Perpetual flight with a small solar-powered UAV: Flightresults, performance analysis and model validation

Philipp Oettershagen, Amir Melzer, Thomas Mantel, Konrad Rudin, Thomas Stastny,Bartosz Wawrzacz, Timo Hinzmann, Kostas Alexis and Roland Siegwart

Autonomous Systems LabSwiss Federal Institute of Technology Zurich (ETH Zurich)

Leonhardstrasse 218092 Zurich

+41 44 632 7395philipp.oettershagen@mavt.ethz.ch

Abstract— This paper presents the design of the small-scalehand-launchable solar-powered AtlantikSolar UAV, summarizesflight results of a continuous 28-hour solar-powered flight thatdemonstrated AtlantikSolar’s capability for energetically per-petual flight, and offers a model-based verification of flight per-formance and an outlook on the energetic margins that can beprovided towards perpetual flight given today’s solar-poweredUAV technology. AtlantikSolar is a 5.6m-wingspan and 6.9kgmass low-altitude long-endurance UAV that was designed toprovide perpetual endurance at a geographic latitude of 45N ina 4-month window centered around June 21st. A specific designemphasis is robust perpetual endurance with respect to localmeteorological disturbances (e.g. clouds, winds, downdrafts).Providing the necessary energetic safety margins is a significantchallenge on small-scale solar-powered UAVs. This paper thusdescribes the design optimizations undertaken on the Atlantik-Solar UAV for maximum energetic safety margins. In addition,this paper presents the flight test results, analysis and perfor-mance verification of AtlantikSolar’s first perpetual endurancecontinuous 28-hour flight. The flight results show a minimumstate-of-charge of 40% or excess time of 7 hours during thenight. In addition, the charge margin of 5.9 hours indicatessufficiently-fast battery charging during the day. Both mar-gins exceed the performance of previously demonstrated solar-powered LALE UAVs. Another centerpiece of the paper is theverification of these flight results with the theoretical structural-,aerodynamics- and power-models that were developed and usedto conceptually design the UAV. The solar-power income modelis extended to take into account solar-panel temperature effects,the exact aircraft geometry and the current orientation and iscompared against flight results. Finally, the paper providesan analysis and overview into under what conditions and withwhich energetic margins perpetual flight is possible with today’sbattery- and solar-cell technology. A perpetual endurance win-dow of up to 6 months around June 21st is predicted at northernlatitudes for the AtlantikSolar UAV configuration without pay-load. A final outlook into first perpetual endurance applicationsshows that perpetual flight with miniaturized sensing payloads(small optical and infrared cameras) is possible with a perpetualflight window of 4-5 months.

TABLE OF CONTENTS1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12. SYSTEM OVERVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23. SYSTEM MODELING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34. MODEL VERIFICATION AND TRAINING . . . . . . . . . . . . . 45. FLIGHT RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

*This work was supported by the EU-FP7 research projects ICARUS andSHERPA as well as a number of project partners and generous individuals,see http://www.atlantiksolar.ethz.ch/

6. CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7BIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1. INTRODUCTIONSolar-electrically powered fixed-wing Unmanned Aerial Ve-hicles (UAVs) promise significantly increased flight en-durance over purely-electrically or even gas-powered aerialvehicles. A solar-powered UAV uses excess solar energygathered during the day to recharge its batteries. TypicalUAV applications such as industrial and agricultural sensingand mapping clearly benefit from this increased flight en-durance. However, given an appropriate design and suitableenvironmental conditions, the stored energy may even beenough to continuously keep the UAV airborne during thenight and, potentially, subsequent day-night cycles. This socalled perpetual flight capability makes solar-powered UAVsgreat candidates for applications in which data needs to becollected or distributed either continuously or on a large scale.Applications such as large-scale disaster relief support, mete-orological surveys in remote areas and continuous border ormaritime patrol would benefit in particular from this multi-day continuous flight capability [1].

Figure 1. The AtlantikSolar solar-powered low-altitudelong-endurance (LALE) UAV: a) After take-off b) exposingthe solar-cells and engaged spoilers c) after a night flight d)

during hand-launch in a Search-and-Rescue research missionwith the sensing and processing pod attached below the left

wing. Images a) and c) are from the continuous 28-hourperpetual endurance demonstration flight on June 30th 2015.

