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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMSI: REGULAR PAPERS, VOL. 56, NO. 11, NOVEMBER 2009 2519

Design of a Solar-Harvesting Circuit forBatteryless Embedded Systems

Davide Brunelli, Clemens Moser, Lothar Thiele, Member, IEEE, and Luca Benini, Fellow, IEEE

AbstractThe limited battery lifetime of modern embedded sys-tems and mobile devices necessitates frequent battery rechargingor replacement. Solar energy and small-size photovoltaic (PV)systems are attractive solutions to increase the autonomy ofembedded and personal devices attempting to achieve perpetualoperation. We present a batteryless solar-harvesting circuit thatis tailored to the needs of low-power applications. The harvesterperforms maximum-power-point tracking of solar energy collec-tion under nonstationary light conditions, with high efficiencyand low energy cost exploiting miniaturized PV modules. Wecharacterize the performance of the circuit by means of simulationand extensive testing under various charging and discharging

conditions. Much attention has been given to identify the powerlosses of the different circuit components. Results show that oursystem can achieve low power consumption with increased effi-ciency and cheap implementation. We discuss how the scavengerimproves upon state-of-the-art technology with a measured powerconsumption of less than 1 mW. We obtain increments of globalefficiency up to 80%, diverging from ideality by less than 10%.Moreover, we analyze the behavior of supercapacitors. We findthat the voltage across the supercapacitor may be an unreliableindicator for the stored energy under some circumstances, and thisshould be taken into account when energy management policiesare used.

Index TermsDCDC power conversion, embedded systems,energy harvesting, maximum-power-point tracking (MPPT),

photovoltaic (PV) cells, power supply, wireless sensor networks(WSNs).

I. INTRODUCTION

THE INTEREST in supply circuits that harvest energy from

the surrounding environment for powering embedded sys-

tems has been increasing over the last years [1][4]. Thanks

to the progress in low-power design, research has greatly re-

duced the size and the power consumption of distributed em-

bedded systems, and the autonomy of these systems can be

further increased by energy-harvesting techniques. Nowadays,

small solar panels suffice to ensure continued operation, and

several photovoltaic (PV) harvesting circuits have been recently

proposed for this purpose [5], [6].

Manuscript received November 27, 2007; revised October 27, 2008. Firstpublished February 18, 2009; current version published November 04, 2009.This work was supported by the European Network of Excellence ArtistDesign.This paper was recommended by Associate Editor C. K. Tse.

D. Brunelli and L. Benini are with the Department of Electronics, ComputerSciences and Systems (DEIS), University of Bologna, 40136 Bologna, Italy(e-mail: [email protected]; [email protected]).

C. Moser and L. Thiele are with the Computer Engineering and NetworksLaboratory, Swiss Federal Institute of Technology (ETH) Zurich, 8092 Zurich,Switzerland (e-mail: [email protected]; [email protected]).

Digital Object Identifier 10.1109/TCSI.2009.2015690

The output characteristics of a PV array vary nonlinearly

when temperature or irradiance conditions change. Therefore,

maximum-power-point tracking (MPPT) techniques are ex-

ploited for adjusting the operating point of the solar panel

in order to obtain the maximum output power from

the PV module. So far, MPPT methods have been roughly

classified into two groups: large-scale PV power systems,

generally making use of digital signal processors (DSPs) or

microcontrollers [7][9], and small-scale PV power systems,

usually without digital controllers. They are less accurate, but

they are cheaper with an advantageous cost efficiency in PV ap-plications below 50 W [10], [11]. With the increased interest in

harvesting technology, a third class of MPPT methods, focused

on microscale PV power systems with some square-centimeter

area, has recently emerged. Compared to well-known industrial

PV systems, these approaches address the tracking of the MPP

with power consumption of a few milliwatts since the maximal

energy drawn from PV modules is very limited (i.e., hundreds

of milliwatts) due to the small size of the cells.

