of choice. Figure 3.8 compares the energy density of common battery chemistries to gasoline, the fuel for traditional and MEA.

Li-ion, the common battery chemistry for 21st century applications, is only capable of providing 250 Wh/kg, well below that of gasoline. The gasoline energy density shown in Figure 3.8 accounts for the tank-to-wheel eﬃciency, with the unadjusted

47 Comparison of Energy Density for Existing Battery Chemistries 2000

1700 1700

1500

1000

500

Energy Density (Wh/kg) 250 200 100 40 60 0

LiPo Ni-Cd Ni-MH Li-ion Li-air Lead Acid Gasoline Battery Chemistry

Figure 3.8: Energy Densities for existing battery chemistries. Adapted from [9–11].

value being 13.2 kWh/kg [10,60]. With an energy density approximately seven times

higher than Li-ion, it is a much easier choice in current applications.

Closely related to the energy density limitation is the weight limitation. Because

Li-ion batteries have a much smaller energy density than gasoline, more batteries must be on board to provide the same amount of energy to the power system. As a result, the weight of the aircraft increases signiﬁcantly. Table 3.2 compares the battery weight for various electric vehicle applications.

The all-electric vehicle energy requirement in Table 3.2 references the battery capacity for the Tesla Model 3, which can travel approximately 310 miles on a charge [61]. To properly compare the other vehicles with the Tesla Model 3, an

48 Table 3.2: EV Energy Requirements and Resulting Total Battery Weight Vehicle Properties Vehicle Energy (kWh) Weight (kg) All-Electric Vehicle 75 300 All-Electric Ship 5,862 23,448 More Electric Aircraft 28 112 Battery More Electric Aircraft 9,807 39,228 All-Electric Aircraft 235,262 941,048

estimation of the travel time using the 75 kWh battery capacity and vehicle range must be made. AEV range is approximated using the testing standard J1634 set forth by SAE International [62]. The testing procedure includes a multi-cycle range and energy consumption test consisting of four different cycles. The overall testing procedure is complex, so for simplification purposes only the constant speed cycle will be considered. Using the steady-state speed of the constant speed cycle (55 mph) and the estimated range, a travel time of 5.63 hours can be approximated. This estimate is applied to the remaining vehicles to determine their resulting energy requirement and battery weight. The table includes two MEA, one traditional and another battery powered, which represent the specifications of an entire power system powered via batteries, not just the emergency situations and engine start-up of traditional

MEA [24]. The additional weight for traditional MEA is much less signiﬁcant than if the whole power system was powered through batteries, as suggested by the battery powered MEA. The battery powered MEA could be considered to be one of the intermediate steps toward AEA, a solution independent of the propulsion limitation previously addressed.

49 Energy requirements for AES vary signiﬁcantly depending on the size of the ship

under consideration. For this example, the energy storage system of the MF Ampere, the world’s ﬁrst fully electric battery powered ferry, was used for comparison [22].

It should be noted that earlier discussion in Section 1.2 suggested that the term all-electric ship is often misused to represent a ship with an all-electric power system and electric propulsion, but not powered by batteries. The MF Ampere, however, was able to electrify propulsion and the power system, and power all of it with batteries.

The power system of a typical large civilian aircraft was used to compute the energy requirements for MEA and AEA [34]. Inclusion of the propulsion thrust power requirement distinguishes the AEA from the MEA. The total battery weight was computed using the gravimetric energy density of the best available Li-ion batteries, shown to be 250 Wh/kg on Figure 3.8. The resulting battery weights illustrate the current shortcoming of Li-ion battery chemistry. An all-electric commercial airplane capable of flying for 5.63 hours would have a battery weight of 941,048 kg, much too heavy to fly. A reduction in the flight time would still result in a battery weight that makes the aircraft incapable of flight. Therefore, until battery chemistry improves, an all-electric commercial aircraft is a vision of the future.

Lastly, the cost of Li-ion batteries limits the transition from more electric to all- electric aircraft. The cost of Li-ion battery packs for electric vehicles is approximately

$600/kWh. Current forecasts predict this could drop to $200/kWh by 2020 [63].

Assuming the cost of battery packs for aircraft is comparable to that of electric vehicles, the total cost is much higher than when using gasoline. Ultimately, the trade-oﬀ between cost of fuel and cost of batteries plus the beneﬁts of going all-electric

50 will drive the feasibility of implementation. In the future, when battery chemistry

improves, the diﬀerence in cost will be much smaller giving AEA more overall beneﬁt.