Recently, interest in employing large-scale (wingspanabove 20m), solar-powered High-Altitude Long-Endurance

1

(HALE) UAVs as atmospheric satellites - i.e. station-ary/loitering platforms e.g. for telecommunications relay -has peaked. Notable examples of this trend are Solara [2]and Zephyr, which has already demonstrated a continuousflight of 14 days [3]. In contrast, smaller scale solar-poweredUAVs are mostly designed for Low-Altitude Long-Endurance(LALE) applications. Though faced with the more chal-lenging meteorological phenomena of the lower atmosphere(clouds, rain, wind gusts or thermals), low-altitude UAVsprovide the advantages of higher resolution imaging withreduced cloud obstruction, lower complexity and cost andsimplified handling (e.g. through hand-launchability). Asa result, solar-powered non-perpetual-flight capable LALEUAVs aiming to reach flight times up to 14 hours are currentlybeing intensely studied in the industry [4], [5]. On the otherhand, research targeting perpetual endurance in small-scalesolar UAVs has been relatively sparse. Most research hasbeen focusing on conceptual design studies without exten-sive flight experience, e.g. [6]. Projects that have demon-strated perpetual flight are Cocconi’s SoLong [7], which per-formed a continuous 48-hour flight using solar power whileactively seeking out thermal updrafts, and SkySailor [8],which demonstrated a 27-hour solar-powered continuousflight without the use of thermals in 2008. However, theseUAS were mainly developed to demonstrate the feasibilityof perpetual flight for the first time, and do neither providesufficient robustness against deteriorated meteorological con-ditions (e.g. clouds or downwinds) nor the capability to flyperpetually with common sensing payloads. For example,the SkySailor UAV crossed the night with only 5.8% ofremaining battery energy.

Given the recent advances in solar-cell, battery- and sensing-technologies, this paper aims to provide an overviewover what improvements in perpetual flight robustnessand payload-carrying capacity can and have already beenachieved today, and what applications this may open up forsolar-powered perpetual flight-capable UAVs in the near fu-ture. We extend the work of [7], [8] by presenting the designand analyzing flight results of AtlantikSolar (Figure 1), asolar-powered LALE-UAV designed in [9] for robust multi-day autonomous operation that still allows the use of on-board optical and infrared sensor systems. More specifically,we will extend our previous work with

• a summary of incremental technical improvements inte-grated into the second AtlantikSolar (AS-2) in comparisonto our previous work.

• a discussion and analysis of flight results from Atlantik-Solar’s AS-2 first perpetual endurance flight of 28-hours.These results validate the design in our previous work andindicate significantly improved energetic safety margins(perpetual flight with up to 40% remaining battery energy).

• an extension of the models and conceptual design toolspresented in [8], [9] with more precise solar power inputmodels, which are crucial to accurately assess the perpetualflight capability of a solar-powered UAV.

• an outlook into what sensing payloads today’s technologyallows solar-powered UAVs to carry in perpetual flightmissions.

2. SYSTEM OVERVIEWThe solar-powered UAV considered in this paper is the At-lantikSolar UAV (Figure 1), a small-size low-altitude long-endurance (LALE) UAV developed to allow multi-day con-tinuous flight in a 4-month window centered around June21st

at a geographical latitude of 45◦N. A special design emphasiswas to provide sufficient energetic margins to deviations fromthe nominal operating point as well as to local meteorologicaldeteriorations such as clouds or vertical winds. The aerialvehicle is targeted towards large-scale operations includingmeteorological observations, aerial mapping or search-and-rescue support, and as such can be hand-launched for quickon-field deployment. Three AtlantikSolar aircraft have beendeveloped and constructed at ETH Zurich so far. The com-plete system design approach, and specifically the designcharacteristics of the first AtlantikSolar aircraft (AS-1), aresummarized in our previous work [9]. However, this paperdeals with the second revision of the AtlantikSolar aircraft(AS-2). The aircrafts are equal except for a set of technicaloptimizations that will be described below.

UAV Platform Design

All AtlantikSolar UAV airframes are of a conventional glider-like T-tail configuration with two ailerons, an all-movingelevator and a rudder. At a wingspan of 5.6m, the aircraft totalmass for AtlantikSolar AS-2 is now mtotal = 6.93kg dueto structural optimizations and a redesign and thus decreasein battery-mass. More specifically, 60 high energy densitylithium-ion battery cells (Panasonic NCR18650b, 243Wh/kg)are integrated into the wing spars for optimal weight dis-tribution and provide Emaxbat = 705Wh. The wings alsohouse 88 SunPower solar cells, which have been upgradedto SunPower E60 cells with a ηsm = 23.7% module-levelefficiency. The aircraft is driven by a RS-E Strecker 260.20brushless DC motor with kV = 400RPM/V that works at upto 450W electrical input power. It drives an all-steel planetarygearbox with four pinion gears and a 5:1 reduction ratio anda foldable custom-built carbon-fiber propeller with diameterD = 0.66m and pitch H = 0.60m. The gearbox had to beupgraded from previous designs to increase propulsion sys-tem reliability during multi-day flights. The characteristics ofAtlantikSolar AS-2 are summarized in Table 2.