Many tracking techniques have been developed for the former

two classes in the last years [12]. The hill-climbing method

directly computes the MPP by measuring the gradient of the

output power in dependence of the output voltage [13]. A similartechnique, namely,perturb and observe[6], [14], continuously

perturbs the systems in order to detect the MPP, and the oper-

ating voltage of the panel oscillates around it. The incremental

conductancemethod [15] is based on the fact that the slope of

a PV power curve is zero at the MPP, positive on the left of the

MPP, and negative on the right. Through the help of a simple

calculation, a microcontroller periodically checks and tunes the

current operating point [16], resulting usually in good accuracy

and efficiency. The fractional open-circuit voltage (FVoc) tech-

nique [10] exploits the existing nearly linear relationship be-

tween and the open-circuit voltage under varying ir-

radiance and temperature levels. Since the linear factor depends

on the characteristics of the used solar cell, it usually has to becomputed beforehand by empirically determining the desired

operating points for the specific cell. The technique is, how-

ever, very easy and cheap to implement and does not require

any digital controller. Similar to FVoc, fractional short-circuit

current methods rely on the fact that there also exists an approx-

imately linear relationship between the short-circuit current

and [7] and that the linear factor has to be determined for

each specific solar cell. Even if this ratio is usually more stable

than fractional Voc under irradiance and temperature variations,

measuring during operation is usually more complicated for

low-power embedded systems that need extra current sensors,

adding complexity and cost to the system. Finally, [17] exploits

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the mean-value theorem to provide the analytic solution of a

point in a close neighborhood of the MPP. Thus, the system

never works on the actual MPP but close to it. Mathematical

solutions are stored in a lookup table of at least 64 kB and are

addressed for all possible combinations of the input variables,

such as the measured short-circuit current, open-circuit voltage,

and temperature.Unfortunately, even if the experience in exploiting large-scale

PV modules has rapidly advanced in the last two decades, re-

search on solar scavenger for small and low-power embedded

devices is characterized by additional new issues to tackle. In

fact, MPPT implementation is practicable only if the power con-

sumed by the tracker is significantly lower than the amount of

output power that it gains. For this reason, most of the proposed

solutions for industrial electronics are unsuitable for low-power

embedded systems or must be reconceived at best. Moreover,

small-scale solar harvester systems usually experience a sub-

stantial interaction between the scavenger circuit and the pow-

ered device, so designing a solar harvester separately from the

embedded systems is not always feasible [6].In this paper, we present a highly efficient solar energy har-

vester for batteryless embedded systems, starting from the cir-

cuit described in [18]. We present an extensive evaluation in

terms of simulations and measurements focusing on the power

consumption and efficiency of the PV harvester. The adoption

of a simple MPPT circuit allows us to shrink the size of PV mod-

ules and to reduce the capacity of the energy reservoir. In fact,

the harvester circuit consumes less than 1 mW and diverges from

the optimal situation by less than 10%. Since it does not use any

microcontroller or DSP for MPPT, the embedded system can

be shut down when unused to save energy. Moreover, it does

not require a precharged storage device (such as rechargeablebatteries), and it works even if the energy buffers are empty.

Experimental results show increments of global efficiency up to

80% and demonstrate the flexibility and robustness of our ap-

proach. All these features make the proposed solution suitable

for low-power embedded systems.

The advantage of solar energy over other forms of environ-

mental energy is that the available solar energy can be pre-

dicted, at least to some extent. This allows one to implement

power management techniques and to plan and optimize the fu-

ture system activity in order to achieve a more sustainable op-

eration. These energy-harvesting-aware features might require

also the knowledge of the current available energy [19], [20]

to tune the system behavior. Clearly, if there is a relation be-

tween conversion efficiency and energy buffer level, the en-

ergy prediction will require more computing effort and may re-

duce the accuracy. Moreover, the charging process of an en-

ergy storage device (ESD) by means of a solar cell is a non-

linear process. The amount of energy that can be harvested de-

pends on various factors, such as the voltage level of the ESD

and the incident-light intensity. Common MPPT systems ex-

ploit a combination of different storage devices (batteries and/or

supercapacitors) and methods that require microcontrollers or

power-consuming devices for the management [21]. Such hy-

brid systems use the advantages of both technologies providing

both high power and energy density. For example, a combina-tion of thin-film solid-state batteries (TFBs) and supercapacitors