3.2.2 Looking to the Future

In the coming years, the push toward the true AEA will continue. Improving battery chemistry is an essential step in reaching this goal. In 2009, a new battery chemistry, lithium-air, began to receive recognition around the world for its potential implementation in various electric vehicle applications [10]. Lithium-air batteries boast higher gravimetric energy densities than Li-ion. Some current estimates suggest the energy density for these batteries could reach 1700 Wh/kg [10]. Commercializa- tion of lithium-air technology could be the improvement in battery chemistry needed to enable AEA.

The major concern with lithium-air batteries is the cleanliness of the surrounding air needed for the reaction. Air-cleaning systems will need to be developed on a much larger scale if lithium-air is to become a feasible solution [11]. The increase in total weight of the battery, as a result of these systems, will further decrease the estimated energy densities. For now, Li-ion will continue to be the battery chemistry utilized in aircraft applications.

Even with its shortcomings, current Li-ion battery chemistry has enabled progres- sion toward an all-electric aircraft. The current trend is toward the development of smaller all-electric commercial planes before the larger commercial planes are electri-

ﬁed. It is easier to overcome challenges and technological limitations when initially addressed on a smaller scale. This is one of the main reasons why AEVs are widely available today, i.e., energy density of Li-ion batteries is not as big of an issue when

51 the power requirements are not as signiﬁcant. Eviation, an Israeli aviation com-

pany, has created prototypes for a new AEA capable of ﬂying 9 passengers up to 600

miles [64]. The plane could be commercially available as soon as 2021. While smaller

than potential AEA of the future, Alice would be a major developmental milestone.

Continued development of the all-electric power system in AEVs, AES, and MEA,

has played an important role in the push for all-electric aircraft. Speciﬁcally, dis-

tributed propulsion has and will continue to enable future growth. As mentioned, dis-

tributed propulsion splits up the sources of thrust on board the ship, or aircraft, with

the beneﬁt being improved speciﬁc fuel consumption and longer operating range [32].

Distributed propulsion will help enable electric propulsion on board aircraft, one of the

necessary steps before a commercial AEA can be realized. Implementation on MEA

results in better management of the electrical energy on board the aircraft. Improved

electrical energy eﬃciency will expedite the implementation of electric propulsion, as

the motors need to provide less power. For ships, distributed propulsion also enables

more eﬃcient use of the energy sources, resulting in a lower overall fuel consump-

tion [54]. The success of distributed propulsion thus-far, as seen on ships and MEA,

will certainly inﬂuence the design of power systems on board future commercial AEA.

3.3 Conclusion

The invention and advancement of power electronic technology paved the way for new developments in the aviation industry. MEA transitioned from a goal to the standard and have crafted the vision of the all-electric aircraft of the future.

AEA boast numerous beneﬁts over MEA, mainly increased eﬃciency, zero emissions, and lower costs in the long run. This paper has presented a comparison of power

52 systems of various electric vehicles, including the all-electric vehicle, all-electric ship,

and more electric aircraft with the goal of predicting the future of all-electric aircraft

power system development. Analysis of current power system characteristics and

architectures show that AES and MEA have similar power systems and indicate that

commercial AEA of the future will be similar to the power systems seen in these 21st

century electric vehicles.

Current battery technology and propulsion methods have been limiting factors in the push for commercial all-electric aircraft. Increasing power requirements due to electriﬁcation of energy sources has illuminated the shortcomings of Li-ion battery chemistry. Higher energy densities and lower costs are necessary before commercial all-electric vehicles can be developed.

Lastly, small-scale all-electric aircraft developments have been a sign of a bright future for all-electric aircraft, serving as a stepping stone toward commercial-sized

AEA. Overcoming current limitations on a small scale will validate the goals of AEA and give time for battery technology to continue to develop. The all-electric aircraft is certainly the plane of the future. It is only a matter of time before its impact is felt worldwide.

53 CHAPTER 4

Conclusions and Future Work

4.1 Conclusion

The work presented in this thesis has been centralized on two main facets of aircraft. First, considering aircraft on the small scale, a method of energy optimization and system modeling for hybrid UAVs was presented. Considering the energy optimization of small aircraft is helpful for identifying trends in optimization that could ultimately be expanded to larger applications. Using a dynamic programming algorithm, baseline results as to optimal usage of the energy sources were obtained. The energy sources onboard the hybrid UAV include an internal combustion engine and a battery. The optimal usage of these sources is the usage resulting in the minimiza- tion of fuel consumption. Simulation results showed that charging the battery just prior to a “dash” period, then proceeding to use the stored energy on the “dash” resulted in the lowest fuel consumption. The battery was charged again during the next “cruise” period according to the duration of the upcoming “dash”. It should be noted that this algorithm was written with the intent of optimizing the system under the most critical constraints, including battery limitations, engine power limits, and power balance equations. The nonlinear constraints imposed on the various

54 system components are left for the simulation model, which is used to validate the

results. This virtual UAV model was then presented and explained in detail. This

ﬁrst facet concluded with the results comparison, which illustrated that the baseline

results closely reﬂected the results when validated in the simulation model.