Table 1. AtlantikSolar AS-2 design characteristics

Parameter ValueWing span 5.65mWing chord 0.305mLength 2.03mHeight 0.45mTotal mass* 6.93kgBattery mass 2.92kgMax. payload mass 1.0kgStall speed 7.9m/s*No payload

The avionics are centered around the Pixhawk PX4 Autopi-lot [10] - an open source and open hardware project initiatedat ETH Zurich. An ADIS16448 10-Degrees of Freedom(DoF) Inertial Measurement Unit (IMU), a u-blox LEA-6HGPS receiver, and a Sensirion SDP600 differential pressuresensor are used to estimate the aircraft attitude. The main433MHz medium-range telemetry link is complemented byan optional IRIDIUM-based satellite communication backuplink. The custom-made Maximum Power Point Trackers(MPPTs) and battery monitoring circuits provide detailed en-ergy flow information including solar power income Psolar,outgoing power Pout and battery charging power Pbat andin addition monitor the overall and cell-level charge statesto increase system safety. Four high-power indicator LEDsare installed to allow night operation. The low-level avionics

2

framework can be complemented by a sensor and process-ing unit - comprising an optical camera, an infrared cam-era, and an Intel Atom-based on-board computer runningUbuntu and the Robot Operating System (ROS) - that wasdeveloped at the Autonomous Systems Lab (ASL) and hasbeen demonstrated in multiple Search-and-Rescue researchapplications [11].

State Estimation and Control

State estimation is based on an Extended Kalman Filter(EKF) design that fuses data from the 10-DoF IMU, theGPS and the airspeed sensor to estimate position, veloc-ity, orientation (attitude and heading), QFF (pressure at sealevel), gyroscope biases, accelerometer biases and the windvector [12]. Sideslip angle and Angle of Attack (AoA) cansubsequently be derived from these estimates. The estimatorcan cope with prolonged GPS outage scenarios through animplemented airspeed sensor fallback mode and is optimizedto run efficiently on the on-board Pixhawk flight controller.

The underlying on-board control system employs cascadedPID controllers for rate and attitude control and a com-bination of Total Energy Control System (TECS) and L1-nonlinear guidance for position and altitude control [9]. Thisapproach offers the advantage of easy integration into the on-board Pixhawk flight controller due to the low computationaldemands. The control framework supports features such ascoordinated-turn control and passive use of energy from ther-mal updrafts to increase flight-performance and -efficiency,and the automatic deployment of spoilers in excessivelystrong thermal updrafts to increase flight safety.

3. SYSTEM MODELINGThe first comprehensive energetic models to assess and de-sign for performance and perpetual flight capability of solar-powered UAVs were developed by [8], [13]. In our pre-vious work [9], these models were extended and used toconceptually design the AtlantikSolar UAV. Figure 2 shows atypical model output. The sections below provide a summaryof these energy-based models, present the extensions to thesolar power income model that were implemented in thispaper, and summarize the main performance metrics thatarise during simulation and will later be used for performanceanalysis.

5 10 15 20 25 30 35 40 45 50 550

200

400

Po

wer

[W]

0

500

Ene

rgy

[Wh

]

Psolar

[W]

Pout

[W]

Ebat

[Wh]Texc

Ebat

tsr

Tcm

teq

Time[h]

max

SoCmin

Figure 2. Exemplary results and major performance metricsas obtained for an energetic simulation of a 48-hour

solar-powered UAV flight.

Energetic models for solar-powered UAVs

The simple quasi-static energy input/output-models used forsolar-powered UAVs typically neglect the UAV’s kinetic en-ergy and only model the electric energy Ebat in the aircraft

batteries and the altitude h as a representation of the po-tential energy Epot. These two state equations are forward-integrated to assess the energy flows and the energetic safetymargins that a solar-powered UAV provides with respect toperpetual flight. The combined state equation may be writtenas

dEbatdt = Psolar − Poutdhdt =

1mtotg

· (ηprop · Pprop − Plevel)(1)

In the special case of fully charged batteries(Ebat = Emaxbat ),we enforce dEbat/dt ≤ 0, and for level flight, we have dh/dt =0. The total required electric output power in equation (1) is

Pout = Pprop + Pav + Ppld. (2)