could be the optimal solution to bridge the mismatch between

available and required energy in small size. Unfortunately, get-

ting the information about available stored energy to perform

power management techniques becomes too complex in systems

with low computation capability, such as wireless sensor net-

works (WSNs). For this reason, in this paper, we focus on super-

capacitors as unique ESDs. They have better cycle life and tem-perature characteristics and can cope with unpredictable peak

power demands by wireless communication or advanced sen-

sors (e.g., CMOS cameras in wireless video sensor networks

[22]). Furthermore, supercapacitors offer a simpler way to esti-

mate the stored energy than batteries or TFBs, but we find that

the voltage across the supercapacitor may lead to misinterpre-

tations if previous charging/discharging cycles or precharging

is not taken into account. Therefore, we investigated deeply the

characteristics of ultracapacitors and verified that the classical

formula , which is used to determine and

optimize the efficiency of energy-scavenging systems in the lit-

erature [23], [24], needs a revision.

This paper is organized as follows. Related work will re-viewed in the next section. Section III presents experiments that

show the behavior of supercapacitors used as storage devices

for PV harvester circuits. Starting from the description of the

used MPPT method, Section IV describesthe implementation of

our PV harvester. Section V shows experimental results and the

achieved performance, followed by an analysis of the cost im-

pact of such solutions, considering the scalability of the system

for small and distributed embedded systems (e.g., WSNs). Fi-

nally, Section VII concludes this paper.

II. RELATEDWORK

Several techniques for the MPPT of PV arrays have been pre-sented, and the number of proposed implementations has grown

significantly in recent years. Techniques vary in many aspects,

such as complexity, cost, or accuracy of the tracking method.

Large-scale PV power systems are out of the scope of this paper,

but a detailed survey with the great majority of articles presented

on MPPT can be found in [12] and [25].

Concerning energy harvesters using small and microscale PV

modules, [11] presents a cost-effective MPPT system that can be

directly integrated onto solar arrays. The authors focus on the

issue that it is more cost effective to design high-efficiency low-

power MPPT systems in order to scale down the PV array and

storage devices, resulting in a lower cost system that is suitable

to be utilized on wider application scenarios (e.g., distributed

embedded systems and WSNs).

References [23] and [24] propose systems that attempt to en-

able perpetual operation of low-power embedded systems. In

both solutions, the replenishment of the energy buffers is per-

formed by a direct connection between the PV panel and the

storage device, which forces the operating point to the capac-

itor voltage . Both solutions do not perform any MPPT, al-

though the size of the panel and the collected power permit some

forms of power management.

In [5], [6], and [21], low-power systems for tracking the peak

power point are presented. They exploit microcontroller and

analog circuits to track MPP during light variations. The size ofthe adopted PVmodulesis greater than20 , which isenough

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to provide tens of milliwatts and to perform an efficient power

collection. In particular, [21] supports different power sources

and tries to eliminate the overhead in cost and power consump-

tion caused by a microprocessor-based algorithm for MPPT.

The harvesting unit is independent from the target system, and

it requires the presence of a rechargeable battery as secondary

buffer to work when the primary buffer is empty. The adoptedtechnique to estimate the position of the MPP relies on a light

sensor (e.g., photodiode), and they associate this information to

the solar-cell characteristic. Since the relationship between light

sensor and solar panel is usually almost empirical, this method

is less accurate than a simple fractional Voc. Additionally, the

sensor has a sizable power consumption that influences the en-

ergy budget of the system.

