A Novel Energy Harvester for Powering Small Uavs: Performance Analysis, Model Validation and Flight Results

sensors

Article A Novel Energy Harvester for Powering Small UAVs: Performance Analysis, Model Validation and Flight Results

Rocco Citroni 1,* , Franco Di Paolo 1 and Patrizia Livreri 2

1 Department of Engineering, University of Rome Tor Vergata, 00100 Roma, Italy; [email protected] 2 Department of Engineering, University of Palermo, 90128 Palermo, Italy; [email protected] * Correspondence: [email protected]

Received: 4 March 2019; Accepted: 10 April 2019; Published: 13 April 2019

Abstract: The proposed work aims at exploring and developing new strategies to extend mission parameters (measured as travel distance and mission duration (MD)) of a new class of unmanned vehicles, named Micro Air Vehicles (MAVs). In this paper, a new analytical model, identifying all factors, which determine the MAV power consumption, is presented. Starting from the new model, the design of a nanoarray energy harvester, based on plasmonics nano-antenna technology is proposed. The preliminary study was based on a 22,066,058 22,066,058 62,800-dipole rectenna array × producing an output power level of 84.14 mW, and an energy value of 2572 J under a power density of 100 mW/cm2 and a resonant frequency of 350 THz as input conditions. The preliminary analytical results show a possible recharge of an ultra-fast rechargeable battery on board of a MAV and an MD improvement of 16.30 min.

Keywords: energy harvester; MAV; power consumption model; nano-antennas; dipole rectenna array; perpetual ﬂight

1. Introduction Considered as the eyes and ears behind the enemy lines, today Unmanned Air Vehicles (UAVs) are playing an important role in global society due to their ability to carry a payload and to their compact dimensions. These air vehicles can operate and monitor complex scenarios where the presence of onboard human pilots is unnecessary [1]. Today, thanks to the miniaturization and compact dimensions of the electronics components, a new class of unmanned vehicles, named micro air vehicles (MAVs), hand-launched (their weight being less than 100 g) and powered by electric engines, is emerging. Currently, MAVs can operate where larger UAVs cannot, such as inaccessible and contaminated areas. Despite some benefits, several technological limits must be overcome. Typically, the mission parameters of these vehicles are limited to a few meters and minutes. The loss of a MAV’s performance is due to energy restrictions of the battery on board. Considering a large area to be monitored as fast as possible, with the current technology, a single drone will be not able to land, recharge the battery and take off again in a few minutes. Therefore, to make this unmanned technology more attractive it is mandatory to find a solution for the problem of how to increase the mission parameters [2–4]. The authors suggest two directions, which are extensively detailed below. First, a model identifying all factors that determine the power consumption of a drone with a negative impact on mission parameters is developed and implemented. Starting from the proposed model, mission parameters can be increased with an energy harvesting (EH) technique. This technique is capable of extending the mission parameters considering a renewable, intermittent source of energy [3,5–10]. The Sun is the most important renewable energy source to generate the electricity

Sensors 2019, 19, 1771; doi:10.3390/s19081771 www.mdpi.com/journal/sensors Sensors 2019, 19, 1771 2 of 21 necessary for any human activity. The Sun radiates energy mainly in the form of visible light, and infrared radiation with small amounts of ultraviolet [11,12]. Solar radiation is usually considered as shortwave infrared (SWIR), radiation typically classified as belonging to the 0.7 to 2.5 µm wavelength range [13]. The wavelengths in the visible region are currently harvested using solar cells based on the photovoltaic (PV) effect. However, this technology has several problems for unmanned applications. Quantum technologies are strongly dependent on daylight, which makes them also sensitive to weather conditions. Only photons charged with an energy equal to the silicon bandgap can be harvested efficiently. This effect decreases the solar cell efficiency to only 21% [11]. A low-cost alternative to PV effect is a novel harvester constituted by nanosized rectennas (submicron antennas combined with ultra-high frequency diode rectification) based on plasmonics technology [14,15]. This technology would theoretically allow a more efficient, direct conversion of electromagnetic radiation (visible light frequency range) into electricity, above the Shockley and Queisser (S&Q) limit. In contrast to solar cells, which are limited by material bandgaps, this technology is based on electromagnetic waves that induce a time-changing current at the same frequency of the wave incident on the antenna surface. This time-changing current can be rectified in order to produce a direct current (DC) power and to power supply an external load. For this reason, an ultra-high speed rectifier based on the tunnel effect placed at the feed point of a nano-antenna is mandatory [16,17]. Therefore, compared to quantum technology, this novel harvester, which can be seen as a small nano-generator, represents an alternative way to supply power to a MAV for longer times, even when the Sun is heavily shaded. These new devices, thanks to the development of new techniques, such as electron beam lithography and atomic layer deposition (ALD), are able to assure the level of miniaturization required for our purposes [16]. This significantly reduces the size and weight of the battery, thus improving the mission parameters. This paper is arranged as follows: Section2 presents a model based on a commercial quadcopter identifying all factors that determine the power consumption of a drone with a negative impact on mission parameters. Section3 describes the design and simulations of the novel harvester based on plasmonics technology. The harvester circuit impedance has to be matched with the load (a typical DC/DC converter for harvesting applications) to optimize the power transfer. Therefore, in this section, an optimal matching between harvester and load is proposed simply by tuning the impedance of the harvester with that of the load. The harvested energy stored in a buffer is described in Section4. Concluding remarks are given at the end of Section5.

2. Several Scenarios Proposed for Electric Power Consumption Model In this section, in order to determine the thrust and the power consumption of the MAV, the weight of each component must be estimated. Subsequently, a novel model based on a commercial quadcopter able to determine the power consumption of a drone with a negative impact on mission requirements is proposed. The numerical analysis for this model has been carried out through the software Mathcad R2014 with an academic license.

2.1. MAV Platform Design In order to determine the thrust and the power consumption of the MAV, the weight of each component must be estimated. Table1 shows the total weight of the MAV and all its components [3].

Table 1. Estimated weights of propulsion, payload and avionics components.

Parameter Value Description

mprop 24.4 (g) Propulsion mass mpayl 6.3 (g) Payload mass mav 23 (g) Avionics mass (including all cabling) mairframe 10 (g) Airframe mass marray 2 (g) Dipole rectenna array mass Wtot 80 (g) Total Weight MAV Sensors 2019, 19, 1771 3 of 21

2.2. A Power Consumption Model for MAVs This more sophisticated model identifies all factors that determine the power consumption of a small drone with a negative impact on mission parameters (MD and travel distance) considering various flight scenarios. MD of a MAV is a function of the power P: MD = f (1/P) therefore, to maximize MD, it is necessary to minimize the power consumption. This situation triggers the need for strategies to reduce the power demand onboard drones. An important need is the ability to understand the way in which power is used and spent. In this way, it is possible to identify what are the factors (maneuvers, movements and flight profile) that can involve more energy investment, and therefore faster battery discharge. The factors that determine the power consumption of a drone can be grouped as [3,18]:

(1) Impact of Motion: The motions of a drone can be divided into hovering, vertical and horizontal moving. This paper investigates the power consumption of a drone in each motion type. (2) Impact of Weight: We study how diﬀerent weights of payloads limit their travel distance. (3) Impact of Wind: The major environmental factor that aﬀects the drone is wind. We study the power consumption of a drone in headwind conditions.

2.2.1. Impact of Motion: Hovering Condition In this paper, we are considering a quadcopter as a drone. A quadcopter produces both lift and thrust by using one or more rotors. In our ﬁrst case, we have a hovering condition where the thrust of the rotor is used to completely equilibrate gravity. The thrust T produced by the rotor is [19–21]:

T = 2ρAVw (1) where ρ = 1.225 kg/m2 is the air density, A is the area of the MAV (0.033 m2), V is the resultant velocity and w is the induced velocity induced by rotor blades: q V = (w V sin α)2 + (V cos α)2 (2) − where V is the speed of the object relative to the ﬂuid, α is equal to the angle-of-attack of the rotor plane. Now, considering only the hovering condition V = 0, so that V = w, and using momentum theory:

T = 2ρAw2 (3)

In hovering condition, the net thrust T of the four rotors pushing the drone up must be equal to the gravitational force Wtot = mtot g pulling it down:

2 mtot g = 2ρAw (4) which delivers an expression for the induced velocity: r mtot g w = (5) 2ρA

The total airplane mass mtot in Equation (5) is represented by:

mtot = mbat + mstruct + mprop + mharvester + mav + mpld (6) where battery, structure, propulsion, harvester, avionics and payload masses, have to be optimized. The power needed for the propulsion is given by:

P = DV + Tw (7) Sensors 2019, 19, x FOR PEER REVIEW 4 of 22

Sensors 2019, 19, 1771 mg33 4 of 21 P Tw tot (8) 2A where D is the drag, V = 0 (hovering condition) and T = mtot g, so Equation (7) becomes: Therefore, to reduce the ideal power, we need to decrease weight, increase the rotor blade size s or simply fly at a lower altitude with the capabilities to3 operate3 with environmental temperatures. m tot g The next step is that of calculating theP = thrustTw = needed to lift and hover the quadcopter. As rule(8) 2ρA suggests, thrust should be more than twice the weight of the quadcopter. Total thrust T will be    Tg2.0Therefore, 80 160 to reduce. Now, the in ideal order power, to calculate we need the to thrustdecrease action weight, on increaseeach motor, the rotor it is bladenecessary size or to simplydivide ﬂy the at total a lower thrust altitudeT for with the the number capabilities of motors to operate = 160 with / 4 environmental 40 g . Maximum temperatures. MD in hovering The next stepcondition is that can of calculatingbe written as: the thrust needed to lift and hover the quadcopter. As rule suggests, thrust should be more than twice the weight of the quadcopter. Total thrust T will be T = 2.0 80 = 160 g.  ECVmax 60  n × Now, in order to calculate the thrustthov action on  each  motor,  it is necessary to divide the total thrust T(for9) Pp1000  the number of motors =160/4 = 40 g. Maximumtot MD in hovering tot condition can be written as:

60 ! where efficiency   0.80 includes propellerEmax and motor,60 C V nconverts mAh into Amin, C is the thov = η = η 1000· (9) Ptot · · 1000 ptot battery capacity (in mAh), Vn is the nominal battery voltage (in Volt) and ptot represents propulsion 60 where(taking e ﬃfourciency motorsη = into0.80 account), includes propellerpayload and motor,avionics1000 powerconverts consumption. mAh into Amin, For thisC is quadcopter, the battery capacitywe are considering (in mAh), V an Liis-Po the battery nominal with battery an operating voltage (involtage Volt) of and 3.7p totV andrepresents a capacity propulsion of 400 mAh. (taking By four motors into account), payload and avionics power consumption. For this quadcopter, we are taking into account Equations (5)–(9), the results are w  1.73 m/s, pprop  1.11 W (calculated for considering a Li-Po battery with an operating voltage of 3.7 V and a capacity of 400 mAh. By taking four motors), p  1.57 W, p  1.21 W, ptot p prop  p pld  p av  4.00 W, p  3.88 W without into account Equationsav (5)–(9),pld the results are w = 1.73 m/s, pprop = 1.11 Wtot (calculated for four    motors),GPS, ptotpav 2.=791.57 W without W, ppld payload.= 1.21 W, Byp totusing= Eppropquation+ ppld (9)+ wepav have= 4.00 thov W, p18.16tot = min3.88, t Whov without 18.30 GPS, p = 2.79 W without payload. By using Equation (9) we have t = 18.16 min, t = 18.30 min min withouttot GPS, thov  25.46 min without payload. Figure 1 plotshov mission durationhov (MD) in min withoutvs. capacity GPS, int hovmAh.= In25.46 several min conditions without payload. as hovering, Figure hovering1 plots without mission GPS duration and hovering (MD) in minwithout vs. capacitypayload, inMD mAh. increases In several according conditions to the battery as hovering, capacity. hovering However, without as the GPS capacity and o hoveringf battery withoutextends, payload,the increase MD in increases MD becomes according ineffective. to the battery capacity. However, as the capacity of battery extends, the increase in MD becomes ineﬀective.

thov(C) 20 thov_gps(C)

t (C) hov_pld 10

mission (min) duration

0 100 200 300 400 C capacity (mAh)

FigureFigure 1.1. Mission duration vs capacity.

FromFrom thethe results,results, we we observe observe that that the the drone drone can can maintain maintain a su aﬃ sufficientlyciently steady steady ﬂying flying altitude altitude with steadywith steady power power consumption. consumption.

2.2.2. Impact of Motion: Vertical Condition When considering a vertical condition, the MAV rises and falls quickly. This happens if the thrust of the four MAV rotors is greater than the mass and gravity force. Figure2 shows the forces on the MAV during the ﬂight [18,22–25].

Sensors 2019, 19, x FOR PEER REVIEW 5 of 22

2.2.2. Impact of Motion: Vertical Condition When considering a vertical condition, the MAV rises and falls quickly. This happens if the thrust of the four MAV rotors is greater than the mass and gravity force. Figure 2 shows the forces on the MAV during the flight [18,22–25]. Sensors 2019, 19, 1771 5 of 21

Figure 2. Forces on the MAV during the ﬂight. Figure 2. Forces on the MAV during the flight.

. FThrust FGravity Fd =. mv (10) − − (10) FThrust F Gravity  F d  mv FThrust = T , represents the thrust force, FGravity represents the gravity force, Fd represents the

aerodynamic drag, m is the mass of MAVGravity and ν is the speed of the object related to the fluid. FTThrust  , represents the thrust force, F represents the gravity force, Fd represents the Fd can be written as: aerodynamic drag, m is the mass of MAV and ν is1 the speed of the object related to the fluid. Fd can F = ρAv2C (11) be written as: d 2 D 3 where: ρ = air density (kg/m ); A = top area of1 the MAV;2 ν = speed of the object related to the fluid FdD  Av C (11) (m/s); CD = drag coefficient (dimensionless constant2 fixed at 0.80 in this case study). When replacing the values, Equation (10) can be rewritten as: where: ρ = air density (kg/m3); A = top area of the MAV; ν = speed of the object related to the fluid

(m/s); CD = drag coefficient (dimensionless constantρ fixed at2 0.80 in. this case study). When replacing T mtot g cDA v = mtotv (12) the values, Equation (10) can be rewritten− as: − 2 e f f

. 2 where: T = total motor thrust (N); g = gravity of Earth2 (9.81 m/s ). Equation (12) can be rewritten as: T m g  c A v  m v (12) tot2 D eff tot T ρ 2 g cDAe f f v 2= 0 (13) where: T = total motor thrust (N); g = gmravitytot − of− E2arthmtot (9.81 m/s ). Equation (12) can be rewritten as:  From (13) we get the maximum climbingT rate (vertical2 speed): g  cD A eff v  0 (13) mm2 tots tot From (13) we get the maximum climbing rate (verticalT mtot speed)g : vver = 2 − (14) ρcDA T m g v  2 tot (14) The thrust to weight ratio TWR = verT/W = T/mtot g is the main dynamic characteristic that will cAD determine the drone flight profile. To take off a MAV needs TWR > 1 so it has a net acceleration

upwards.The thrust When to weight hovering, ratio thrust TWR is purely T/W vertical,T / mtot g asis thethe MAVmain pitchesdynamic forward, characteristic the thrust that actswill at determinean angle the named drone angle flight of attackprofile.α Toshown take in off Figure a MAV2. The needs angled TWR thrust > 1 so is madeit has upa net of aacceleration vertical and upwards.horizontal When component. hovering, The thrust last componentis purely vertical acts on, as the the MAV MAV so pitches as to move forward, it forward. the thrust This requiresacts at an the angleTWR namedto be atangle least of 1.3 attack for an α approximate shown in Figure maximum 2. The angle angled of attack thrust of is 40 made degrees. up of When a vertical introducing and horizontalthe thrust component. over weight The ratio last in component Equation (14) acts we on get the the MAV climbing so as rate to move as a function it forward. of the This thrust-weight requires theratio TWRTWR to : be at least 1.3 for an approximates maximum angle of attack of 40 degrees. When q introducing the thrust over weight ratio in Equation2mtot g (14) we get the climbing rate as a function of the vver = (TWR 1) (15) thrust-weight ratio TWR: ρcDA · −

Sensors 2019, 19, x FOR PEER REVIEW 6 of 22

2mgtot vver  ( TWR  1) (15) cAD

SensorsThe2019 ,thrust19, 1771 weight ratio has to be greater than 2. Beside the thrust weight ratio, the maximum6 of 21

climbing rate is a function of the weight ( mgtot ) and the size (area A) of the quadcopter, as well as the aerodynamic drag coefficient . The power consumption of the propulsion motor will be: The thrust weight ratio hasCD to be greater than 2. Beside the thrust weight ratio, the maximum climbing rate is a function of the weight (mtot g) and the size (area A) of the quadcopter, as well as the 2mg aerodynamic drag coeﬃcient CDp. The power Tv  consumptionTtot ( TWR of the  propulsion1) motor will be: (16) prop ver cA s D q 2mtot g With Equations (14)–(16),pprop we= haveTvver the= Tresults v  (2.24TWR m/s1,) p  1.43 W (calculated(16) for ρcDAver· − prop  four motors), pav  1.57 W, ppld 1.21 W, ptot p prop  p pld  p av  4.21 W. By using Equation (9) With Equations (14)–(16), we have the results vver = 2.24 m/s, pprop = 1.43 W (calculated for four tver  17.20 min. Figure 3 plots power propulsion (W) vs. vertical speed (m/s) of one motor. Electric motors), pav = 1.57 W, ppld = 1.21 W, ptot = pprop + ppld + pav = 4.21 W. By using Equation (9) tver = 17.20power min. consumption Figure3 plots of the power propulsion propulsion motor ( W increases) vs. vertical slightly speed and (m linearly/s) of one when motor. the Electricdrone ascends power consumptionsteadily. of the propulsion motor increases slightly and linearly when the drone ascends steadily.