Pav and Ppld represent the required avionics and payloadpower respectively, but the main contribution usually comesfrom the required electric propulsion power Pprop. In the im-portant case of level-flight, Pprop = Plevel/ηprop, where ηpropincludes propeller, gearbox, motor, and motor-controller ef-ficiency. Using the assumption that the UAV operates at theairspeed requiring minimum aerodynamic level-flight power,we can state

Plevel =

(CD

C32

L

)min

√2(mtotg)3

ρ(h)Awing. (3)

Here, mtot = mbat+mstruct+mprop+msm+mav +mpldis the total airplane mass, where battery, structure, propulsionand solar module masses mbat, mstruct, mprop, msm areautomatically sized according to [9] and mav , mpld are userchoices. The local earth gravity is g, Awing is the wingarea, and ρ(h) is the local air density. The airplane lift anddrag coefficients CL and CD are retrieved from 2-D airfoilsimulations using XFoil [14], with CD being combined withparasitic drag from the airplane fuselage and stabilizers andthe induced drag

CD,ind =C2L

π · e0 · λ. (4)

Here, e0 ≈ 0.92 is the Oswald efficiency and λ the wingaspect ratio. The power income through solar radiation ismodeled as

P nomsolar = I ·Asm · ηsm · ηmppt, (5)

where our previous work [9] considers the exposed solarmodule areaAsm as a horizontally-oriented and thus aircraft-attitude independent area Asm = fsm · Awing with relativefill-factor fsm, module efficiency ηsm, and Maximum PowerPoint Tracker (MPPT) efficiency ηmppt. The solar radiationI = I(ϕ, h, t) is assumed to be a function of the geographicallatitude ϕ, the altitude h, and the current date and local timet, and is modeled as in [15]. This model is extended in thesection below.

Extension of solar-power income model

Instead of modeling the aircraft’s solar modules as one singlehorizontal surface, this paper takes the current orientation ofthe aircraft (roll, pitch and yaw) into account and uses a moreelaborate geometric representation of the aircraft as depictedin Figure 3. The set of N solar module-covered areas Aismeach contains the cell configuration and relative orientationof the surface with respect to the airplane as a property.In lateral direction, this is the wing dihedral angle ∆φdih.

3

In longitudinal direction, every solar module area Aism ismounted at an additional solar cell pitch angle ∆θAi withrespect to the wing chord line due to the wing upper surfaceprofile. The relative pitch orientation between the aircraft’slongitudinal or x-axis (which is approximately aligned withthe IMU longitudinal axis) and the wing chord line is addedon top and is designated as ∆θwing . Using the specificnumber of solar cells on each surface, nAism , the geometricproperties above, and the global aircraft attitude in the formof the Euler-angles roll φ, pitch θ and yaw ψ we retrieve arepresentation for the sun-exposed area of each surface as

Aism = f(φ, θ, ψ,∆φdih,∆θwing,∆θAism , nAism). (6)

Combining the individual exposed surface areas via the sim-ple sum

Asm =

N∑i

(Aism) (7)

returns the sun-exposed surface area of the whole aircraft asrequired for equation (5). The exact model parameters usedin this paper are given in table 2.

NED HORIZON PLANE

dih

A4

A3A1

A6

A5

A2

ΔθAism

θ

ΔθAi+1

smΔθwing

AiAi+1

NED HORIZON PLANE

Figure 3. Geometric representation of AtlantikSolar withinthe solar power model. The set of sub-surfaces and their

respective orientation to each other and to theNorth-East-Down horizontal plane in lateral direction (top)

and arrangement in longitudinal direction(bottom)

In addition to geometric considerations, this paper also mod-els the solar module efficiency losses occurring at tem-peratures above the Standard Test Conditions (STC) ofTSTC=25◦C using the simple linear relationship

ηsm = ηSTCsm ·

(1 − cl · (Tsm − TSTC)

). (8)

An initial value for the solar module efficiency under STCis given in table 2. The loss factor cl = 0.3%/K is extractedfrom datasheets. The instantaneous solar module temperatureis approximated using the linear relationship

Tsm = Tamb + ∆Tmax ·PsolarPmaxsolar

. (9)

Here, Tamb is the ambient temperature measured by theairplane, and ∆Tmax is the temperature difference betweensolar module and ambient temperature that was measured inflight approximately at maximum insolation (i.e. at Pmaxsolar ≈260W ). Note that further effects such as shading of the solarmodules e.g. by the horizontal tail plane, the influence ofcell-cell interaction and the protection diodes, the influence

of movements of the solar-cell covered ailerons, and changesin the efficiency of the MPPTs as well as the solar moduleswith respect to the insolation level and angle of incidence arenot modeled yet but are considered candidates for followingresearch.