III. SUPERCAPACITORANALYSIS

The occasionally unexpected behavior of supercapacitors and

its influence on the performance of an energy scavenger have

never been investigated in the context of PV harvesters for low-

power embedded systems. In fact, supercapacitors are usually

employed in the power supplies of the driver and logic circuits

to allow them to operate in the absence of the primary power

source. Conventional ESDs, such as batteries and aluminum

electrolytic capacitors, must often be replaced during the life-

time of a product. Supercapacitors do not suffer from aging

effects, and they do not experience irreversible chemical reac-

tions. Moreover, they do not suffer from dry-up problems, unlike

aluminum electrolytic capacitors. These features allow superca-

pacitors to fill the gap between conventional capacitors and bat-

teries and to be an attractive solution as energy reservoirs for

low-power distributed embedded systems.In Section II, we described several energy-harvesting sys-

tems that have opted for supercapacitors as at least one of the

used energy reservoirs. This papers always assume a behavior

that is identical to that of conventional capacitors with negli-

gible internal losses. Supercapacitors accumulate charges at the

interface between the surface of a conductor and an electrolytic

solution. Even if they store energy in a similar way as lower

value capacitors, experiments showed, however, that superca-

pacitors do demonstrate a different behavior from normal ca-

pacitors under certain conditions, and that is not covered by

datasheets. The validity of the capacitor energy-content formula

is, however, of substantial importance to several applicationsof embedded systems. In papers like [23] and [26], the infor-

mation about available stored energy is used to estimate the

performance of the system and to perform power management

techniques.

Electrical charge is stored in the double layer of a super-

capacitor when an external voltage is applied. The flow of

charges across the interface is not an instantaneous process, and

it is followed by a series of charge-distribution processes that

can take a considerably long time. To represent the combination

of many processes with different time behavior, a simplified

model consisting of three branches with time constants on the

order of a few seconds, a few minutes, and tens of minutes,

respectively, has been proposed [27], [28] and used for ourconsiderations.

Fig. 1. Internal charge-distribution process after the first charging cycle.(a) Voltage decreasing during the delay interval. (b) Difference measuredenergy and formula.

A. Charge Distribution

Supercapacitors experience several charge-distribution pro-

cesses with different time constants, even in isolated and discon-

nected state. This makes it difficult to identify the process that

is responsible for voltage variations. After just being charged

for a short period, a disconnected supercapacitor will exhibit a

decreasing voltage. This decrease is mainly caused by a charge

distribution within branches. To be able to visualize the internal

charge-distribution processes of the supercapacitor, a measure-

ment that is similar to that in [27] was performed (see Fig. 1).

A delay period of 10 min was inserted in between the charging

and discharging phases of a 22-F supercapacitor. The capacitor

was charged from an initial voltage of 12.5 V, and during the

delay, the charged supercapacitor was disconnected.

The graph in Fig. 1(b) shows the result of the experiment, in-

cluding two different plots, denoted as and . rep-resents the energy content of the supercapacitor computed by

measuring its output voltage and using the

formula, while is obtained by measuring first the energy de-

livered by the generator for the charging phase and then, during

discharging, measuring the energy delivered to the load. The

energy is the total energy provided to the supercapacitor

during charging, while represents the total energy deliv-

ered by the supercapacitor during discharging. Notice that the

supercapacitor does not behave as an ideal storage device as

is larger than . Hence, we can define thecycle efficiencyof

the supercapacitor as

(1)

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Fig. 4. and plots of the used PV module.

Fig. 5. Fractional open-circuit voltage relation between and undervarious conditions.

where is the generated current, is the reverse saturation

current, is the electronic charge, is a dimensional factor,

is the Boltzmann constant, is the temperature (in kelvins), and

is the series resistance of the cell. The characteristic plot of

the PV module adopted in our solar harvester is shown in Fig. 4.

The aim of an MPPT method is to harvest as much energy

as possible from the available solar panel by operating contin-

uously as close as possible to its MPP adapting to irradiance

and temperature condition variations. Among the numerous pro-

posed methods, the fractional open-circuit voltage technique is

the most effective one in terms of cost and power consumption

for microscale PV systems.Fig. 5 shows the nearly linear proportional relationship

between the operating voltage at the MPP of a PV

module and the open-circuit voltage under different

irradiance conditions. By a periodical brief disconnection of

the PV module from the circuit, the MPP can be estimated by

measuring its . This conceptually simple solution presents

two important practical drawbacks: 1) The system experiences

a temporary drop of power from the panel, and 2) this control

method is not a real tracking method but a quasi-power point

tracker. In fact, the harvester tunes the tracker according to

the value of the last sampling, and it does not try to find

continuously the current MPP. If the sampling rate is low,

or the environmental conditions change quickly, this methoddecreases the accuracy and responsiveness.