0.4

0.3

ppropVver0.2

0.1

power propulsion (W) propulsion power

0 0.5 1 1.5 2

Vver vertical speed (m/s)

FigureFigure 3.3. ElectricElectric powerpower consumptionconsumption forfor thethe propulsionpropulsion ofof oneone motormotor vs.vs. verticalvertical speed.speed. 2.2.3. Impact of Motion: Horizontal Condition 2.2.3. Impact of Motion: Horizontal Condition In this condition, the drone moves horizontally without altering neither its altitude nor its cruising In this condition, the drone moves horizontally without altering neither its altitude nor its velocity. In forward ﬂight condition, Equation (12) can be rewritten as [18,22–25]: cruising velocity. In forward flight condition, Equation (12) can be rewritten as [18,22–25]: r 22 mtot g ρ 2 . 1 mgtot T  cDAe f f2v = mtotv (17) 1−T ·T − 2 cD A eff v  m tot v (17) T 2 due to the air drag, the quadcopter will reach a speed limit without further acceleration: due to the air drag, the quadcopter will reach a speed limit without further acceleration: r 2 mtot g 2 T ρ 2 1 mg T  cDAe f f v = 0 (18) − Ttot · mtot − 2mtot 2 10  cD A eff v  (18) T mtot2 m tot From Equation (18), we can calculate the horizontal speed as: From Equation (18), we can calculate the horizontal speed as: uv q t mtot g 2 2 1 2 T −mgT · vhor = tot (19) 21ρcDA T Te f f (19) v  hor  By using the thrust-weight ratio TWR and substitutingcAD eff T = TWR mtot g we get · · r s By using the thrust-weight ratio TWR and substituting T TWR· mtot · g we get: 4 1 2mtot g v = 1 √TWR (20) hor 2 − TWR ρcDAe f f Sensors 2019, 19, 1771 7 of 21

The first square root requires for the TWR to be at least 1.3. The effective area Aeff is a function of the forward pitch angle α. To simplify the calculation we only take into account the vertical projection of the quadcopter top area:

sin α = Ae f f /A = mtot g/T = 1/TWR (21)

and as a result we get: r s 4 1 T v = 1 2 √TWR (22) hor 2 − TWR ρcDA Equation (22) becomes: r s 4 1 2mtot g v = 1 TWR (23) hor 2 − TWR ρcDA The top area A can be calculated also taking into account the motor-to-motor distance (MTM) and the propeller size (rprop):

1 1 A = MTM2 + 3 πr2 = (0.16)2 + 3π(0.046)2 = 0.033 m2 (24) 2 · prop 2 The power consumption for propulsion will be:

r s 4 1 2mtot g p = Tv = T 1 TWR (25) prop hor 2 − TWR ρcDA

For longer MD, it is necessary to fly under ideal conditions (no wind and at a room temperature of 20 ◦C). However, the movement during the flight creates a fluid-air displacement of intensity equal to vhor but in the opposite direction (vair = vhor) therefore, the power is obtained by assuming that the body drag (FD) is proportional to airspeed (vair):

1 3 pprop = FD v = ρCDAv (26) · air 2 air

By considering Equations (22)–(26), the results are vhor = 4.20 m/s, pprop = 4.80 W (calculated for four motors), pav = 1.57 W, ppld = 1.21 W, ptot = pprop + ppld + pav = 7.57 W. By using Equation (9) thor = 9.38 min. Figure4 plots the electric power consumption of the propulsion motor (W) vs. horizontal speed (m/s) for one motor. Electric power consumption, which is calculated for one motor increases Sensorsslightly 2019 and, 19, exponentially x FOR PEER REVIEW when the drone ﬂies along the horizontal direction. 8 of 22

1.5

p prop vhor

0.5 power propulsion (W)

0 1 2 3 4

vhor horizontal speed (m/s)

Figure 4. ElectricElectric power power consumption consumption for for one one motor vs. horizontal horizontal speed. speed.

2.2.4. Impact of Weights The MAV owes its success to its ability to carry a payload. However, the weight of a payload limits the mission parameters in particular travel distances. We observe that power consumption increases almost linearly when the weight of a payload increases. The maximum weight of a payload depends on the thrusts that the motors can produce. By considering a payload of 6 g, Equation (26) shows that pprop  4.80 W and ptot  7.57 W. Equation (27) shows, known the motor efficiency and specific energy of the battery, how the payload’s weight could affect the travel distance of a MAV. D’Angelo estimates the energy requirement to be [18,22–25]:

d mmvpp   tot (27)   1vrvrc 370 The travel distance d in meters is:

2(1)Ev d  br mmpvp  (28)  tot  370 rvc

where Eb is the source specific energy of the battery (for Li‐Po 100–265 Wh/kg), mp = payload mass

(grams), mv = vehicle mass (g), r = lift‐to‐drag ratio,  = power transfer efficiency for motor and propeller, ptot = power consumption of MAV in W, vr = ratio of headwind to airspeed and vc = cruising velocity of the aircraft in m/s. The values used in Equation (28), to calculate the travel distance d are Eb = 100 – 265 Wh/kg, vr  0.3, mp  6 g, mv  74 g,  0.8 , r  3 , ptot  7.57

W, vc  4.20 (m/s). Figure 5 plots the travel distance vs. payload. The aircraft’s travel distance is roughly proportional to the payload mass. As payload mass increases, the total weight of the aircraft increases. Increasing the weight raises the minimum speed of the aircraft and reduces the travel distance. Figure 6 plots the travel distance as a function of cruising velocity and motor efficiency. The variation of motor efficiency with cruising velocity leads to increased travel distance. This means that power will increase because the motor will have to spin faster and absorb more current.

Sensors 2019, 19, 1771 8 of 21

2.2.4. Impact of Weights The MAV owes its success to its ability to carry a payload. However, the weight of a payload limits the mission parameters in particular travel distances. We observe that power consumption increases almost linearly when the weight of a payload increases. The maximum weight of a payload depends on the thrusts that the motors can produce. By considering a payload of 6 g, Equation (26) shows that pprop = 4.80 W and ptot = 7.57 W. Equation (27) shows, known the motor efficiency and specific energy of the battery, how the payload’s weight could affect the travel distance of a MAV. D’Angelo estimates the energy requirement to be [18,22–25]: ! d mv + mp ptot + (27) 1 vr 370ηr vc − The travel distance d in meters is:

2 Eb(1 vr) d = · + − (28) mp mv + ptot 370ηr vc

where Eb is the source speciﬁc energy of the battery (for Li-Po 100–265 Wh/kg), mp = payload mass (grams), mv = vehicle mass (g), r = lift-to-drag ratio, η = power transfer eﬃciency for motor and propeller, ptot = power consumption of MAV in W, vr = ratio of headwind to airspeed and vc = cruising velocity of the aircraft in m/s. The values used in Equation (28), to calculate the travel distance d are Eb = 100–265 Wh/kg, vr = 0.3, mp = 6 g, mv = 74 g, η = 0.8, r = 3, ptot = 7.57 W, vc = 4.20 (m/s). Figure5 plots the travel distance vs. payload. The aircraft’s travel distance is roughly proportional to Sensors 2019the, 19 payload, x FOR PEER mass. REVIEW As payload mass increases, the total weight of the aircraft increases. Increasing the 9 of 22 weight raises the minimum speed of the aircraft and reduces the travel distance.

185.8

185.6

185.4 dmp 185.2

185 travel distance (m) 184.8 0 2 4 6

m p payload (g)

Figure 5. Travel distance vs. payload. Figure 5. Travel distance vs. payload. Figure6 plots the travel distance as a function of cruising velocity and motor e ﬃciency. The variation of motor eﬃciency with cruising velocity leads to increased travel distance. This means that power will increase because the motor will have to spin faster and absorb more current.