Table 2. Solar model parameters (AtlantikSolar AS-2)

Parameter Value Source∆φdih 6.0◦ Aircraft specs∆θwing 5.7◦ Aircraft specs

∆θAism∆θA1,A3,A5 = −0.5◦∆θA2,A4,A6 = 9.4

◦ Measured

nAismnA1,A3,A4,A6 = 12nA2,A5 = 20

Aircraft specs

ηSTCsm 23.7% Measured in lab [16]ηmppt 95% Estimated

Performance metrics for solar-powered UAVs

A typical simulation result from a forward integration ofthe model described in the sections above, together withperformance parameters that can be retrieved from it, can beseen in Figure 2. The battery energy Ebat and the equivalentmeasure state of charge (SoC) show the expected behaviorthat the batteries are charged during the day, remain at aplateau while Psolar > Pout, and are discharged during thenight. The relevant performance metrics that can be extractedfrom such a simulation are:

• Minimum state-of-charge SoCmin, expected at t = teq inthe morning, and defined as SoCmin = min(SoC(t))

• Excess time Texc, a safety margin indicating for how muchlonger the aircraft could stay airborne after a successfulday/night cycle ifPsolar = 0 on the second day, i.e. definedas

Texc =Ebat(t = teq)

P nomout

∣∣∣Psolar(t>teq)=0

. (10)

• Charge margin Tcm, a safety margin indicating how muchunused charging time the aircraft has available after fullycharging its batteries before the discharge begins, i.e. de-fined as

Tcm = T (Ebat = Emaxbat ). (11)

4. MODEL VERIFICATION AND TRAININGThe power output and power income models used for per-formance prediction were verified in flight tests with theAtlantikSolar AS-2 UAV. A second step involves training themodel parameters based on that flight test data to allow moreaccurate performance predictions.

Required Output Power

The required power for level flight in equation (2) is de-termined as in [9], i.e. through direct measurement ofthe Pout = f(vair) relationship. The airplane is set intoautonomous loitering mode and performs an airspeed sweepfrom vair = 7.4...13.5m/s at constant altitude. The measuredpower curve is depicted in Figure 4. The minimum requiredtotal electric power for level flight is Pout = 39.7W at anairspeed of vair = 8.3m/s. This power curve is a direct inputfor the perpetual flight simulation.

4

True Airspeed [m/s]

7 8 9 10 11 12 13 14

Po

ut [W

]

30

40

50

60

70

80

90

Figure 4. Power curve, i.e. the required level power Poutvs. airspeed, measured in constant-altitude loiter mode andaveraged over T=200s intervals for each plotted data point.

Received Solar Power

To provide reliable results on the charging performance ofa UAV, the solar power income models of section 3 needto be verified against the measured power income duringflight. Figure 5 shows such a direct comparison from the earlymorning to shortly before noon for one AtlantikSolar AS-2 autonomous loitering test flight. The comparison showedthat deviations of up to 5% in the of total amount of solarenergy collected throughout the day exist when assuming themodeling parameters of table 2. The total amount of collectedsolar power, however, needs to be modeled as accurately aspossible to correctly predict the aircraft battery charge state.To train the used model, the model parameters of table 2were therefore adapted. More specifically, the solar moduleefficiency ηSTCsm - which was only known from lab tests doneon the raw modules without them being installed on theAtlantikSolar AS-2 wings - was adapted to ηSTCsm = 22.53%to fully align the measured and modeled accumulated solarpower income.

6 7 8 9 10 11

Po

we

r [W

]

0

100

200

Psolar

measuredP

solar

theor,total

Local time [hours]

8 8.05 8.1 8.15 8.2

Po

we

r [W

]

0

100

200

Psolar

measuredP

solar

theor,totalP

solar

theor,total (+5deg)

Local time [hours]

9.4 9.45 9.5 9.55 9.60

100

200

a)

b) c)

Figure 5. Theoretically expected vs. measured solar powerincome for the early morning to the early noon of an

AtlantikSolar AS-2 test flight. For better readability, theoriginal flight data has been filtered with a two-sided moving

average filter with semi-window length nf = 200 points.

