Power management of SUAS

Active Power ManagementSystem for an UnmannedAerial Vehicle Powered bySolar Cells, a Fuel Cell,and Batteries

BOHWA LEESEJIN KWONKorea Advanced Institute of Science and TechnologyDaejeon, Republic of KoreaPOOMIN PARKKEUNBAE KIMKorea Aerospace Research InstituteDaejeon, Republic of Korea

200 W class, low-speed, long-endurance unmanned aerial vehicle(UAV) that employs solar cells, a fuel cell, and a battery pack as itspower sources is considered. This study applies an active powermanagement method that directs each individual source to generatethe appropriate power, depending on the power supply and demand,instead of the passive method in which the power sourcesirresponsibly generate power, depending on their characteristics.The power management system (PMS) under active managementdetermines the power output from each source. The flight test of theUAV with a PMS onboard is conducted for 3.8 h. The active PMSverifies its own feasibility as it successfully keeps the power sourceswithin their proper operational bounds and maintains a targetstate-of-charge of 45%, while responding to the various conditionsassociated with the power required. In addition, through acomparison of flight test results with a power simulation of thepassive method, the usefulness, advantages, and disadvantages of anactive power management method over a passive method areinvestigated.

Manuscript received July 16, 2013; revised December 17, 2013; releasedfor publication March 2, 2014.

DOI. No. 10.1109/TAES.2014.130468.

Refereeing of this contribution was handled by M. Veerachary.

Authors addresses: B. Lee, S. Kwon, Korea Advanced Institute ofScience and Technology, Department of Aerospace Engineering, 335Gwahango, Yuseong-Gu, Daejeon, 305-701 Republic of Korea, E-mail:([email protected]). P. Park, K. Kim, Aero Propulsion Division, KoreaAerospace Research Institute, 115 Gwahangno, Yuseong-Gu, Daejeon305-333 Republic of Korea.

0018-9251/14/$26.00 C 2014 IEEE

I. INTRODUCTION

The mainstay of aircraft propulsion has historicallybeen the internal combustion engine, exemplified by gasturbines and reciprocating engines, which use fossil fuel.However, the need for alternative propulsion devices thatuse environmentally friendly energy is rapidly increasingbecause of the exhaustion of fossil fuel resources, which,in turn, brings soaring fuel costs and the tightening ofcontrols on environmental by-products, such as carbondioxide emissions and noise. Furthermore, recentadvances in the performance and weight of electric powersources, such as fuel cells, solar cells, batteries,supercapacitors, and associated components, have greatlyincreased their practical applications and spurred variousstudies associated with employing electric propulsion fortransportation [1, 2].

Recent forecasts suggest that due to their superiorendurance, unmanned aerial vehicles (UAVs) powered bythese electric sources have the potential to expand theircommercial applications by substituting as communicationand broadcasting satellites. This is in addition toperforming military ground observation, meteorologicalinvestigation, and stealth reconnaissance [3].

Keeping pace with the world trend, the KoreaAerospace Research Institute (KARI) is conductingstudies on electric-powered UAV systems. The electricaerial vehicle-2 (EAV-2) is one of the electric-poweredUAV systems developed by KARI. The EAV-2 employsthree distinctive electric power sources: solar cells, a fuelcell, and a battery pack.

The power from the three different sources can besupplied to the propulsion system using a passive methodor an active method. Under a passive method, poweroutput from each source is manually determined,depending on the characteristics of the power source. Incontrast, an active method has full authority to control thepower sources through a power converter [4]. As a result,the efficiency of power usage and the safety of the powersystem are not as optimized under a passive method asthey can be under an active method. Furthermore, hybridpower source performance may be unnecessarily limitedby one of those three components [5, 6]. However, thereare benefits to using a passive method. As the passivemethod does not require any power converters orcontrollers, it is simple and light. In addition, it can avoidthe collateral power loss associated with powerconversion. Therefore, the passive method is more widelyused in small UAVs, while the active method is usuallyused in land-based hybrid electric vehicles (HEVs) thatemphasize power efficiency and are less concerned withweight reduction than the aircraft [710].