Figure 6. Travel distance as a function of motor efficiency and cruising velocity.

2.2.5. Impact of Wind Wind condition is a major environmental factor that affects the power consumption of a MAV. We are only considering the flight of a MAV in headwind conditions. In this scenario, the motors are forced to generate more thrust to maintain a horizontal condition and thrust requires power. The drone was set to fly with a wind speed of 7 m/s. Equation (25) will be re‐written considering the power needed to overcome FD in headwind conditions [18,22–25]:

1 pFv  CAv3 (29) prop D wind2 D wind

By considering Equation (29), we have the results vwind  7 m/s, pprop  22 W (calculated for four motors), pav  1.57 W, ppld  1.21 W, pptot prop pp pld av 24.78 W. By using Equation (9) twind  3.17 min. Figure 7 plots the electric power consumption of the propulsion motor (W) vs. wind speed (m/s) for one motor. Electric power consumption calculated for one motor increases exponentially when the drone flies along the horizontal direction in headwind condition. In fact, in this condition, the motor will have to spin faster and absorb more current.

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185.8

185.6

185.4 dmp 185.2

185

travel distancetravel (m) 184.8 0 2 4 6

mp payload (g)

Sensors 2019, 19, 1771 Figure 5. Travel distance vs. payload. 9 of 21

Figure 6. Travel distance as a function of motor efficiency and cruising velocity. Figure 6. Travel distance as a function of motor efficiency and cruising velocity. 2.2.5. Impact of Wind 2.2.5. WindImpact condition of Wind is a major environmental factor that affects the power consumption of a MAV. We areWind only condition considering is a major the flight environmental of a MAV in factor headwind that affects conditions. the power In this consumption scenario, the of motors a MAV. are Weforced are toonly generate considering more thrustthe flight to maintain of a MAV a horizontalin headwind condition conditions. and thrust In this requires scenario, power. the motors The drone are forcedwas set to to generate fly with more a wind thrust speed to ofmaintain 7 m/s. a Equation horizontal (25) condition will be re-written and thrust considering requires power. the power The droneneeded was to overcomeset to fly withFD in a headwind wind speed conditions of 7 m/s. [18 E,22quation–25]: (25) will be re-written considering the power needed to overcome FD in headwind conditions [18,22–25]: 1 3 pprop = FD v = ρCDAv (29) · wind 2 wind 1 3 pprop F D  v wind   C D Av wind (29) By considering Equation (29), we have the results2 vwind = 7 m/s, pprop = 22 W (calculated for four motors), pav = 1.57 W, ppld = 1.21 W, ptot = pprop + ppld + pav = 24.78 W. By using Equation (9) twind = By considering Equation (29), we have the results vwind  7 m/s, pprop  22 W (calculated for 3.17 min. four motors), p  1.57 W, p  1.21 W, p p  p  p  24.78 W. By using Equation (9) Figure7 plotsav the electric powerpld consumptiontot of prop the propulsion pld av motor (W) vs. wind speed (m /s) tforwind one 3.17 motor. min Electric. power consumption calculated for one motor increases exponentially when the droneFigure flies along7 plots the the horizontal electric power direction consumption in headwind of the condition. propulsion In motor fact, in (W) this vs condition,. wind speed the motor(m/s) Sensors 2019, 19, x FOR PEER REVIEW 10 of 22 forwill one have motor. to spin Electric faster power and absorb consumption more current. calculated for one motor increases exponentially when the drone flies along the horizontal direction in headwind condition. In fact, in this condition, the motor will have to spin faster and absorb6 more current.

ppropvwind

power propulsion (W) propulsion power

0 2 4 6 8

vwind wind speed (m/s)

FigureFigure 7. 7. PowerPower consumption consumption of of propulsion propulsion for for one one motor motor vs vs.. w windind speed speed..

FigureFigure 8 plotsplots MDMD inin vertical,vertical, horizontalhorizontal andand inin headwindheadwind condition.condition. In these conditions, MD increasesincreases as as a a function function of of the the battery battery capacity. capacity. However, However, as the as capacity the capacity of battery of battery extends, extends, the increase the increasein MD becomes in MD becomes ineﬀective. ineffective.

15 tver(C)

t (C) hor 10

twind(C) 5

mission duration (min)mission duration

0 100 200 300 400 C capacity (mAh)

Figure 8. Mission duration vs capacity.

3. Economical Alternative to PV This section describes the design and simulations of a novel harvester based on plasmonics technology, which is able to improve mission parameters. Moreover, to optimize the power transfer, an optimal matching between harvester and load is proposed.

3.1. Novel Harvester to Power Small Aerial Vehicles The model presented above shows that the weight of the battery plays one of the most important roles in determining the mission parameters of a flying object. Energy harvesting gives the opportunity of reducing dimensions and weight of the battery reducing the total weight of a MAV and consequently decreasing the amount of energy required to power it. This harvester is called nano-rectenna. The term results from joining “rectifying circuit” and “nano-antenna” [17], i.e., a novel answer to the power issues of the MAVs. Figure 9 shows the block diagram of the energy harvesting system proposed. Each rectenna contains a receiving antenna, a rectifier circuit, a DC-DC step-up converter, a storage device and a load as shown in Figure 9. These nano-antennas use excited localized surface plasmons to induce electrons flow along the antenna, generating alternating current (AC) at the same frequency of the incident wave [17]. Subsequently, the rectifying circuit receives the AC from the

Sensors 2019, 19, x FOR PEER REVIEW 10 of 22

p prop v wind

2 power propulsion (W)

0 2 4 6 8

v wind wind speed (m/s)

Figure 7. Power consumption of propulsion for one motor vs. wind speed.

Figure 8 plots MD in vertical, horizontal and in headwind condition. In these conditions, MD increases as a function of the battery capacity. However, as the capacity of battery extends, the Sensorsincrease2019 in, 19 MD, 1771 becomes ineffective. 10 of 21

15 t ver ()C

t ()C hor 10

t wind ()C 5 mission duration (min)

0 100 200 300 400 C capacity (mAh)

Figure 8. Mission duration vs capacity. 3. Economical Alternative to PV 3. Economical Alternative to PV This section describes the design and simulations of a novel harvester based on plasmonics This section describes the design and simulations of a novel harvester based on plasmonics technology, which is able to improve mission parameters. Moreover, to optimize the power transfer, an Sensorstechnology, 2019, 19 ,which x FOR PEERis able REVIEW to improve mission parameters. Moreover, to optimize the power transfer,11 of 22 optimal matching between harvester and load is proposed. an optimal matching between harvester and load is proposed. dipole metallic strips. After the rectifier, the output presents a unidirectional current flow and a not 3.1. Novel Harvester to Power Small Aerial Vehicles constant3.1. Novel voltage. Harvester To to make Power this Small output Aerial voltage Vehicles constant, a capacitor is usually added at the rectifier’s output.The The model battery presented and load above need shows to be thatfed at the a specific weight ofand the regulated battery plays voltage one value. of the A most DC- importantDC boost The model presented above shows that the weight of the battery plays one of the most important converterroles in determining is introduced the mission after the parameters rectifier, of so a a ﬂying low object. output Energy voltage harvesting of the rectenna gives the array opportunity can be roles in determining the mission parameters of a flying object. Energy harvesting gives the increasedof reducing to dimensions a typical battery and weight voltage. of theAfter battery the DC reducing-DC block, the totalthe energy weight is of then a MAV stored and in consequently the energy opportunity of reducing dimensions and weight of the battery reducing the total weight of a MAV storagedecreasing device, theamount often chosen of energy as a requiredrechargeable to power battery/supercapacitor. it. This harvester is These called flexible nano-rectenna. dipole rectennas The term and consequently decreasing the amount of energy required to power it. This harvester is called areresults thought from to joining be incorporated “rectifying into circuit” the frame and of “nano-antenna” a MAV [26]. In [the17], following i.e., a novel paragr answeraph, tothe the complete power nano‐rectenna. The term results from joining “rectifying circuit” and “nano‐antenna” [17], i.e., a novel characterizationissues of the MAVs. of a Figurerectenna9 shows in Figure the block9 is presented. diagram of the energy harvesting system proposed. answer to the power issues of the MAVs. Figure 9 shows the block diagram of the energy harvesting system proposed. Each rectenna contains a receiving antenna, a rectifier circuit, a DC‐DC step‐up converter, a storage device and a load as shown in Figure 9. These nano‐antennas use excited localized surface plasmons to induce electrons flow along the antenna, generating alternating current (AC) at the same frequency of the incident wave [17]. Subsequently, the rectifying circuit receives the AC from the