The achieved fit of the power income is visible in Figure 5a.Although the accumulated solar power income is correct, themeasured power income is slightly lower than the theoreticalone at low income power, and higher than the theoretical oneat high income power. Excerpts from the curve in Figure5a are shown in Figure 5b (early morning) and Figure 5c

(later morning). Figure 5a clearly indicates that the averagemeasured power income at that time is below the theoreticallyexpected one. One reason for this behaviour can be inferredfrom the flight logs. These show that, especially in low ra-diation conditions, the MPPTs exhibit sub-optimal maximumpower point (MPP) tracking behavior. The MPPT efficiencyηmppt is also known to decrease at lower income power. Athird explanation is related to the solar module efficiencyηsm, which decreases in weak irradiation conditions [17]. Ofcourse, all other effects described in section 3 have not beenmodeled, which can also contribute to model/measurementdiscrepancies. Integrating precise models for all these factorsis a research target for the final solar power income model.

In addition, visible in Figure 5b and Figure 5c, the amplitudeof the oscillations due to the aircraft loitering is larger for themeasured power income. One reason are the uncertaintiesin determining the exact current pitch attitude of the solarmodule surfaces, which may be falsified by errors in themeasurement of ∆θwing and ∆θAism , but also by attitudeestimation errors of the aircraft pitch angle θ itself. Figures5b and 5c therefore both include a theoretically expectedpower income for an additional ∆Θadd = 5◦, which alreadyprovides an excellent fit. Note that in general, the modelcaptures small-scale changes e.g. due to instantaneous roll-angle and pitch-angle changes very well. It remains tobe investigated what specific error in the total pitch angleestimation might be the reason for the amplitude differences.

Overall, the quality of the fit means that the theoretical powerincome model is a very good quantitative fit for flights insimilar - i.e. very good - irradiation conditions. However,if large deviations from this training point arise, for examplecaused by significant cloud cover that decreases the powerincome over the full day and results in the battery not evenbeing fully charged, then the model will overestimate theactual solar power income. This is one reason why - untilfurther research has been undertaken - our conceptual designand simulation environment applies additional safety marginson the minimum SoC under which we assume that perpetualflight is possible (section 5).

5. FLIGHT RESULTSA total of 60 test flights with a flight duration of 225 hourshave been completed with the three versions of AtlantikSolarproduced at ETH Zurich so far. The flight test programhas recently culminated in the first completely solar-poweredday/night flight of 28 hours and 18 minutes with AtlantikSo-lar AS-2. The flight set a new Swiss endurance record forsolar-powered UAVs and proved AtlantikSolar’s perpetualendurance capability. This perpetual endurance capabilityhas only been shown by one smaller (in terms of mass) solar-powered UAV [8] before.

Flight results from 28-hour continuous solar-powered flight

The 28-hour continuous solar-powered flight was performedon June 30th and July 1st 2015 at a geographical latitudeof φlat = 47.6◦ in Rafz, Switzerland. Flight conditionsthroughout the whole flight proved to be excellent, with onlyoccasional and very marginal high-altitude cloud cover andmoderate winds up to 4m/s. Figure 6 shows an excerpt oflogged flight data. The airplane was launched at 11:14am onJune 30th at 58% state of charge. While launch and landingwere performed in manual RC-control, overall 98% of theflight were performed in autonomous mode. As common

5

for LALE-UAVs aiming for perpetual endurance, the flightstrategy was to fly low at constant altitude for as much timeas possible to reduce power consumption, and ascend onlywhen the batteries are fully charged at the end of the day tostore potential energy in altitude.

The analysis of the required output power Pout in Figure 6shows strong fluctuations for both Pout and the aircraft alti-tude h after launch- and from late morning to early eveningin general. This is caused by significant time-varying thermalup- and downdraft activity. As described in [9], in this casethe UAV will passively gain altitude and thus energy fromthermal updrafts until it reaches the maximum user-specifiedaltitude hmax = 720m AMSL. However, the average powerconsumption over the sections with strong thermal activity(e.g. until t = 20h on the first day) is still P avrgout = 67.8Wand thus around 50% more than measured in calm air (Figure4). The effect of thermal downdrafts therefore exceeds that ofthermal updrafts. The resulting slower charge process needsto be considered as a function of location and time whenanalyzing solar-powered UAV performance flights and wasthus included as a parameter in our modeling environment.During the night, the air is naturally calmer, and we retrieveP avrgout = 41.8W - mostly taken from the battery - from 9pmto 6am. This represents less than 5% error with respect to themeasured data in Figure 4.