Various studies have been conducted that employactive power management for hybrid systems using solarcell/battery, fuel cell/battery, and solar cell/fuel cell/batterysystems, as found in [1116]. Shiau et al. [11] designed asolar panel/battery hybrid system and developed a solarpower management system (PMS) that consisted of

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 50, NO. 4 OCTOBER 2014 3167

TABLE ISpecifications of the EAV-2

Property Value

Weight Structure 7.5 kgPropulsion system 10.5 kgPayload 0.5 kg

Wing/airfoil Wing area 1.92 m2Wing span 6.9 mAspect ratio 20.0Chord 0.32 mAirfoil shape SG 6043Horizontal airfoil shape NACA 0010Vertical airfoil shape NACA 0012

Motor/propeller BLDC motor AXI 5320/34Propeller type Folding, tractorPropeller size 0.5334 0.3302 m

Operating condition Stall speed 9.8 m/sCruise speed 12.1 m/s

maximum power point tracking, battery management, andpower conversion stages for an experimental UAV. Jianget al. [12, 13] proved that for a fuel cell/battery hybridsystem with a DC-DC converter, active control is afeasible way of managing the fuel cell power under themaximum efficiency strategy, maximum power strategy,and adaptive control strategy (a combination of the twoprevious strategies). Jiang [14] demonstrated throughnumerical simulation that for a photovoltaic panel/fuelcell/battery hybrid system with appropriate DC-DCconverters, the control system satisfies the loadrequirements, while ensuring operation under the variouslimitations of electrochemical components, such as thebattery overcharge limit and the fuel cell current limit.Becherif et al. [15], as well as Chen and Khaligh [16]investigated the modeling and control of a photovoltaicpanel/fuel cell/battery hybrid system in which the controlobjective is to maintain a constant DC bus voltage. Theirsystem was modeled through MATLAB/Simulink, andvery simple controllers were designed [15].

This study investigates changes in the outputcharacteristics of each power source due to the passive andactive power management strategies of a solar cell/fuelcell/battery hybrid system connected in parallel.

Power sources with the same operational voltage arefabricated or purchased for the passive method, and a PMSthat controls the power sources is developed for the activemethod. Through flight tests using the PMS, this studyverifies the conceptual feasibility of the active PMS forUAVs and investigates the usefulness, advantages, anddisadvantages of active management over passivemanagement.

II. HYBRID ELECTRIC POWER SYSTEM

The main features and dimensions of the EAV-2 areshown in Table I. The EAV-2 is a low-speed,long-endurance UAV with a gross weight of 18.5 kg and a

wingspan of 6.9 m. The total cruise power required,including operational power for the flight controlcomputer (FCC) and payload is 200 W, with a peak valueof 1.0 kW. To increase endurance, the EAV-2 is notequipped with landing gear. Instead, a launcher installedon an automobile is used for takeoff. The onboard FCCenables the EAV-2 to conduct autonomous flights inaccordance with missions transmitted from the groundcontrol station (GCS).

It is essential for the power sources employed in theEAV-2 to minimize their weight. However, as the protonexchange membrane (PEM) fuel cell could not bedeveloped in time, it has been selected from commerciallyavailable lightweight products. The selected PEM fuelcellthe 200 W rated Aeropak from Horizon EnergySystemshas its stack and chemical hydride-based fuelcartridge combined into a single unit.

The fabricated 4.3 Ah battery pack consists of sevenEnerland PQ4550XQ Li-ion polymer unit cells connectedin series. The nominal voltage of the battery pack is25.9 V. The batteries are protected by a batterymanagement system (BMS), which informs the PMS ofthe battery packs operational status.

Sunpower A300 crystalline silicon solar cells areinstalled on the main wing of the EAV-2 over an area of1.17 m2. Although the total area of the wing reaches1.92 m2, the available area is limited to avoid the largecurvature and control surfaces of the leading edge. Thesolar cells are embedded into the wing with a projection ofless than 0.1 mm, so as to minimize the drag in flight.Half-cells are employed to accommodate the airfoilcurvature. Fifty-two cells are serially connected to form astring that matches the battery voltage, and the wing areaaccommodates three strings connected in parallel.