Fig. 6. Nearly linear relation between and the pilot cell under variousconditions of irradiance.

To overcome both problems, we employ an additional small

PV module acting as a reference module. Fig. 6 shows how the

pilot cell follows almost linearly the behavior of the main PVmodule during light variations. The plot displays the operating

voltage of the pilot cell over the of the main

array. For clarity, we also plot the same and the of

the main module to verify the similarity of the variations. As

shown, the behavior is nearly linear, so it is possible to exploit

the voltage of the reference cell as a reference signal

for tracking the position of the MPP

(3)

The reference cell has been carefully selected to represent the

characteristics of the principal PV array, and since it is exposed

to the same light conditions as the main solar cell, it is reason-

able to suppose that both PV modules will be built in the same

manufacturing process. For the proposed harvester, we adopt the

CPC1824 [29], a monolithic PV string of solar cells of only 9

. Providing continuously feedback irradiance information,

we can perform continuous tracking, and the power from the

main cell will always be used to power the embedded system or

stored into the energy buffer. Moreover, using a pilot cell has

several additional advantages: 1) It is not necessary to provide

a power supply for a light irradiance sensor, as in [21], and (2)

as the pilot cell can be extremely small, it does not waste power

that could be used for powering the system, it does not increase

the system size and cost, and it does not require extra compo-nents, such as photodetectors and signal-conditioning circuits.

B. Harvester Platform

The hardware architecture of the solar scavenger is shown

in Fig. 7. It is realized using commercial off-the-shelf (COTS)

parts, and it consists of three units: the MPP regulator, the

MPP tracker, and the MPP power supply. The MPP regulator

that is usually exploited in MPPT techniques is a buck power

converter with a 2.2- inductor. It operates in continuous

mode because the maximum capacitor voltage is lower

than the nominal operating voltage of the solar cell. Since the

input power varies continuously with the atmo-spheric conditions, the circuit adjusts dynamically the output

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Fig. 7. Conceptual diagram of the harvester platform.

pulsewidth-modulated (PWM) signal to track the maximum

operating point supported by a small capacitance of 470 nF at

the input. In our work, narrow pulses with variable duty ratioare generated by the tracking unit to drive a PMOS transistor

with minimum power losses due to parasitic resistance and

switching actions.

The core of the scavenger is the MPP tracker that attempts

to obtain the maximum achievable power from the solar cell.

The operating voltage is led to one of the inputs of the

ultralow-power comparator LTC1440 [30], which compares to

the reference derived by the pilot cell using a voltage

divider and generates the PWM control signal for the

buck converter. By adding hysteresis, one threshold voltage is

replaced by a lower and an upper threshold. The hysteresis

prevents unwanted switching that would occur because of the

noise of the solar cell and pilot cell. In this way, the actual

operating point is not a fixed value but oscillates around the

MPP. Narrowing the hysteresis around the estimated MPP

means operating at higher switching frequencies, with higher

conversion efficiency, because the solar cell is confined to a

smaller voltage range.

We intensively simulated the circuit before implementation,

and a simulation plot is shown in Fig. 8. This specific simulation

depicts the startup of the MPP tracker. The curve represents

the current going through the inductor in the direction of the

supercapacitor, while represents the current leaving the

solar cell. The small negative dent of at the beginning of the

simulation is caused by the fact that the comparator still needed

to start up. For a moment, the switch was closed, which caused

the current to flow from the slightly precharged supercapacitor

to the input capacitor . Moreover, the figure clearly shows

how the PV voltage oscillates around the estimated MPP.