The overall validation and training of the solar power modelshas been performed in section 4, and the direct comparisonwith flight test data from the 28-hour flight confirms thefindings: The generated solar power Psolar is slightly lowerthan the theoretically expected value at low power income(morning and evening), and slightly higher at high powerincome (around noon). Figure 6 also shows the actual batterycharging power Pbat, and when the batteries are fully charged(t = 13.73h on day 1, t = 12.33h on day 2), it is evidentthat the MPPTs limit the effective Psolar to reduce batterywear by charging slowly at high state of charge. This is incompliance with the standard CC/CV charging method forLi-Ion batteries, which suggests an exponentially decreasingcharge power similar to Figure 6. It can also be seen thatwhile the MPPTs limit the battery charge power Pbat, theywill still supply the full output power Pout if available. Theplots in addition show that SoC(t = tlaunch + 24h) >SoC(t = tlaunch), and thus the perpetual flight capabilityis proven.

The exact performance metrics achieved during the 28-hourflight are determined from the data in Figure 6 and aresummarized in Table 3. The minimum state of charge duringthe whole flight is SoCmin = 41%. This is a significantimprovement over previous solar-powered UAS designs suchas SkySailor, which reached SoCmin = 5.8% [8]. Theminimum state of charge translates to an excess time ofTexc = 7.04h. The charge margin Tcm is 5.9 hours, andthereby provides significant margin against e.g. decreasedsolar power input caused by clouds. Note that the time untilthe batteries are at SoC = 90% - which would still allowperpetual flight in these conditions - is much lower becausethe charge rate is decreased after that to decrease battery wear.The resulting charge margin until SoC = 90% is thus alsoincreased. The measured performance metrics correspondwell with the performance metrics predicted during the initialconceptual design of AtlantikSolar [9]. However, note theinitial design was carried out for AtlantikSolar AS-1 and withslightly different flight data.

The performance metrics predicted by our conceptual design

and simulation tools agree very well with the measured data.We retrieve SoCmin = 39% and Texc = 6.28h, i.e. errors of4.8% and 10.8% respectively. This close agreement is foundprimarily because the state of charge and excess time are -given that Psolar = 0 during the night - nearly exclusivelya function of power consumption Pout and maximum batteryenergy Emaxbat , where we learned the former parameter fromextensive flight testing and have a rather precise estimate ofthe latter. The relative errors regarding the charge marginsTcm and T 90%cm are 16.7% and 9.2% respectively. In additionto being harder to measure from flight test data, the chargemargin is also harder to model, as it is prone to both localdeviations in input and output power as well as to modelingerrors in both.

Table 3. Comparison of performance metrics for the 28hour flight test and the corresponding simulation.

Metric Flight Sim. CommentSoCmin 41% 39%Texc 7.04h 6.28h (with t1eq=7.59hTcm 5.9h 6.89h (t2eq=19.67h, tfc=13.77hT 90%cm 8.18h 8.93h (t

2eq=19.67h, t

90%fc =11.49h

Overall, and especially compared to previous designs suchas [8], the flight results show significantly improved energeticmargins both with respect to local deteriorations in powerincome (e.g. clouds) as well as power consumption (e.g.horizontal and vertical winds). Given that the performancemetrics Texc and Tcm from [9] were largely confirmed,the robustness analysis for perpetual flight in these localmeteorological deteriorations is still valid with only minormodifications. As a last step, the improved energetic marginsmotivate an outlook into multi-day flight applications of suchperpetual flight capable low-altitude solar-powered UAVs.Figure 7 provides an overview over the minimum state ofcharge versus the day of year DoY assuming flight at anorthern latitude of φlat = 47.6◦N . Perfect conditions bothwith respect to solar power income (i.e. clearness CLR =1.0, or equivalently Psolar = Pnomsolar) and required output(Pout = Pnomout ) are assumed for the calculation.

Day of year [-]

50 100 150 200 250 300

Min

imu

m S

tate

of

Ch

arg

e [

-]

0

0.1

0.2

0.3

0.4

Critical SoC

June 21st

July 21st

Aug. 21st

Sept. 21stNo Payload

mpld

= 0.2kg, Ppld

= 3W

mpld

= 0.3kg, Ppld

= 5W

Figure 7. Predicted minimum state-of-charge (SoC) as afunction of the day of the year for AtlantikSolar AS-2 at a

latitude of φlat = 47.6◦N . The critical SoC is anexperience-based limit below which the batteries should not

be discharged for safety reasons.