Figure 9. Block diagram of an energy harvesting system. Figure 9. Block diagram of an energy harvesting system. Each rectenna contains a receiving antenna, a rectifier circuit, a DC-DC step-up converter, a storage 3.2.device Internal and aN loadano-Antenna as shown Impedance in Figure E9valuation. These nano-antennas use excited localized surface plasmons to induce electrons flow along the antenna, generating alternating current (AC) at the same frequency The antenna operating in receiving mode can be represented by a voltage source V and an of the incident wave [17]. Subsequently, the rectifying circuit receives the AC from the dipoleopen metallic inputstrips. impedance After the rectifier, in series theZA output R in presents jX in · Vopen a unidirectionalrepresents the current open circuit flow andvoltage a not at constant the terminals voltage. of Tothemake antenna this output when voltage the load constant, is not a connected. capacitor is usuallyZ represen addedts at the rectifier’s impedance output. of the The antenna battery and load need to be fed at a specific and regulatedA voltage value. A DC-DC boost converter is characterized by a resistive component R defined as the sum of the radiation resistance R and introduced after the rectifier, so a low outputin voltage of the rectenna array can be increased to a typicalrad lossbattery resistance voltage. R Afterloss and the by DC-DC a reactive block, component the energyXin is(that then is stored equal into thezero energy at resonant storage frequency). device, often In orderchosen to asevaluate a rechargeable the nano- batteryantenna/supercapacitor. impedance, the Thesestudy flexiblehas been dipole carried rectennas out focusing are thoughton aluminum to be [27]incorporated nano-dipole into antenna the frame shown of a MAVin Figure [26]. 10. In the following paragraph, the complete characterization of a rectennaAluminum in Figure is a9 is low presented.-loss metal whose permittivity at the frequency of interest is (  )  '(  ) i  ''(  )  −90.56 + i38.56 where '( ) and ''( ) respectively represent real and imaginary parts of the complex permittivity ()[14,28]. The numerical analysis in this section was performed with a commercial full-wave 3D electromagnetic simulator CST Studio Suite 2016.

Figure 10. Schematic representation of the aluminum nano-dipole antenna, defining its geometrical parameters.

The dipole consists of two identical arms of length L. At 350 THz (   857 nm) we set antenna’s   eff 2 length L as L    80 nm, where n eff = 2.8, numerically extracted for   857 nm from 2 n eff

[28] represents the effective refractive index, eff represents the effective wavelength experienced by

Sensors 2019, 19, x FOR PEER REVIEW 11 of 22 dipole metallic strips. After the rectifier, the output presents a unidirectional current flow and a not constant voltage. To make this output voltage constant, a capacitor is usually added at the rectifier’s output. The battery and load need to be fed at a specific and regulated voltage value. A DC-DC boost converter is introduced after the rectifier, so a low output voltage of the rectenna array can be increased to a typical battery voltage. After the DC-DC block, the energy is then stored in the energy storage device, often chosen as a rechargeable battery/supercapacitor. These flexible dipole rectennas are thought to be incorporated into the frame of a MAV [26]. In the following paragraph, the complete characterization of a rectenna in Figure 9 is presented.

Figure 9. Block diagram of an energy harvesting system.

3.2. Internal Nano-Antenna Impedance Evaluation

The antenna operating in receiving mode can be represented by a voltage source Vopen and an input impedance in series Z R jX · V represents the open circuit voltage at the terminals of Sensors 2019, 19, 1771 A in in open 11 of 21 the antenna when the load is not connected. ZA represents the impedance of the antenna characterized by a resistive component R defined as the sum of the radiation resistance R and 3.2. Internal Nano-Antenna Impedance Evaluationin rad loss resistance Rloss and by a reactive component Xin (that is equal to zero at resonant frequency). In The antenna operating in receiving mode can be represented by a voltage source V and an order to evaluate the nano-antenna impedance, the study has been carried out focusing on openaluminum input impedance in series Z = R + jX Vopen represents the open circuit voltage at the terminals of [27] nano-dipole antenna shownA inin Figurein ·10. the antenna when the load is not connected. Z represents the impedance of the antenna characterized Aluminum is a low-loss metal whoseA permittivity at the frequency of interest is by a resistive component R deﬁned as the sum of the radiation resistance R and loss resistance R (  )  '(  ) i  ''(  )  −90.56in + i38.56 where '( ) and ''( ) respectivelyrad represent real andloss and by a reactive component Xin (that is equal to zero at resonant frequency). In order to evaluate imaginary parts of the complex permittivity ()[14,28]. The numerical analysis in this section was the nano-antenna impedance, the study has been carried out focusing on aluminum [27] nano-dipole performedantenna shown with ina commercial Figure 10. full-wave 3D electromagnetic simulator CST Studio Suite 2016.

Figure 10. Schematic representation of the aluminum nano-dipole antenna, deﬁning its Figuregeometrical 10. Schematic parameters. representation of the aluminum nano-dipole antenna, defining its geometrical parameters.

Aluminum is a low-loss metal whose permittivity at the frequency of interest is ε(ω) = ε0(ω) + iε00 (ωThe) = dipole90.56 consis+ i38.56ts of where two identicalε (ω) and armsε00 (ofω )lengthrespectively L. At 350 represent THz (   real 857 and nm) imaginary we set antenna’s parts of − 0 the complex permittivity ε(ω) [14,28]. The numerical analysis in this section was performed with a  lengthcommercial L as full-waveL eff  3D2 electromagnetic  80 nm, where simulator n = 2.8, CST numerically Studio Suite extracted 2016. for   857 nm from 2 n eff The dipole consistseff of two identical arms of length L. At 350 THz ( λ = 857 nm) we set antenna’s λ λ e f f 2  [2length8] representsL as L = the effective= = refractive80 nm, where index,n eff=represents2.8, numerically the effective extracted wavelength for λ = 857 experienced nm from[ 28by] 2 ne f f e f f represents the effective refractive index, λe f f represents the effective wavelength experienced by the plasmonic dipole, which is shorter than the free-space wavelength λ0. The width and the height of the arms are fixed respectively at 10 nm and at 15 nm. The gap G between the two arms is fixed at 5 nm. The substrate on which the aluminum nano-antennas are patterned is assumed to be a silicon dioxide (SiO2) (the substrate thickness is fixed at 50 nm, length and width are fixed respectively at 350 nm and 100 nm, the related dielectric permittivity is 4.82 and the dielectric loss tangent is 0.002). The feeding point at the center of the antenna is set to a lumped port instead of a real diode for simplicity [29,30]. In general, the maximum transferred energy obtained when the antenna is resonant is measured at the minimum of the return loss (S11) parameter shown in Figure 11. The antenna is resonating at 350 THz with a return loss of 45 dB. At this value of return loss, the maximum power transmitted to − the antenna is 99.87% [15]. To determine the input impedance, the gap is excited by a default Gaussian pulse at the frequency of 350 THz. Figure 12 shows input impedance of a dipole antenna in resonance condition: (a) input resistance Rin = 1055 Ω and (b) input reactance Xin = 0 Ω. Sensors 2019, 19, x FOR PEER REVIEW 12 of 22

the plasmonic dipole, which is shorter than the free-space wavelength 0 . The width and the height of the arms are fixed respectively at 10 nm and at 15 nm. The gap G between the two arms is fixed at Sensors5 nm. 2019The, 19substrate, x FOR PEER on REVIEWwhich the aluminum nano-antennas are patterned is assumed to be a silicon12 of 22 dioxide (SiO2) (the substrate thickness is fixed at 50 nm, length and width are fixed respectively at

the350 plasmonicnm and 100 dipole, nm, the which related is shorter dielectric than permittivity the free-space is 4.82 wavelength and the dielectric0 . The widthloss tangent and the is height0.002). ofThe the feeding arms are point fixed at respectively the center of at the10 nm antenna and at is 15 set nm. to Thea lumped gap G betweenport instead the two of a arms real isdiode fixed for at 5simplicity nm. The substrate[29,30]. In on general, which the the maximum aluminum transferred nano-antennas energy are obtained patterned when is assumed the antenna to be is resonanta silicon dioxideis measured (SiO 2at) (the the substrateminimum thickness of the return is fixed loss at (S 5011) nm,parameter length shownand width in F igureare fixed 11. respectivelyThe antenna atis 350resonating nm and at 100 350 nm, THz the with related a return dielectric loss of permittivity −45 dB. At isthis 4.82 value and ofthe return dielectric loss, lossthe maximumtangent is 0.002).power Thetransmitted feeding topoint the antennaat the center is 99.87% of the [15]. antenna is set to a lumped port instead of a real diode for simplicity [29,30]. In general, the maximum transferred energy obtained when the antenna is resonant is measured at the minimum of the return loss (S11) parameter shown in Figure 11. The antenna is resonating at 350 THz with a return loss of −45 dB. At this value of return loss, the maximum power Sensors 2019 19 transmitted, ,to 1771 the antenna is 99.87% [15]. 12 of 21

Figure 11. S-parameter magnitude (dB) vs. Frequency.