Different from the microscale solar scavengers proposed in

the literature, the tracker is still able to operate, even when the

energy reservoir is empty. Indeed, the MPP power-supplyunit,

with two low-threshold diodes, leads two possible power sup-

plies to the harvester comparator, and only one source can be

employed at the same time. With the supercapacitor being empty

and the dc/dc converter being turned off, the tracker is still able

to work if the solar cell supplies a high-enough voltage. Thisway of supplying power to the harvester tries to ensure proper

Fig. 8. Simulation of the startup of the MPP tracker.

Fig. 9. Embedded platform powered by the solar harvester.

operation of the MPP tracker, even when the supercapacitor is

completely discharged and permits one to have the highest con-

trol signal to switch off the PMOS transistor, suppressing

any drain current that could decrease the performance of the

power converter.

The proposed solar harvester does not need any specific mi-

crocontroller or computational platform. It is very flexible and

can supply a large variety of low-power embedded systems. In

particular, we use a 50-F ultracapacitor as ESD, while the output

dc/dc regulator is an LTC3401 step-up voltage regulator [31],

which provides a stable 3.3-V voltage with up to 97% efficiency.

In Fig. 9, we show a photograph where we power a microcon-

troller-based embedded system equipped with several sensor de-

vices and radio communication capability. It is a commercial

sensor platform for WSN applications called Tmotesky[32]. It

is equipped with a 16-bit RISC CPU architecture with 8-kB of

on-chip flash memory and 256 B of RAM. It accommodates at

least seven different sensors (e.g., temperature, humidity, etc.)

and can transmit radio information to other sensor nodes or di-

rectly to a PC using a low-power radio within a range of about

50 m. The peak power consumed by our test application is 90

mW when the radio is active. Adjusting the communication rate,

it is possible to guarantee an average energy intake that is higher

than the energy required by the Tmoteskyplatform and to sus-tain the application.

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Fig. 10. Power consumption of the comparator in the MPP tracker duringcharging and idle phases.

V. EXPERIMENTALRESULTS

The core of this paper is the design of an autonomous energyharvester that can be used as simple plug-and-play enhancement

to embedded systems and wireless sensor nodes. A test setup has

been implemented exploiting a 112- PV module, with an

irradiation that forces the solar cell to produce about 50 mW. We

evaluated the performance by measuring the power consumed

by the MPPT system and the efficiency of the PV harvester.

A. Power Consumption

The average power consumed by the whole circuit is below

1 mW, and the main contributions are given by the comparator

of the MPP tracker and by the switching activity of the MPP

regulator. The power consumed by the comparator is shown inFig. 10. The measurement was performed using a 50-F superca-

pacitor. After a complete recharging, the PV panel was covered

in order to emulate the absence of environmental energy and to

power the target system using only the energy in the reservoir.

We repeated several cycles and measured the power consump-

tion also during idle periods. The figure shows that a maximum

of 700 is used at the beginning of charging, during which

the frequency of the MPP tracker is the highest. In the charging

interval, the frequency and the average comparator power de-

crease. Right before the end of charging, only 150 is re-

quired. When the switch of the MPP regulator is open, the cir-

cuit consumes less than 0.5 mW -

. Peaks of about

1 mW have been measured when the comparator switches the

MOS transistor on - , but the actual value depends on

the voltage of the supercapacitor .

B. Harvester Efficiency

We could define the harvesting process efficiency as follows:

(4)

where is the power at the MPP and

is the average power transferred to the energy buffer.

Using this definition, only scavengers that always work at thecorrect MPP can achieve efficiencies that are close to 100%,

Fig. 11. Efficiency of power conversion.

and losses are only caused by power dissipation of system com-

ponents. In our tests, has also been evaluated considering

the dc/dc converter that affects the result with its own intrinsic

losses. Fig. 11 shows how efficient the proposed method is in

replenishing the supercapacitor over elapsed time. The contin-

uous curve represents the efficiency of the charging behavior in

the case of a direct connection between the PV panel and the

storage device. The dashed curve shows the ideal trend: The su-

percapacitor is constantly refilled with the maximum available

power . The charging behavior using the proposed energy

scavenger is plotted as the curve with dots.