Second, the research on small-scale hybrid UAVs was extended to larger aircraft

applications, mainly AEA. The similarities between hybrid UAV power systems and

MEA power systems (i.e., the major diﬀerences being the power system complexity

and usage of energy sources) resulted in a study of AEA, considered to be the next leap

in commercial aircraft development. The AEA was deﬁned to be an aircraft relying

on electrical energy sources only, hence removing the need for onboard ICEs, which

use gasoline. This work considered power system architectures and characteristics of

AES, AEV, MEA, and hybrid UAVs with the goal of predicting the power system

structure onboard the AEA of the future. It was predicted that the power system

architecture onboard the AEA will be a radial distribution, similar to what is seen on

existing MEA. Four common architectures on MEA were given, and the centralized,

semi-distributed, or advances electric system architectures seem most reasonable for

future applications. Should the complexity of the all-electric power system increase,

a radial distribution more close to those seen on AES could be used.

The voltage levels onboard future MEA must be held below 1 kV for breakdown purposes, therefore the bus voltage will likely be 350 V DC or 540 V DC on the

AEA. Power requirements on future AEA will exceed those seen on MEA, with the addition of all-electric energy sources. Current power requirements are in the 1-5

MW range for MEA, but additional electriﬁcation could increase this quantity. The

55 load characteristics on the AEA will contain some recurring properties, a primary one

being the ability to shed loads in the event of a fault.

Finally, some limitations preventing the transition to AEA were given, including battery chemistry and propulsion methods. Distributed propulsion was predicted to be a major part of the AEA because increasing the number of onboard electric motors decreases the power requirements of each respective motor, which will allow for existing motor technology to be used. Regarding battery chemistry, lithium-air was proposed as a potential solution in the future, should the technology surrounding its implementation improve. While the current lithium-ion battery chemistry is the state-of-the-art, it does not have a high enough energy density for commercial AEA applications of the future. In light of these challenges, there are still advancements being made. Eviation, an Israeli aviation company, is in the process of designing an

AEA capable of ﬂying 9 passengers upwards of 600 miles. This application, while smaller than what is envisioned further in the future, is still a signiﬁcant step toward commercial AEA.

4.2 Future Work

The information presented in Chapter 2, regarding system modeling and energy optimization of a hybrid UAV, can be extended beyond its current level of application. The algorithm will be converted to a model predictive control (MPC) algorithm, which will help better characterize the nonlinear dynamics of the system. Addition- ally, the virtual UAV model will be dramatically improved. It was seen that several things were constrained or purposefully omitted from the model that would help make the simulations more representative of the real-world system. The simulations of this

56 thesis were intended to validate the methods presented, not provide the ﬁnal simula-

tion results most representative of the real-world system. Therefore, the future work

for this thesis focuses on making the virtual UAV model more representative. This is

accomplished by addressing three main things.

First, the battery charging and discharging circuits will be improved to more accurately reflect real-world systems. The charging and discharging rates were fixed in the results of this thesis. This is not always an appropriate assumption. There- fore, the updated model will allow the system to vary its charging and discharging rates. Second, system losses must be incorporated. Including system losses was not necessary in initial results because it is an additional complexity of the system that would have made result validation more difficult. Therefore, it was omitted. For the updated virtual UAV model, the resistance and inductance of the lines must be included for accurate representation. Additionally, power electronics converters and the engine components must have non-ideal efficiencies included. The assumption that all converters are %100 efficient is not realistic. Therefore, the adjusted value will accurately reflect industrial applications in the next iteration of the model. Finally, noise must be included in the system. The load profile does not have noise present in the results of this thesis, therefore adding some normally distributed noise to the existing profiles will help accurately model the system. The added noise should also consider disturbances present on the system during a mission (e.g., wind, directional changes, etc.).

In addition to the three main future works presented above, some additional test cases will be considered in the future. The addition of renewable energy sources onboard the hybrid UAV will allow for further study in an application that may be

57 relevant in the future. The renewable energy source would be photovoltaic (PV) cells.

Additionally, longer mission durations and diﬀerent load proﬁles will be consiered.

Analysis of extensions such as the ones mentioned here will help further characterize the system.

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