It is shown that without payload, perpetual flight is possiblein a six-month window around June 21st assuming that the

6

Po

we

r[W

]

0

100

200

300P

solar

theor, totalP

solar

theor, diffuseP

solar

measuredP

out

measuredP

bat

measuredS

oC

[%]

0

50

100

SoCBat

[%]

Vo

lt.[

V]

20

25U

Bat [V]

Att

itu

de

[°]

-200

0

200roll

pitch

yaw

Local time [hours]

15 20 25 30 35 40

Altitu

de

[m]

400

600

800Altitude[m]

Figure 6. Flight log data for the AtlantikSolar AS-2 28-hour perpetual endurance flight on June 30th 2015. The recordedpower income and output data is recorded at 2Hz and was time averaged with a two-sided moving average filter with

semi-window size 1000 samples to reduce the effects of aircraft attitude-induced oscillations.

aircraft shall not discharge its batteries below a certain criticalSoC level SoCcrit. This safety margin is crucial, first, toaccount for remaining uncertainties in the modeling process(sections 3 and 4), but on the other hand to allow for sufficientremaining battery energy and, thus, propulsion power e.g. ifa go-around during landing is required. Initial applications ofmulti-day solar-powered low-altitude flight may involve long-endurance aerial sensing, observation and mapping missions.For that purpose, Figure 7 also considers two miniaturizedsensing payloads: A simple optical camera with integratedon-board image recording or, alternatively, an atmosphericsensing payload (mpld = 200g, Ppld = 3W ) and a small-scale infrared camera such as described in [11] with either on-board recording or additional live-image downlink (mpld =300g, Ppld = 5W ). The first application allows continuousmulti-day flight in up to a window of 5 months and thelatter in a 4 month window around June 21st. Note that theendurance provided by such solar-powered UAVs even whenperpetual flight is not possible is still sufficient to provide fulldaylight-flight capability and is thus sufficient for most large-scale mapping tasks [11].

6. CONCLUSIONThis paper has presented design improvements of the small-scale hand-launchable solar-powered AtlantikSolar UAV, hassummarized flight results of a continuous 28-hour solar-powered flight that demonstrated AtlantikSolar’s capabilityfor energetically perpetual flight, and has offered a model-based verification of flight performance and an outlook onthe energetic margins that can be provided towards perpetualflight given today’s solar-powered UAV technology. The

flight results show that a significant increase in the perpetualflight energetic margins has been achieved with respect to ear-lier low-altitude perpetual flight-capable UAVs. This increasein energetic margins comes from the progress in battery-and solar-cell technology, but also from the thorough de-sign optimization and testing performed for the AtlantikSolarUAV. The improved performance is the basis for more robustperpetual flight in solar-powered LALE UAVs. However, itshould be noted that even the high achieved energetic marginsof Texc = 7.04 hours and Tcm = 5.9 hours are not enough tosustain perpetual flight in severe meteorological conditionssuch as strong winds or cloud cover. Nevertheless, theresults motivate a look into first applications of solar-poweredlow-altitude perpetual flight. While perpetual flight withoutpayload is possible in a 6-month window around June 21st forAtlantikSolar AS-2, applications with a 200g optical cameraor atmospheric sensing payload or a 300g infrared camerasystem would still provide a four and five months perpetualflight window in optimal conditions. These applications forperpetual flight-capable solar-powered UAVs could be of sig-nificant importance in simple monitoring, mapping or sensingtasks including border and maritime patrol or meteorologicalsensing in the medium future.

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[3] QinetiQ, QinetiQ files for three world records for itsZephyr Solar powered UAV, QinetiQ Press Release,2010.

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[6] S. Morton, L. Scharber, and N. Papanikolopoulos,“Solar Powered Unmanned Aerial Vehicle for Contin-uous Flight: Conceptual Overview and Optimization,”in IEEE International Conference on Robotics andAutomation (ICRA), 2013.

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[9] P. Oettershagen, A. Melzer, T. Mantel, K. Rudin,R. Lotz, D. Siebenmann, S. Leutenegger, K. Alexis,and R. Siegwart, “A Solar-Powered Hand-LaunchableUAV for Low-Altitude Multi-Day Continuous Flight,”in IEEE International Conference on Robotics andAutomation (ICRA), 2015.

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BIOGRAPHY[

Philipp Oettershagen received a M.Sc.in Aerospace Engineering from Califor-nia Institute of Technology in 2010 and aDiploma (Dipl.-Ing.) in Aerospace En-gineering from University of Stuttgart,Germany, in 2011. He is currently aresearch assistant and PhD-student atETH Zurichs Autonomous Systems Lab,where he is leading the AtlantikSolarUnmanned Aerial Vehicle (UAV) project.

Besides the conceptual design and system engineering ofhigh-performance solar-powered UAVs, he is interested inusing in-flight estimations of the often highly cluttered anddynamic meteorological environment for trajectory planningand UAV decision making.

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