To determine the input impedance, the gap is excited by a default Gaussian pulse at the frequency of 350 THz. Figure 12 shows input impedance of a dipole antenna in resonance condition:

(a) input resistance Rin = Figure1055 Ω 11. and SS-parameter- parameter(b) input magnitudereactance (dB)Xin = vs. 0 Ω. Frequency.Frequency .

To determine the input impedance, the gap is excited by a default Gaussian pulse at the frequency of 350 THz. Figure 12 shows input impedance of a dipole antenna in resonance condition:

(a) input resistance Rin = 1055 Ω and (b) input reactance Xin = 0 Ω.

Sensors 2019, 19, x FOR PEER REVIEW 13 of 22 (a)

(a)

(b)

FigureFigure 12. 12.( (aa))Input Input resistanceresistance of of thethe proposedproposed antenna; antenna; ( b(b)) Input Input Reactance Reactance of of the the proposed proposed antenna. antenna.

3.3. Effective Area Evaluation

A parameter usually used in the antenna theory is the effective area Aeff of the antenna calculated by Equation (30) [16]:

2 VZopen 0 eff  A 2 (30) 4EZiA

During the simulation, the nano-dipole was irradiated by a linearly polarized plane wave at a

frequency value of 350 THz with an arbitrary amplitude of 1 V/m. By assuming, for Vopen the simulated value of 5 × 10−5 V as reported in Figure 13, calculated when the load is not connected, for the

intrinsic impedance of free space Z0 a value of 377 Ω, for the nano-dipole impedance ZA a value of

1055 Ω and for the incident electric field Ei a value of 1 V/m, the value of nano-dipole effective area calculated by (30) is 223 nm².

Figure 13. Open circuit voltage of the dipole when the load is not connected.

3.4. Electrical Parameters Evaluation The energy radiated by the Sun with a temperature equal to 5778 K follows the Plank’s blackbody radiation formula [16]:

Sensors 2019, 19, x FOR PEER REVIEW 13 of 22

(b)

Figure 12. (a) Input resistance of the proposed antenna; (b) Input Reactance of the proposed antenna.

3.3.Sensors Effective2019, 19 Area, 1771 Evaluation 13 of 21

A parameter usually used in the antenna theory is the effective area Aeff of the antenna calculated3.3. Effective by Area Equation Evaluation (30) [16]: A parameter usually used in the antenna theory2 is the effective area A of the antenna calculated VZopen 0 e f f eff  by Equation (30) [16]: A 2 (30) 4EZ2iA VopenZ0 Ae f f = (30) During the simulation, the nano-dipole was irradiated2 by a linearly polarized plane wave at a 4Ei ZA frequency value of 350 THz with an arbitrary amplitude of 1 V/m. By assuming, for V the simulated During the simulation, the nano-dipole was irradiated by a linearly polarizedopen plane wave at a −5 valuefrequency of 5 value× 10 of 350V as THz reported with an in arbitrary Figure amplitude13, calculated of 1 Vwhen/m. By the assuming, load is not for Vconnected,open the simulated for the intrinsicvalue of 5impedance10 5 V asofreported free space in FigureZ a value 13, calculated of 377 Ω, whenfor the the nano load-dipole is not connected,impedance for Z thea value intrinsic of × − 0 A impedance of free space Z a value of 377 Ω, for the nano-dipole impedance Z a value of 1055 Ω and 1055 Ω and for the incident0 electric field Ei a value of 1 V/m, the value of nanoA -dipole effective area for the incident electric field E a value of 1 V/m, the value of nano-dipole effective area A calculated calculated by (30) is 223 nm².i e f f by (30) is 223 nm2.

FigureFigure 13. 13. OpenOpen circuit circuit voltage voltage of of the the dipole dipole when when the the load load is is not not c connected.onnected. 3.4. Electrical Parameters Evaluation 3.4. Electrical Parameters Evaluation The energy radiated by the Sun with a temperature equal to 5778 K follows the Plank’s blackbody The energy radiated by the Sun with a temperature equal to 5778 K follows the Plank’s radiation formula [16]: blackbody radiation formula [16]: 2πhc2 = Wλ hc (31) λ5(e λkT 1) − where h is the Plank’s constant equal to 6.63 10 34 [J s], c is the speed of light equal to 3 108 [m/s], × − · T is the temperature [k], and K is the Boltzmann’s constant equal to 1.38 10 23 [J/K]. To obtain the × − power density (Poynting vector) on the antenna we are integrating Wλ between λmin and λmax:

λZmax S = Wλ(λ, T)dλ (32) λ min

In this case, the actual Poynting vector has been obtained by taking into account the radiation rad eﬃciency η whose values depend on the wavelength and by integrating Wλ in the operating range of the optical nano-antenna, i.e., between λ1 = 300 nm and λ2 = 1200 nm [31–33]. This range of wavelengths is commonly used in literature because it covers the visible and infrared interval. Equation (32) can be rewritten as:

Zλ2 rad S0 = Wλ(λ, T)η (λ)dλ (33)

λ1 Sensors 2019, 19, x FOR PEER REVIEW 14 of 22

2 hc2 W   hc (31)  5 (e kT  1) where h is the Plank’s constant equal to 6.63  1034 [J s], c is the speed of light equal to 3·108 [m/s], T is the temperature [k], and K is the Boltzmann’s constant equal to 1.38  1023 [J/K]. To obtain the power density (Poynting vector) on the antenna we are integrating W between min and max :

 max   S W (,) T d (32)  min

In this case, the actual Poynting vector has been obtained by taking into account the radiation rad efficiency  whose values depend on the wavelength and by integrating W in the operating range of the optical nano-antenna, i.e., between 1  300 nm and 2  1200 nm [31–33]. This range of wavelengths is commonly used in literature because it covers the visible and infrared interval. Equation (32) can be rewritten as:

2 rad S'(,)() W T   d  (33) Sensors 2019, 19, 1771   14 of 21 1

The available power on the load matching condition has been evaluated as: The available power on the load matching condition has been evaluated as: PASeff ' (34) P = Ae f f S0 (34) Table 2 shows the values of the actual Poynting vector· and the corresponding power on the load in matchingTable2 shows conditions the values by considering of the actual a dipole Poynting with vector arm length and the of corresponding80 nm. power on the load in matching conditions by considering a dipole with arm length of 80 nm. Table 2. Poynting Vector and Optimal Power.

L [nm] TableActual 2. PoyntingPoynting vector Vector and Optimal[W/m2] Power.Power P [nW] 80 70 15.6 L [nm] Actual Poynting Vector [W/m2] Power P [nW]

The maximum80 value of the electrical field 70 has been calculated as: 15.6

 The maximum value of the electricalES ﬁeld has2'  been  calculatedo as: (35) i  s ro µo Ei = 2 S0 (35) o · |h i| · εo where is the intrinsic impedance of free space Z0 equal to 377 Ω. Figure 14 shows the  o q µ where o is the intrinsic impedance of free space Z equal to 377 Ω. Figure 14 shows the equivalent equivalentεo circuit, which serves to calculate the 0 output voltage V0 obtained when the load is concircuit,nected which to the serves nano to-antenna calculate [16] the output voltage V0 obtained when the load is connected to the nano-antenna [16]

Figure 14. Equivalent circuit for the calculation of the output voltage V0.

Figure 14. Equivalent circuit for the calculation of the output voltage V0 . Assuming the impedance matching condition between the antenna ZA and the load ZL, the output Vopen voltage V0 is equal to 2 . Table3 shows the values of the incident electric ﬁeld, of Vopen and of the output voltage V0 for dipole arm length of 80 nm.

Table 3. Electric Field, Vopen and Output Voltage V0.