Every power source is selected or fabricated to havethe same operation voltage, which ranges from 21 to 33 V.The power sources operate under the same voltage, whichenables them to be connected directly to the power bus

3168 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 50, NO. 4 OCTOBER 2014

Fig. 1. PMS hardware architecture.

without converters in between. This is the passive method,in which the output from each power source is manuallydetermined by matching the voltage without a powercontroller [6].

III. DEVELOPMENT OF THE PMS

Fig. 1 illustrates the architecture of the active PMS thatemploys the PMS developed in this study. Thisarchitecture is basically the passive management systemaugmented with the PMS. In contrast with the passivesystem, which connects the power sources directly to thebus, active management connects the power sourcesthrough the PMS, which controls each source individually.

A. Hardware

The PMS consists of a main processor board, DC-DCmodules, and an interface board. The main processor usedin the main processor board is the TC1797 from InFineon,which is a TriCoreTM-based 32-bit microcontroller [17].Three types of communication interface are available fromthe main processor board: RS232, Universal Serial Bus(USB), and Inter-Integrated Circuit (I2C). The RS232 isused to communicate with the fuel cell and FCC, while theUSB is used to communicate with a personal computer forPMS data monitoring and remote program download.Uploading and debugging of the program is done via thejoint action test group interface of the TC1797 processor.The BMS communicates with the PMS through the I2Cinterface. This I2C communication is governed by afield-programmable gate array. The PMS is also equippedwith a micro-SD card to store communication andinput/output data.

The power output of each source is controlled in termsof the terminal voltage, which should be maintainedseparately from the bus voltage. Therefore, three 86 WDC-DC buck-boost converters and a 300 W DC-DCbuck-boost converter are installed for the solar cells andfuel cell, respectively. The system employs a LTC 3789buck-boost switching regulator from Linear TechnologyCorporation [18].

The main processor of the PMS controls the solar cellDC-DC converter to make the solar cells operate at their

Fig. 2. Average power conversion efficiency of each converter by load.

maximum power output by the perturbation andobservation method [19], while the fuel cell converterbalances the output and required load of the fuel cell; itlowers the DC-DC converters input voltage (which isequal to the fuel cells output terminal voltage) to increasethe output from the fuel cell when the load required for thefuel cell increases. Fig. 2 presents the average powerconversion efficiency of the 300 W class convertor byload. It is confirmed from the figure that the average powerconversion efficiency for input voltages of 2036 Vexceeds 95% when the DC-DC converter terminal voltageis 26 V.

The battery pack is directly connected to the power buswithout a DC-DC converter. Therefore, the power outputfrom the battery pack is adjusted to make up the differencebetween the power required and the PMS-controlled totaloutput from the solar cells and fuel cell. Furthermore,because the bus is directly connected to the battery pack,the bus voltage is determined by the battery pack outputvoltage.

The interface board measures the efficiency of theDC-DC converters and the current and voltage of theinput/output terminals of the converters and load. Thepower consumption for the measurement is reduced byemploying a noncontact current measurement method.The speed of the brushless DC (BLDC) motor is measuredusing the back-electromotive force zero-crossingtechnique on the power line without a sensor.

The fabricated PMS weighs 620 g in total, and itsdimensions are 193 (L) 122 (W) 48.5 (H) mm.

B. Power Control Logic

The control algorithm is developed using themodel-based development (MBD) technique. Thealgorithm development and verification time isconsiderably reduced by using MATLAB/Simulink formodeling, and MBD is applied to the algorithm tominimize software errors that might creep into thecontroller. The active power management method

LEE ET AL.: ACTIVE POWER MANAGEMENT SYSTEM FOR AN UNMANNED AERIAL VEHICLE 3169

employed by this study keeps each power source withinoptimal operating limits. At the same time, it activelyassigns power to each source to reserve a certain level ofbattery power for emergencies.

The battery should reserve an appropriate level ofpower during the flight for the following reasons: first, assolar cells and fuel cells have very slow dynamics, theyare not as suitable as batteries for supplying bursts ofpower and attenuating power fluctuations. Second, in thecase of a malfunction of the DC-DC converter or failure ofthe PMS, the battery pack becomes the only power sourcefor the system. Another favorable effect of reservingbattery power is a decrease in required fuel andcorresponding reduction in the weight of the fuel cell.Once the battery is charged to an appropriate reserve level,the extra power can be used for cruising so that the systemrequires less power from the fuel cell.