The curve with triangles has been obtained, excluding from

the scavenging platform the MPP power-supplyunit and using

the dc/dc as a unique source. This configuration is implemented

by several scavenging solutions, such as [21]. It is evident that

they cannot perform better since their tracking circuit does not

operate with an empty energy reservoir. Nevertheless, the har-

vester is still able to charge the ESD because the comparator

output is low, switching the PMOS transistor on and guaran-

teeing a conductive path to the supercapacitor. In the interval

between , the dc/dc attempts sporadi-

cally to start up, providing temporary supply to the tracking

circuit, which causes a slight increment of the average ef-

ficiency of the system. Only when the voltage level of the

supercapacitor is high enough to turn the dc/dc on steadily

that the scavenger can work properly

and increase the efficiency.

When the dc/dc regulator is active, it introduces an over-head due to its own power consumption, which decreases the

charging efficiency. In Fig. 12, we try to estimate this phenom-

enon, illustrating the charging behavior of a direct connection

and of our harvester with and without an output dc/dc converter

(in the case of our harvester, the tracking circuit was powered

by an external source). As is known, a direct connection be-

tween the PV module and the supercapacitor is characterized

by a linear charging shape. Meanwhile, our scavenger presents

higher rate of conversion efficiency from the beginning. More-

over, we observe that the presence of an output dc/dc converter

decreases the charging slope as soon as the voltage of the en-

ergy buffer reaches , turning the converteron. The oscillations of the actual operating point of the solar

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Fig. 12. Overhead of the dc/dc converter in the charging process.

Fig. 13. Comparison of the operating point with and without a tracking circuit.

cell simulated in Fig. 8 were measured directly on the imple-

mented system. In Fig. 13, the ideal situation, shown in the

first photograph, is obtained when the MPP regulatoris driven

directly by a signal generator tuned at 100 kHz. A duty ratio

of the switching activity of the MOS transistor is defined as

, where is the switch-on time of the MOS tran-

sistor. The duty ratio is computed in order to match

accurately the actual of the solar cell at the given irradi-

ance. Under the same condition, our solar harvester oscillates

around (as shown in the middle photograph) because it

automatically generates a control signal with . Fi-nally, the operating point of the circuit without the tracking

Fig. 14. Efficiency of the tracking system.

system is plotted in the last photograph. Since the duty cycle

of the switching activity is very different from the ideal one

(in this case, ), the PV module will never operate atthe MPP.

Finally, we also evaluated how the tracking system influences

the energy conversion and the charging process. Fig. 14 shows

the efficiency defined in Section V-B using different irradiance

levels from the previous plots. Also, here, the lowest contin-

uous curve depicts the direct connection between the PV module

and the supercapacitor, while the dashed curve is the ideal case

using a constant as source. The curve with dots is ob-

tained with the proposed circuit and can achieve conversion ef-

ficiency up to 80%. As is shown, it approaches closely the ideal

curve, with a maximum error that is less than 10%. Excluding

theMPP tracker, the efficiency curve varies widely with the ir-radiance condition, ranging from the dashed curve to below the

continuous one, representing the direct connection. The figure

shows a case in which decreases to around 50% (curve with

squares).

VI. COSTANALYSIS

The proposed solar harvester can be very economical, and the

total cost is currently below 50 Euros. Furthermore, using (sur-

face mounting device) SMD technology in the PV panel itself

or directly onto the target system, it also occupies a small area.

However, the cost of the solar harvester is an important consider-ation that certainly influences the choice between components

(i.e., high-speed versus ultralow-power comparators, superca-

pacitors versus other ESDs such as TFBs, etc.), considering also

that high volumes of manufacturing will decrease the compo-

nent cost.

Of course, the contribution of the harvester to the global costs

may vary widely, depending on the supplied embedded system.

As simple analysis, Fig. 15 shows the cost distribution of the

system used for experimental results and shown in Fig. 9. The

pie chart shows that the PV module and the supercapacitor take

up to 84% of the total harvester cost, demonstrating that a har-

vester, which does not need any digital controller, increases thecost efficiency.