L [nm] Ei [V/m] Vopen [µV] Vo [µV] 80 230 50 25

Unfortunately, the results above show that the output power and voltage of a single rectenna tuned at 350 THz are very low, of the order of nW and µV, respectively. These values are limited to the characteristics of the metal insulator metal diode (MIM) or multi insulator metal diode (MIIM) [11,34]. For aeronautic applications, a MAV requires high current and low voltage. Consequently, an arrangement in array is necessary. The dipole rectenna elements are connected in series and parallel to meet the requirement of increased power. To obtain an eﬃcient power transfer, the coupling between the impedance of the array and the impedance of the DC-DC boost power converter is required. Therefore, the array resistance has to be suﬃciently low and close to the DC-DC resistance. Consequently, a reduction in power loss is achieved. Sensors 2019, 19, 1771 15 of 21

3.5. Design of a Rectenna Array

3.5.1. Impedance Matching The rectenna model used in this paper is reported in [3]. To calculate the impedance matching supposed above, the diode junction capacitance CD can be neglected because it behaves as a low pass ﬁlter and only the DC component can be considered. The current ﬂowing in the circuit can be expressed as [16]: Vopen RD + RL I = = Vopen (36) RDRL · RA(RD + RL) + RDRL RA + RD+RL The total power in the circuit can be expressed as: " # RD + RL PTOT = Vopen2 (37) · RA(RD + RL) + RDRL

The power in the antenna, in the diode and in the load are shown below:

" #2 RD + RL PA = Vopen2 RA (38) · RA(RD + RL) + RDRL ·

" #2 RL PD = Vopen2 RD (39) · RA(RD + RL) + RDRL · " #2 RD PL = Vopen2 RL (40) · RA(RD + RL) + RDRL · The best impedance matching condition is obtained when the value of RD is much larger than the values of RA and RL, on the contrary, RA and RL must be equal. Therefore, the optimal matching can be achieved by a rectenna array whose equivalent impedance equals the impedance ZIboost of the DC-DC boost converter.

3.5.2. DC-DC Boost Power Converter Figure9 shows a DC-DC boost converter introduced after the rectiﬁer, so a low output voltage of the rectenna array can be increased to a typical battery voltage [35–37]. By following this approach, an LTC3108 DC/DC boost converter, is used [38]. LTC3108 produces an output voltage up to 5 V with a peak current of 4500 µA under 500 mV input voltage and a resistance of 3 Ω as input conditions. The knowledge of the DC-DC impedance (3 Ω) is important for obtaining the optimal matching with that of rectenna array.

3.5.3. The Equivalent Circuit of an Array of Optical Rectennas The equivalent circuit of an array of optical rectennas is shown in Figure 15. Each cell consists of an antenna with the rectiﬁer placed in the gap. The equivalent resistance of a single rectenna RRECT can be expressed as [16]: RARD RRECT = (41) RA + RD

Currently, the state of the art indicates a diode resistance RD around 1.2 MΩ [39], whereas the value of RA is equals to 1055 Ω. If N rectennas are connected in series (matrix column), the value of the column resistance RCOL becomes equal to:

RARD RCOL = N RRECT = N (42) · · RA + RD Sensors 2019, 19, x FOR PEER REVIEW 16 of 22

3.5.2. DC-DC Boost Power Converter Figure 9 shows a DC-DC boost converter introduced after the rectifier, so a low output voltage of the rectenna array can be increased to a typical battery voltage [35–37]. By following this approach, an LTC3108 DC/DC boost converter, is used [38]. LTC3108 produces an output voltage up to 5 V with a peak current of 4500 μA under 500 mV input voltage and a resistance of 3 Ω as input conditions. The knowledge of the DC-DC impedance (3 Ω) is important for obtaining the optimal matching with that of rectenna array.

3.5.3. The Equivalent Circuit of an Array of Optical Rectennas The equivalent circuit of an array of optical rectennas is shown in Figure 15. Each cell consists of an antenna with the rectifier placed in the gap. The equivalent resistance of a single rectenna RRECT can be expressed as [16]:

RRAD RRECT  (41) RRAD

Currently, the state of the art indicates a diode resistance RD around 1.2 MΩ [39], whereas the value of RA is equals to 1055 Ω. If N rectennas are connected in series (matrix column), the value of the column resistance RCOL becomes equal to:

Sensors 2019, 19, 1771 RRAD 16 of 21 RNRNCOL  RECT   (42) RRAD R RCOLCOL isis higherhigher thanthan the DC-DCDC-DC resistance, therefore the impedance matching between the array and the DC DC-DC-DC boost converter is not satisfied. satisﬁed. To To obtain the maximum power transfer between the rectennarectennass array and the DC-DCDC-DC converter, it is necessarynecessary to decreasedecrease RCOL connecting in parallel M column. Therefore, the equivalent resistance of an array is given by: column. Therefore, the equivalent resistance of an array is given by: N N R R NNRRA D Req, array = RRECT =  AD (43) RReq, arrayM · RECT M · RA + RD (43) MMRRAD and the ratio of N/M is obtained as: and the ratio of NM/ is obtained as: ! + N RA RD N= Req, array RRAD (44) M Req, array ·RA RD (44) MRRAD·

FFigureigure 15 15.. Equivalent circuit of an array of N·M N M optical rectennas rectennas,, each cell corresponds to the circuit · in F Figureigure 14.

With this method, the series connection, allows the open circuit voltage to be increased whereas the parallel connection gives the degree of freedom needed to achieve the impedance matching with the load. In a matrix arrangement, the dipoles are considered as simply connected in series-parallel, however, the practical realization of this matrix would require a further study to avoid parasitic interactions among them; this aspect will be investigated in the future. To achieve the voltage matching between the optical rectenna array and the DC-DC converter, the rectenna array output voltage has to be greater than the lower input voltage of the DC-DC converter. For this case study, the maximum output voltage of the array V0,array is ﬁxed at 1000 mV (this value will decrease to 500 mV when the matching load is connected to the array). N can be obtained as [16]:

VO, array 2V N = = Iboost V (45) VDC open π

1 where the DC value can be obtained from the Vopen multiplied for π (which corresponds to an half-wave rectiﬁer). From Equation (45), it is possible to calculate the value of M:

N M = (46) RA+RD Req, array R R · A· D The array area can be expressed as:

Aarray = N M Adipole (47) · · Table4 shows the values of N and M for a nano-dipole which arm length is of 80 nm. Sensors 2019, 19, x FOR PEER REVIEW 17 of 22 the load. In a matrix arrangement, the dipoles are considered as simply connected in series-parallel, however, the practical realization of this matrix would require a further study to avoid parasitic interactions among them; this aspect will be investigated in the future. To achieve the voltage matching between the optical rectenna array and the DC-DC converter, the rectenna array output voltage has to be greater than the lower input voltage of the DC-DC converter. For this case study, the maximum output voltage of the array V0,array is fixed at 1000 mV (this value will decrease to 500 mV when the matching load is connected to the array). N can be obtained as [16]:

V 2V N O, array Iboost VDC Vopen (45)  1 where the DC value can be obtained from the V multiplied for (which corresponds to an half- open  wave rectifier). From Equation (45), it is possible to calculate the value of M: N M   RRAD (46) Req, array  RRAD

The array area can be expressed as:

ANMAarray   dipole (47) Sensors 2019, 19, 1771 17 of 21 Table 4 shows the values of N and M for a nano-dipole which arm length is of 80 nm.

TableTable 4. 4. ParametersParameters of of a aRectenna Rectennass Array Array..

L [nm] ZIboost [Ω] VDC [µV] Vo,array [mV] N M L [nm] ZIboost [Ω]VDC [µV] Vo,array [mV] N M 80 3 16 1000 62,800 22,066,058 80 3 16 1000 62,800 22,066,058 By considering Equation (47), the rectenna array area, constituted by dipoles, which arm length, is of 80By nm considering is 23 cm². Equation (47), the rectenna array area, constituted by dipoles, which arm length, is of 80 nm is 23 cm2. 3.5.4. Load Power and Energy Evaluation 3.5.4. Load Power and Energy Evaluation In order to evaluate the available power on the load, the whole array of rectennas can be simulatedIn order as the to evaluateequivalent the circuit available shown power in onFigure the load,16 [16]. the whole array of rectennas can be simulated as the equivalent circuit shown in Figure 16[16].