The consequences of an electrically powered UAVrunning out of reserve batter power are far more severethan those faced by an HEV on the road. When the powersupply to the FCC of a UAV is insufficient, the system willfail to supply power to all of the equipment required forflight, resulting in loss of control over the vehicle. TheUAV may need to terminate a mission early because itcannot deal with unexpected abrupt turbulence during theflight. Vehicle loss or even a catastrophic crash can alsoresult.

The study in [20] presents the active powermanagement algorithm for a hybrid system consisting ofthree types of power sources.

To maximize the efficiency of the power sources, thecontrol algorithm of the PMS assigns the output from eachpower source according to the power required for the load(motor and FCC) and state of charge (SOC). The powercontrol accounts for each power source individually. In thecase of the solar cells, which absorb energy from the Sunand do not require onboard fuel, the output depends onenvironmental conditions. Therefore, the PMS employsthe output from the solar cells as the base load andcontrols the outputs from the other sources to match thepower required.

In the case of the fuel cell used in an EAV-2, themanufacturer-specified minimum sustainment load is50 W. Operating the fuel cell below this level for a longtime results in a reduction of effluence, followed bycooling of the pipe lines, and their eventual clogging withcoagulated remnant fluid. Eventually, the fuel cell stopsoperating. Therefore, the fuel cell should be operated at apower level greater than 50 W. In addition, although thepeak power of the fuel cell exceeds 220 W, it should not beexposed to the highest load level for a long time in caseself-shutdown occur due to overload. Therefore, the PMSsoftware maintains the fuel cell power within the boundsof 50 to 180 W.

In light of the previous discussion, the powerdistribution algorithm that governs the output of eachpower source is constructed, as shown in Fig. 3. The figureindicates that the output from each power source is

Fig. 3. Power management algorithm with six control sectors identified.

divided into sectors, depending on the power required(x-axis) and SOC (y-axis).

Both axes are divided into three sections in bytes. Thesections of the x-axis are 0 10, 0 20, and 0 40,and the sections of the y-axis are 0 01, 0 02, and 0 04. Each of the six control sectors is identified as thedecimal value of the sum of the corresponding byte valuesof an axis section. For example, the emergency sector isidentified as 65 because it corresponds to 0 40 on thex-axis and 0 01 on the y-axis, which together yield thesum of 0 41 or 65 in decimal value.

In each sector, the PMS assigns the value of the rightside to the value of the left side, which represents theoutput of each source that covers the propulsion powerrequired. In sectors 19 and 20, where the power from thesolar cells can cover all the power required, the extrapower is used to charge the batteries (negative output).Once the batteries are fully charged, the system voltageincreases so that only the power required is extracted fromthe solar cells. The increase in system voltage also yields areduction in output from the fuel cell. If the fuel celloutput drops below the specified level, it is shut off toprevent operation under an excessively low load.

In general, the power required in flight exceeds thepower available from the solar cells. Therefore, the wholeoutput from the solar cells is consumed for power required,and on top of that, the solar cells should be augmentedwith other power sources. This implies that control sectors19 and 20 can occur only when the vehicle is taxiing orgliding. Independently from the PMS, the BMS breaks theconnection of the fully charged battery pack to prevent itfrom overcharging. The PMS is set to stop charging thebatteries if the high level of SOC (SOCH) reaches 95%,before the BMS breaks the connection.

In sectors 33 and 38, which mainly correspond to thecruise condition, the primary power source is the fuel cell,which has the highest energy density. To maintain an SOClevel of at least 45%, the fuel cell output should becontrolled to compensate. Control of the SOC level in


Fig. 4. Deployment of power sources and PMS.

flight is carried out using the constrained thermostatcontrol strategy, with an adding factor defined as (1).

Pfc = Preq Psc + Pchg,m (SOCL SOC), (1)where Pchg,m is the maximum charge power (W), Pfc is thefuel cell power (W), Preq is the required power (W), Psc isthe solar cell power (W), and SOCL is the low level ofstate of charge (%).

