Energy Optimization of a Hybrid Unmanned Aerial Vehicle (UAV)

105 Pages·2017·1.59 MB·English

Energy Optimization of a Hybrid Unmanned Aerial Vehicle
(UAV)
Thesis
Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in
the Graduate School of The Ohio State University
By
Danielle Meyer, BS, BA
Graduate Program in Electrical and Computer Engineering
The Ohio State University
2018
Thesis Committee:
Dr. Jiankang Wang, Advisor
Dr. Mahesh Illindala

ABSTRACT
Unmanned Aerial Vehicles (UAV) have continued to receive attention from corpo-
rations and governmental agencies due to their wide range of potential applications
and hybrid nature. More Electric Aircraft (MEA) promise many benefits (e.g., re-
ducedweight, decreasedfuelconsumption, andhighreliability)andtheirdevelopment
continues to be the trend. Hybrid UAVs are an ideal prototype to implement con-
ceptsofaircraftelectrificationduetotheirsmallsizeandtheDCnatureoftheirpower
systems. However, papersaddressingtheenergyoptimizationUAVelectricpowersys-
tems fail to consider the importance of high accuracy and computational speed. This
thesis proposes an energy optimization method to enhance the energy durability of a
UAVthroughanovelapproachintegratinganoptimizationformulationandadetailed
UAV simulation model, with physical circuitry characteristics. This approach allows
for increased computation efficiency while still capturing physical system constraints
experienced during real world flight, which are complex and highly nonlinear due to
aerial, thermal, and electrical dynamics. Optimization formulations created within
this work are based on dynamic programming and moving-horizon model predictive
control (MPC). The efficacy of this method is proven on a realistic UAV system.
Within the MPC formulation, various charge strategies are implemented and fuel
consumption is calculated to provide insight into the trade-offs inherent within the
UAV system, wherein battery discharging is required for high demand dash periods,
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but additional charge can only be supplied via increased output engine power. That
is, minimal fuel consumption must be considered in light of the need for non-optimal
outputenginepowertochargethebatterysuchthatatotalmissioncanbecompleted.
Algorithmic considerations regarding horizon size for MPC and algorithmic enhance-
ments, considering random loads and renewable generation capacity on-board the
UAV are presented. These results regarding enhanced algorithmic elements provide
insight into the capability of the algorithm to function within a real-time environment
and the benefit of solar arrays to provide additional generation. Using MPC as the
optimizationtechniqueofchoiceallowsforthedevelopmentofanalgorithmcapableof
handling both missions with a deterministic load and within online implementations,
as deterministic cases represent a downsized problem where algorithmic considera-
tions can be studied and iterated to reach satisfactory online implementation. While
this thesis approaches the problem from the perspective of UAV design, i.e., opti-
mization for a deterministic load profile, the algorithmic enhancements provided here
represent initial steps towards online implementation.
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Dedicated to my parents
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ACKNOWLEDGMENTS
This thesis would not have been possible without the support of my advisor and
mentor, Dr. Jiankang Wang, and my parents.
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VITA
2009 - 2016 .................................BS, Electrical and Computer Engineer-
ing, The Ohio State University
2009 - 2016 .................................BA, German, The Ohio State Univer-
sity
2016 - present ..............................MS, Graduate Research Associate,
Department of Electrical and Com-
puter Engineering, The Ohio State
University
PUBLICATIONS
Research Publications
D. Meyer, J. Choi, J.K. Wang, ”Increasing EV public charging with distributed
generation in the electric grid,” 2015 IEEE Transportation Electrification Conference
and Expo (ITEC), Dearborn, MI, 2015, pp. 1-6.
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.
D. Mao, D. Meyer, J.K. Wang, ”Evaluating PEV’s impact on long-term cost of
grid assets,” 2017 IEEE Power Energy Society Innovative Smart Grid Technologies
Conference (ISGT), Arlington, VA, pp. 1-5.
In Press. D. Meyer, J.K. Wang, ”Integrating Ultra-Fast Charging Stations within
the Power Grids of Smart Cities: A Review,” IET Smart Grid.
In Press. Ziran Gao, D. Meyer, J.K. Wang, ”Visualizing the Impact of PEV
Charging on the Power Grid,” 2018 IEEE Transportation Electrification Conference
& Expo (ITEC), Long Beach, CA, pp. 1-5.
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FIELDS OF STUDY
Major Field: Electrical and Computer Engineering
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TABLE OF CONTENTS
Page
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Vita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
Dynamic Programming - Nomenclature . . . . . . . . . . . . . . . . . . . . . . xii
Model Predictive Control - Nomenclature and Symbols . . . . . . . . . . . . . xiii
Chapters:
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Prior Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 6
2. Characterization of the UAV System . . . . . . . . . . . . . . . . . . . . 8
2.1 UAV System Representation . . . . . . . . . . . . . . . . . . . . . . 8
2.2 UAV Power Demand . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 Energy Management Considerations . . . . . . . . . . . . . . . . . 12
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3. Dynamic Programming for UAV Energy Management . . . . . . . . . . . 13
3.1 Dynamic Programming . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 Application of Dynamic Programming to UAV Energy Management 15
3.2.1 Algorithmic Implementation . . . . . . . . . . . . . . . . . . 18
3.3 UAV System Model . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4. Model Predictive Control for UAV Energy Management . . . . . . . . . . 25
4.1 Model Predictive Control (MPC) . . . . . . . . . . . . . . . . . . . 25
4.2 Application of MPC to UAV Energy Management . . . . . . . . . . 27
4.2.1 Algorithmic Development . . . . . . . . . . . . . . . . . . . 30
4.3 UAV System Model . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.4.1 Energy Management for a 50cc Engine . . . . . . . . . . . . 34
4.4.2 Energy Management for a 28cc Engine . . . . . . . . . . . . 42
4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5. AdditionalAlgorithmicConsiderations-MPCforUAVEnergyOptimization 57
5.1 The Importance of Horizon Length within the MPC for UAV Energy
Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.2 MPC in the Presence of Load Uncertainty . . . . . . . . . . . . . . 62
5.3 Integrating Renewable Generation . . . . . . . . . . . . . . . . . . 71
5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6. Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . 80
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
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