Figure 16. Circuit for the calculation of a rectenna array power. Figure 16. Circuit for the calculation of a rectenna array power. The impedance matching condition between the rectenna array Req,array and the load Ziboost has been assumed. The input impedance Ziboost is considerate equal to 3 Ω. The load power can be The impedance matching condition between the rectenna array Req, array and the load Z has calculated by: iboost been assumed. The input impedance Ziboost is considerate2 equal to 3 Ω. The load power can be Vout Pload = (48) calculated by: ZIboost

VDC where the output voltage Vout on the load Ziboost is equal to N 2 . Under matching impedance conditions, the array output power and the boost input power are the same and equal to 84.14 mW. Figure 17 shows the curve of solar radiation versus time during a typical July day in a southern Italian region. It should be noted that the maximum of the solar radiation is at the Zenith. The energy that can be delivered by a rectenna array can be expressed as:

Z R(t) ξday = Parray dt (49) Rmax t where R(t) is the solar radiation versus time, Rmax is the value of solar radiation at Zenith and Parray is the maximum value of the array power. By considering Equation (49), the obtained energy is equal to 2572 J. By taking into account the above results, Equation (9) shows improvements for the mission parameters. For MD the improvements with the harvester only in horizontal condition has been of 16.30 mins. With a hybrid system (battery and harvester), the improvements in horizontal and headwind conditions have been respectively of 50 min and 15.30 min. For travel distance, the improvements with the harvester only have been of 1903 m and with a hybrid system of 5845 m. In headwind condition, considering the hybrid system, the improvement has been of 3047 m. The preliminary results above show that a dipole rectenna array with 22,066,058 elements in parallel and 62,800 elements in serial connections are suﬃcient to recharge a small battery on board a MAV. Finally, the angle of incidence of solar radiation on a dipole rectenna array embedded into the frame of a MAV may vary with the instantaneous change in orientation of a ﬂying MAV. When the pole orientation of rectennas does Sensors 2019, 19, x FOR PEER REVIEW 18 of 22

2 Vout Pload  (48) ZIboost

V where the output voltage V on the load Z is equal to N DC . Under matching impedance out iboost 2 conditions, the array output power and the boost input power are the same and equal to 84.14 mW. Figure 17 shows the curve of solar radiation versus time during a typical July day in a southern Italian region. It should be noted that the maximum of the solar radiation is at the Zenith. The energy that can be delivered by a rectenna array can be expressed as: Rt()   P dt day array (49) t Rmax

where Rt() is the solar radiation versus time, Rmax is the value of solar radiation at Zenith and

Parray is the maximum value of the array power. By considering Equation (49), the obtained energy is equal to 2572 J. By taking into account the above results, Equation (9) shows improvements for the mission parameters. For MD the improvements with the harvester only in horizontal condition has been of 16.30 mins. With a hybrid system (battery and harvester), the improvements in horizontal and headwind conditions have been respectively of 50 min and 15.30 min. For travel distance, the improvements with the harvester only have been of 1903 m and with a hybrid system of 5845 m. In headwind condition, considering the hybrid system, the improvement has been of 3047 m. The preliminary results above show that a dipole rectenna array with 22,066,058 elements in parallel and 62,800 elements in serial connections are sufficient to recharge a small battery on board a MAV. Finally, the angleSensors of2019 incidence, 19, 1771 of solar radiation on a dipole rectenna array embedded into the frame of a 18MAV of 21 may vary with the instantaneous change in orientation of a flying MAV. When the pole orientation of rectennas does not match with the electric field of the incident solar radiation, the rectenna may not match with the electric field of the incident solar radiation, the rectenna may not generate a not generate a high output power. To overcome this polarization problem, a polarization-free dipole high output power. To overcome this polarization problem, a polarization-free dipole rectenna array rectenna array configuration with six difference polarity directions is mandatory. For this application, configuration with six difference polarity directions is mandatory. For this application, the state of the the state of the art recommends a typical arranging of the dipole rectenna elements in a circle [40–42]. art recommends a typical arranging of the dipole rectenna elements in a circle [40–42].

Figure 17. Solar radiation versus time. Figure 17. Solar radiation versus time. 4. Energy Storage 4. Energy Storage Figure9 shows the working principle of the novel harvester. From this ﬁgure, it is possible to see that theFigure harvester 9 shows does the not working feed the principle load directly of the butnovel recharges harvester. a small From battery. this figure, In fact, it is the possible harvester to seehas thatan intermittent the harvester nature, does which not feed makes the the load voltage directly and but current recharges unstable. a small On battery. the other In hand, fact, the harvesterbattery output has an can intermittent supply the loadnature, with which a stable makes voltage the andvoltage current. and Forcurrent EH application unstable. On is imperative the other hand,to use the rechargeable battery output batteries. can supply the load with a stable voltage and current. For EH application is imperativeIn this to study, use rechargeable a Li-Po ultra-fast batteries. rechargeable battery with 3.7 V nominal voltage and a capacity of just 400 mAh is used, taking into account the tradeoﬀ between battery life and its size. To extend the mission parameters, Equation (50) explains, as at any instant t, the available energy must be greater or equal to the required energy for supporting the load:

E (t) + E (t) E (t) + E (t) (50) harvested stored ≥ load loss where Eharvested is the generated energy at any instant t by optical dipole rectenna array, Estored is the energy stored in a storage device (battery), Eload is the energy required from the load and Eloss is the energy dissipated due to the Joule effect. The maximum charge current rate fixed from the DC-DC boost converter is 4500 µA. During the normal operation, a Li-Po battery is discharged only for 20% of its capacity, therefore the recharging time is of 2.5 h. At this state, it is easy to recharge the battery with the power delivered by the energy harvester, rather than charging the battery at a later stage, when it is fully discharged. This preliminary study shows that the current flow of this energy harvester is not enough to fastly charge the battery while the MAV is mid-air, however, in the near future, a hybrid system constituted by this energy harvester and a novel battery able to be recharged during the flight, in less than 1 min, could power the system indefinitely [43–45].

5. Conclusions In this paper, a possible solution on how to improve mission parameters (MD and travel distance), for a micro air vehicle (MAV) has been given. This paper has been divided into two different parts. Initially, we have proposed a model that identifies all factors that determine the power consumption of a drone. The results of the MD for several scenarios proposed shown that in hovering condition MD is 18.16 min. In vertical conditions, MD is only 17.20 min. In horizontal condition, MD is only 9.38 min. Finally, in worst-case condition, i.e., in headwind conditions, MD is only 3.17 min. MD Sensors 2019, 19, 1771 19 of 21 and travel distance are limited by the capacity of the energy storage system. In the second part, to answer on how to improve mission parameters, we have designed and simulated a harvester using a flexible optical dipole rectenna array tuned at a frequency of 350 THz thought to be implemented into the frame of a MAV. Moreover, a novel method to make the impedance of the harvester match that of load has been proposed. In light of the results above, Equation (9) shows improvements for the mission parameters. For MD, the improvements with the harvester only in horizontal condition have been of 16.30 min. With hybrid system (battery and harvester), the improvements in horizontal and headwind conditions have been respectively of 50 min and 15.30 min. For travel distance, the improvements with harvester only has been of 1903 m and with hybrid system of 5845 m. In headwind condition, by taking into account the hybrid system, the improvement has been of 3047 m. This is in line with theoretical and simulations results because the power captured by the nano-antennas is largely lost on the rectifier. Finally, in the next future, a hybrid system constituted by this harvester and a novel battery able to be recharged during the flight in less than one-minute could power the system indefinitely. In light of the considerations, which were obtained from the results discussed above, we can assert that nano-antennas, although still at the initial stage and not of immediate application, represent a new, stimulating and not yet consolidated topic, which will be able to foster new research activities in the aeronautic field.

Author Contributions: In the article entitled: “Evaluation of a Novel Energy Harvester for Powering Small UAVs: Performance Analysis, Model Validation, and Flight Results”, the tasks are divided as follows: R.C. addresses the problem to define a new state of the art involving novel harvesters to improve the parameters of flight as travel distance and mission duration (MD) currently limited only to a few meters and minutes of small Unmanned Air vehicles (UAVs). He focused his work on a novel model identifying all factors that determine the power consumption of a drone with a negative impact on mission parameters. This model has been conceived, designed and simulated with Mathcad tool with the help of F.D.P. moreover he, provided advice and feedback on operational requirements for unmanned vehicles. R.C. wrote the body of the manuscript and F.D.P., reviewed the manuscript and provided input for all items regarding UAV electric subsystem (frame, propellers, motors, batteries, and so on). P.L., from the proposed model above considering the Energy Harvesting (EH) technique investigated the feasibility of improving the mission parameters with a novel harvester based on plasmonics technology. This novel harvester based on plasmonics nanoantennas has been conceived, designed and simulated with a commercial full-wave 3D electromagnetic simulator CST Studio Suite 2016. P.L., wrote and reviewed the manuscript and provided advice and feedback on energy requirements to improve the mission parameters, finally she has been also the supervisor of all manuscript. All authors worked together, therefore, the contribution is equally shared. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest.

Abbreviations The following abbreviations are used in this manuscript:

UAVs Unmanned Aerial Vehicles MAVs Micro Air Vehicles MD Mission Duration EH Energy Harvesting SWIR Shortwave Infrared PV Photovoltaic TWR Thrust to Weight Ratio AC Alternating Current DC Direct Current MIM Metal Insulator Metal diode MIIM Multi Insulator Metal diode

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