Sectors 65 and 70 correspond to the cases that requirehigh peak power, such as takeoff and climb. In thesesectors, power is extracted from the batteries, which havethe highest power density. While the SOC of the batterypack exceeds the SOCL, the battery pack supplies thepower required less the outputs from the solar cells andfuel cell, so long as the power required falls within thebattery packs peak power limit. When the battery chargedrops below the SOCL, an emergency status is triggered,whereby the flight mode is changed to reduce the powerrequired and restore the SOC level of the battery pack.

The EAV-2 requires 33 Wh for a 10-min descent at200 W, which corresponds to an SOC of 30%. To secure aminimum inflight SOC of 30% or above, the target SOCLis set at 45%.

VI. COMPARISION BETWEEN ACTIVEAND PASSIVE METHOD

A. Flight Test Validation of Active Power Management

It takes roughly 175 s for the PMS to detect voltagesand current, run the algorithm to distribute the power, andfinish the control command. The control sectors decimalidentifier, which classifies the input and output conditionsof the power sources and the corresponding powerdistribution algorithms, is calculated in real time andstored in a variable named Power Distribution at 10 Hz.

Fig. 4 shows the fuel cell, battery pack and PMS asthey are installed inside the fuselage. The EAV-2s testflight lasted for 3.8 h after takeoff at 12:00 on November27, 2012, from the Goheung Aerospace Center, Korea. Theaircraft was manually controlled from takeoff until 12:55,at which point the autonomous flight mode was engaged.

Fig. 5. Experimental results for EAV-2s whole flight test with PMS:Psys, total power; Isc, solar cell current; Ifc, fuel cell current; and Ibt,

battery current.

Once the aircraft reached the target altitude of 400 m, itperformed a loiter, forming a circle with a radius of 1 kmwith respect to the specified center. During the loiter, theFCC controlled the aircraft to maintain altitude and speedin the circular trajectory. Because the throttle is one of thecontrols adjusted by the FCC, power consumptioncontinually varies, depending on the environmentalconditions, including wind during the flight.

The time histories of the flight conditions and outputsfrom the power sources during the flight are presented inFig. 5. In contrast to a ground test, a flight test is sensitiveto turbulence in the air. As a result, the powerconsumption fluctuates even when the airspeed andaltitude are kept constant, as seen in a later stage of theflight. The results show that the PMS properly respondedto changes in the power conditions and successfullycontrolled the outputs from the power sources. Thisallowed it to supply the power required undercontinuously changing flight conditions, including takeoffand climb, which require significant amounts of power.Fig. 6 presents time histories of the heading and outputfrom the solar cells during the test flight.

Heading is the direction in which an aircraft istraveling. A heading of 0 deg indicates that the aircraft ispointing due north. The regular pattern of the heading,which appears after engagement of the autonomous flightmode, indicates that the aircraft is in a stable rotationalflight. The output from the solar cells oscillates insynchronization with the heading and has a period of8 min. The variation in solar cell output is not due tocloudsno cloud was spotted in the sky during theflightbut due to the periodic change in relative anglebetween the Sun and the solar cells on the wing inaccordance with the rotation of the aircraft.

The dynamic behavior and power distribution of thepower sources before and after takeoff are shown in Fig. 7.The 20 W of power required before takeoff is theminimum operating power for the onboard electricequipment, including the FCC. Because the SOC is below


Fig. 6. Experimental results and relationship between heading andoutput from solar cell.

Fig. 7. Experimental results: power distribution during takeoff: Psys,total power, Psc, solar cell power; Pfc, fuel cell power; and Pbt, battery

power.

95%, the solar cells are engaged to supply the powerrequired, and the extra power is used to charge the batterypack. At this moment, the fuel cell is shut off to prevent itfrom operating under a low load. Therefore, this statecorresponds to power control sector (Power Distribution)19. When the power required jumps to 80 W, it exceedsthe power available from the solar cells, and the PowerDistribution simultaneously switches to 38. Because thefuel cell is set to operate only when it needs to generate50 W or more, no power comes from the fuel cell at thisstage. The battery pack compensates for the powershortage.