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Fig. 15. Cost distribution of the batteryless solar-powered sensor node.

VII. CONCLUSION

An integrated cost-effective PV harvester for low-power and

environmental embedded systems has been proposed. The adop-

tion of an MPPcircuit has led to several benefits, such as the

possibility to shrink the size of PV modules or to reduce the ca-

pacity of the energy reservoir. The presented circuit performs

a high-efficiency conversion through an ultralow-power circuit

that requires less than 1 mW. The estimation of the peak power

point is done automatically, using a small PV module as ref-

erence, whereby sensing operation does not require additional

power. The scavenger can be used with any kind of embedded

system. Experimental results have shown that the global effi-

ciency diverges from the ideal situation by less than 10%.

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Davide Brunelli was born in Italy on March 26,1977. He received the M.S. (summa cum laude)and Ph.D. degrees in electrical engineering from theUniversity of Bologna, Bologna, Italy, in 2002 and2007, respectively.

From 2005 to 2007, he was an Academic Guestwith the Swiss Federal Institute of Technology(ETH) Zurich, Zurich, Switzerland. He is currently

a holder of a postdoctoral position with the De-partment of Electronics, Computer Sciences andSystems (DEIS), University of Bologna. His research

interests concern the development of new techniques of energy scavengingfor wireless sensor networks (WSNs) and embedded systems, optimizationof low-power and low-cost WSNs, interaction and design issues in embeddedpersonal and wearable devices, and pervasive and ubiquitous computing.

Clemens Moser received the B.Sc. and Dipl.Ing.degrees in electrical engineering and informationtechnology from the Technical University of Munich,Munich, Germany, in 2003 and 2004, respectively.For his diploma thesis in 2004, he joined DoCoMoEuro-Labs to work on the topology aspects ofwireless multihop networks. He is currently workingtoward the Ph.D. degree in the Computer Engi-neering and Networks Laboratory, Swiss FederalInstitute of Technology (ETH) Zurich, Zurich,Switzerland.

His research interests include the design, analysis, and optimization of en-ergy-harvesting sensor networks.

Lothar Thiele (S83M85) was born in Aachen,Germany, on April 7, 1957. He received the Dipl.Ing.and Dr.Ing. degrees in electrical engineering fromthe Technical University of Munich, Munich, Ger-many, in 1981 and 1985, respectively.

Since 1994, he has been a Full Professor of com-puter engineering with the Swiss Federal Instituteof Technology (ETH) Zurich, Zurich, Switzerland,

where he is currently leading the Computer En-gineering and Networks Laboratory. His researchinterests include models, methods, and software

tools for the design of embedded systems, embedded software, and bioinspiredoptimization techniques.

Dr. Thiele was the recipient of the Dissertation Award from the TechnicalUniversity of Munich in 1986, the Outstanding Young Author Award fromthe IEEE Circuits and Systems Society in 1987, the Browder J. ThompsonMemorial Award from IEEE in 1988, and the IBM Faculty Partnership Awardin 20002001. In 2004, he joined the German Academy of Natural ScientistsLeopoldina. In 2005, he was the recipient of the Honorary Blaise Pascal Chairof University Leiden, The Netherlands.

Luca Benini (S94M97SM04F07) receivedthe Ph.D. degree in electrical engineering fromStanford University, Stanford, CA, in 1997.

He is currently a Full Professor with the De-partment of Electronics, Computer Sciences andSystems (DEIS), University of Bologna, Bologna,Italy. He also is a holder of a visiting faculty positionwith the Ecole Polytechnique Federale de Lausanne,Lausanne, Switzerland. He has published more than350 papers in peer-reviewed international journalsand conference proceedings, four books, and several

book chapters. His research interests are in the design of system-on-chipplatforms for embedded applications. He is also active in the areas of en-ergy-efficient smart sensors and wireless sensor networks.

Dr. Benini is an Associate Editor for the IEEE TRANSACTIONS ONCOMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS and the

ACM Journal on Emerging Technologies in Computing Systems.

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