During takeoff, the total power available from the solarcells and fuel cell is less than the power required, and theSOC exceeds 45%. Therefore, power control sector 70corresponds to this stage. For a segment with such a highpower requirement, the battery pack makes up theshortage in power. The time history shows that the PMSsuccessfully maintained the fuel cell output at 180 W evenwhen the system voltage plunged due to complete

Fig. 8. Experimental results: power distribution during cruise.

discharge of the battery. This verifies that the output fromeach power source is actively controlled before and aftertakeoff via the predefined control logic for the sector.

Fig. 8 shows the dynamic behavior and powerdistribution of the power sources during the cruise. As therequired cruise power varied from 150 to 400 W,depending on environmental conditions, such as wind, thePower Distribution frequently shifted among 33, 65, 38,and 70. The primary power source is the fuel cell, and anyextra power is used to charge the battery when the SOClevel is low, as in sector 33. In control sector 38, the PMScompares the target value and current value of the SOC. Ifthe current SOC is higher, the PMS reduces the outputfrom the fuel cell and extracts more power from thebattery. However, if the current SOC is lower, the fuel cellis instructed to generate more power than required tocharge the battery. When the total power available fromthe solar cells and fuel cell is lower than the powerrequired and the SOC is below 45%, the aircraft enters theemergency sector with a Power Distribution of 65. Oncethe aircraft enters an emergency state, the GCS screendisplays an emergency message. If this conditionpersists, the flight mode should be changed to one thatrequires lower power to conserve the SOC of the battery.The emergency state occurred during the test flightat 14:52.

The dynamic behavior and power distribution of thepower sources during the descent are shown in Fig. 9. Thedescent power required varies roughly from 80 to 200 W.This range of variation is smaller than the range in cruise.In this particular test flight, the solar cells could barelygenerate power during descent because the solar elevationangle was too small. Therefore, only the fuel cell andbattery were available. Because the batterys SOC wasbelow 45%, the Power Distribution was either 33 or 65,depending on the power required. Note that the fuel celloperated at 180 W, its maximum power available, and thebattery SOC recovered as the extra power was used tocharge the battery pack.


Fig. 9. Experimental results: power distribution during landing.

B. Comparison with Passive Method by PowerSimulation

The previous study in [20] showed that the powersimulation of the EAV-1, a small UAV that employs a fuelcell and batteries as its power source, properly traces thecharacteristics of power from each source captured in theflight tests. This study also applies a power simulation toinvestigate the characteristics of power from each sourcein the passive system. The simulation is preferable overthe actual flight as a source of power characteristics forcomparison, because it can import the power requiredhistory recorded from the previous flights, which cannever be duplicated through another flight. Because thepower available from solar cells varies depending onweather conditions and the aircrafts attitude, as shown inFig. 6, the simulation imports measured solar cell outputsfrom the flight for the active power management tests.

The fuel cell and battery models are established inMATLAB/Simulink. This study employs the mathematicalmodel of the dynamic behavior of a fuel cell stack from[21, 22]. The theoretical voltage of a unit cell is 1.2 V forall operational current values. However, the actual voltageaccounts for losses of Vact, Vohmic, and Vcon are as follows:

Vfc = ENernst Vact Vohmic Vcon, (2)where ENernst is the Nernst potential (V), Vact is theovervoltage due to activation (V), Vohmic is the overvoltagedue to ohmic loss (V), and Vcon is the overvoltageconcentration loss (V). ENernst and the voltage losses aredetermined using (3)(6):ENernst = 1.229 0.85 103(T 298.15)

+ 4.3085 105T [ln(PH2 ) + 0.5 ln(PO2)] (3)

Vact = [1 + 2T + 3T ln(CO2) + 4T ln(Ifc)] (4)

Vohmic = Ifc(Rm + Rc) (5)

Vcon = Bf ln(

1 JJmax

), (6)

Fig. 10. Simulation and experimental results: fuel cell IV and Pcurves.

where CO2 is the concentration of oxygen in the catalyticinterface of the cathode (mole per cubic centimeter) andRm is the equivalent membrane resistance to protonconduction ().

A fuel cell stack is a set of serially connected unitcells, and its voltage is the unit voltage multiplied by thenumber of cells, such that

Vstack = n Vfc. (7)The dynamic voltage of the fuel cell is determined asfollows:

dd

dt= 1

CIfc + 1

d, (8)

where fuel cell electric time constant is given as

= C(

Vact + VconIfc

). (9)

The dynamic behavior of the fuel cell voltage reflects thedynamic voltage as follows [23]:

Vfc = ENernst Vohmic d. (10)A complete model of the fuel cell dynamics accounts forthe effect of detailed mechanisms, such as liquid waterdynamics, membrane hydration, and humidity. However,due to difficulty in accounting for those mechanisms in asystem-level model, this study employs the simplifieddynamic behavior of the fuel cell stack shown in (10). Theparameter of the fuel cell stack used in this study is shownin Table II. The generated hydrogen is first stored in asmall tank that maintains 45.5 bar, and then it is sent tothe stack via the pressure regulator. Therefore, ideally, thegenerated hydrogen behaves identically to hydrogenprovided from a pressurized reservoir. The molar fractionsof hydrogen and oxygen are assumed to be constantduring the simulation. The fuel cell model is implementedin MATLAB/Simulink. The results of both the simulationand the performance test data are shown in Fig. 10.

The kinetic battery model is also implemented inMATLAB/Simulink, using its library model to estimatebattery performance based on the performance test data


TABLE IIParameter for Fuel Cell Modeling

Symbol Description Value

PH2 Partial pressure of hydrogen 1 atmPO2 Partial pressure of oxygen 0.21 atmn Number of cells 35C Capacitance 1.5 FRc Equivalent contact resistance to electron conduction 0.0003 Bf Constant dependent of the cell type and its operation state 0.7 VJ Actual cell current density 100 A/cm2Jmax Maximum cell current density 900 A/cm21 Empirical coefficients of activation over voltage 0.853 Empirical coefficients of activation over voltage 7.0E-54 Empirical coefficients of activation over voltage 0.5E-4

TABLE IIIParameter for a Battery Pack in Simulink Block

Description Value

Battery type Lithium ionNominal voltage 24.83 VRated capacity 4.3 AhCapacity at nominal voltage 3.5 AhFully charged voltage 29.5 VInitial SOC 100%Internal resistance 0.35 Exponential zone voltage 24.96 VExponential zone capacity 3.4 Ah

Fig. 11. Simulation and experimental results: battery packcharacteristic curves.

from the manufacturer [24]. The battery pack parametersused in the Simulink model are shown in Table III. Thesimulation results and the product performance test resultsof the power resource are compared in Fig. 11. Thesimulation generally agrees with the test results, with littledeviation under 25 V in charge.

In the simulation, the power available from theintegrated power source models and the power requiredfrom the PMS flight test are balanced out. The behavior ofthe bus voltage (battery voltage) and fuel cell output under

Fig. 12. Simulation (passive) and experimental (active) results: passivevs. active fuel cell power comparison.

passive and active management are compared in Fig. 12.Note that under active power management, the fuel cell isprevented from operating under extremely high or lowload conditions, as the fuel cell is shut off at a low loadand used only up to 180 W.

The battery voltage operational bounds range from23.3 to 29.3 V under passive management. As the fuel cellis directly connected to the power bus and its voltage isdetermined by the batterys voltage, passive managementis not able to control the decrease in voltage as the batteryruns out. Therefore, the SOC of the battery cannot bemaintained at a proper level under passive management.On the other hand, the battery voltage operational boundsrange from 25 to 29.3 V under active management. In theactive management system, the terminal voltage of thefuel cell is controlled independently from the batterysvoltage. This results in an operation with a generallyhigher output than under passive management. Therefore,active management can maintain the SOC of the battery atan appropriate level.

The batterys SOC and the output of passive and activemanagements can be compared by the time histories, asshown in Fig. 13. The SOC of active management begins


TABLE IVExperimental (Active Method) and Simulation (Passive Method) Results: Used

Energy Comparisons

Active MethodEnergy PMS Input Terminal PMS Output Terminal Passive Method

Total energy 784.6 Wh 747.7 Wh 747.7 WhSolar cell energy 239.9 Wh 209.0 Wh 209.0 WhFuel cell energy 511.9 Wh 505.9 Wh 473.9 WhBattery energy 32.75 Wh 32.75 Wh 64.79 WhMinimum SOC 31.0% 23.9%

Fig. 13. Simulation (passive) and experimental (active) results: passivevs. active battery power comparison.

to deviate from that of passive management at 12:55.Active management maintains an SOC of 45% through arepeated cycle of charge and discharge. However, as thepassive management mainly uses the power from thebattery to cover fluctuating power requirements, thebatterys SOC under passive management drops down to23.9%. The SOC recovers to a limited extent during thedescent, in which the power required is decreased and asmall amount of extra power is left available to charge thebattery. However, the aircraft flies with an SOC levelbelow 30%, which is supposed to be the minimum, for atotal of 0.54 h, from 15:16 until the end of the flight.

The power consumptions of each source under activeand passive management are compared in Table IV. TheSOC is maintained at a minimum of 30% under the activepower management method, but it drops below 30% underthe passive method. To maintain the minimum SOC, theactive power management consumes more power from thefuel cell and less power from the battery than the passivepower method. Active management yields an energydiscrepancy of 36.9 Wh between the input terminal andoutput terminal of the PMS. This energy drives powerconversion and operation of the power devices during theflight test. It accounts for 4.7% of the total energy of thesystem, but it can be reduced by employing ultralighthigh-efficiency power devices and converters. In contrast,

the passive method can avoid power loss due to powerconversion because it does not need any power convertersor power controllers. However, the passive methodrequires the same operational voltage range for all powersources, thus imposing restrictions on selection andfabrication of suitable sources.

V. CONCLUSION

This study investigated an active power managementmethod to individually control power sources inaccordance with a strategy that depends on the status ofpower available from each source. This method wasapplied to the 200 W class electrically powered UAV,which employs solar cells, a fuel cell, and batteries as itspower sources. In contrast to passive management, inwhich the output from each power source depends on thecharacteristics of the source, the active managementemploys a PMS that determines the amount of power fromeach source to maintain the SOC level at 45%. The PMSfollows a power distribution algorithm that accounts forany conditions that the aircraft encounters. The test flightEAV-2 with a PMS onboard lasted for 3.8 h after takeoff at12:00 on November 27, 2012, from the GoheungAerospace Center, Korea. The cruise power requiredvaried within the approximate range of 150 to 400 W. Inresponse to the various conditions associated with thepower required, these tests verified that the active PMSmanaged each power source within the appropriateoperating limits and, at the same time, actively controlledthe output from each power source to maintain thespecified SOC throughout the flight time.

To compare the usefulness, advantages, anddisadvantages of the active method and the passivemethod, a power simulation of the passive method wasalso conducted. The simulation results showed that thepassive method failed to maintain an appropriate batterypower level under some operating conditions. Thisobservation suggests that active power managementshould be engaged to ensure a minimum level of reservebattery power and meet the requirements of the powersources, particularly when the power sources havecomplicated operation specifications. It is expected forfuture UAVs to have even more efficient systems that canpredict weather conditions and power consumption duringa specified mission and control the power output of each


source in advance. The active method of the presentsystem yielded a collateral power loss of 4.7% more thanthe passive method. This loss will be further reduced byemploying ultralight high-efficiency power devices andconverters.

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Bohwa Lee (M13) received M.S. degree in mechanical engineering from the KoreaAdvanced Institute Science and Technology, Daejeon, Korea, in 2006. In 2005, shejoined the Aero Propulsion Division, Korea Aerospace Research Institute, Daejeon,Korea.

Sejin Kwon received his Ph.D. degree in aerospace engineering from the University ofMichigan, Ann Arbor, in 1991. He has been a professor since 2000 in the Department ofAerospace Engineering, Korea Advanced Institute of Science and Technology, Daejeon,Korea.

Poomin Park received his Ph.D. degree in mechanical engineering from KoreaAdvanced Institute Science and Technology, Daejeon, Korea in 2001. In 2003, he joinedthe Aero Propulsion Division, Korea Aerospace Research Institute, Daejeon, Korea.

Keunbae Kim received his Ph.D. degree in mechanical engineering from ChungnamNational University, Daejeon, Korea, in 2005. In 1994, he joined the Aero PropulsionDivision, Korea Aerospace Research Institute, Daejeon, Korea.


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