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General hardware architecture of an energy-harvested wireless sensor network node (EH-WSN) can be divided into power, sensing, computing and communication subsystems. Interrelation between these subsystems in combination with constrained energy supply makes design and implementation of EH-WSN a complex and challenging task. Separation of these subsystems into distinct hardware modules simplifies the design process and makes the architecture and software more generic, leading to more flexible solutions. From the other hand, tightly coupling these subsystems gives more room for optimizations at the price of increased complexity of the hardware and software. Additional engineering effort could be justified by a smaller, cheaper hardware, and more energy-efficient a wireless sensor node. The aim of this paper is to push further technical and economical boundaries related to EH-WSN by proposing a novel architecture which – by tightly coupling software and hardware of power, computing, and communication subsystems – allows the wireless sensor node to be powered by a thermoelectric generator working with about 1.5°C temperature difference while keeping the cost of all electronic components used to build such a node below 9 EUR (in volume).
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Software controlled low cost
thermoelectric energy harvester for
ultra-low power wireless sensor nodes
MICHAŁ MARKIEWICZ1,2 PIOTR DZIURDZIA2,3, TOMASZ KONIECZNY2, MAREK
SKOMOROWSKI1, LILIANA KOWALCZYK2, THOMAS SKOTNICKI2AND PASCAL URARD4
1Faculty of Mathematics and Computer Science, Jagiellonian University, ul. Łojasiewicza 6, 30-348 Cracow, Poland.
2Centre for Advanced Materials and Technologies CEZAMAT PW Sp. z o.o., ul. Poleczki 19, 02-822 Warsaw, Poland.
3Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Cracow, Poland.
4STMicroelectronics, 850, rue Jean Monnet, Crolles, 38926, France.
Corresponding author: Michał Markiewicz (e-mail: markiewicz@ii.uj.edu.pl).
This work was supported by ECSEL JU (Electronic Component Systems for European Leadership Joint Undertaking) under grant 692519
– PRIME (Ultra-Low Power Technologies and Memory Architectures for IoT).
ABSTRACT General hardware architecture of an energy-harvested wireless sensor network node (EH-
WSN) can be divided into power, sensing, computing and communication subsystems. Interrelation between
these subsystems in combination with constrained energy supply makes design and implementation of
EH-WSN a complex and challenging task. Separation of these subsystems into distinct hardware modules
simplifies the design process and makes the architecture and software more generic, leading to more flexible
solutions. From the other hand, tightly coupling these subsystems gives more room for optimizations at the
price of increased complexity of the hardware and software. Additional engineering effort could be justified
by a smaller, cheaper hardware, and more energy-efficient a wireless sensor node. The aim of this paper is
to push further technical and economical boundaries related to EH-WSN by proposing a novel architecture
which – by tightly coupling software and hardware of power, computing, and communication subsystems
– allows the wireless sensor node to be powered by a thermoelectric generator working with about 1.5°C
temperature difference while keeping the cost of all electronic components used to build such a node below
9 EUR (in volume).
INDEX TERMS Energy harvesting, thermoelectric generator, SMPS, IoT, TEG, Peltier module, boost
regulator, EH-WSN, self-powered IoT node.
I. INTRODUCTION
General hardware architecture of an energy-harvesting wire-
less sensor network (EH-WSN) node could be divided into
four subsystems: power subsystem (responsible for acquiring
energy from a power generator, converting it into electri-
cal energy and providing system supply voltage), sensing
subsystem (that measures specific physical phenomena and
performs analog to digital conversion), computing subsystem
(responsible for data processing and node management) and
communication subsystem (enabling wireless communica-
tion) [1]. The continuing trend for increasing performance,
miniaturization and reducing of power consumption led to
development of highly integrated System-On-Chip (SoC)
hardware architectures. Some of them consist of integrated
ultra-low power micro-controller units (MCUs) and radio-
frequency (RF) modules, and are well suited for wireless
sensing applications. Further integration of sensing sub-
system and power subsystem within SoC is not common,
mainly due to application specific requirements of WSN
nodes. However, analog-to-digital converters (ADC) typi-
cally present on SoC are frequently used for digitalization of
the electric signal from some types of sensors. In this paper
we push further technical and economical boundaries related
to EH-WSN by proposing a novel architecture that integrates
to a large extent power subsystem with a SoC of a WSN node.
The paper is organized as follows:
In section II, we describe the constraints imposed by
the communication and sensing subsystems and the
consequences of this for the power subsystem.
In section III, we focus on the most fundamental compo-
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Markiewicz et al.: Software controlled low cost thermoelectric energy harvester for ultra-low power wireless sensor nodes
nent of the power subsystem of a WSN node – a power
source, and more specifically, on a Peltier module as a
thermoelectric generator.
In section IV, we analyse two other elements of the
power subsystem: the energy storage and the voltage
converter.
In section V, we propose an ultra-low power wireless
sensor node architecture, in which power management
activities are delegated to the software executed by the
MCU.
Finally, in section VI, we show the results of our exper-
iments including tests of a thermoelectric energy har-
vesting wireless sensor node with power management
routines executed solely by the MCU.
In section VII, we discuss the obtained results.
II. POWER REQUIREMENTS OF WSN
The main activities of a wireless sensor node are [2]:
1) Data sampling and processing,
2) Network operations including routing, data transmis-
sion and reception.
These two activities are the most demanding in terms of
power consumption. From the other hand, each WSN node
could be only in one of two states:
1) Active, during which data is sampled and radio trans-
mission is carried out, characterised by high power
consumption,
2) Sleep, characterized by ultra-low power consumption.
Typically, a wireless node spends around 99% of the time in
sleep mode, so overall energy budget is dominated by energy
consumed in this mode [1]. Specific network architectures
define the duty cycle, as well as the way the node remains
synchronized with the network. Many WSNs base on the
IEEE 802.15.4 standard which defines a protocol for data
communication using low-data-rate, low-power, and low-
complexity short-range radio frequency transmissions [3].
One of them is GreenNet, a radio transmission protocol that
has been initially developed by STMicroelectronics and Uni-
versité de Grenoble, and lately by Cezamat within PRIME
(Ultra-Low Power Technologies and Memory Architectures
for IoT) project [4]–[6]. To dive into the level of details
required for specifying requirements for power subsystem
we will use this implementation as an illustrative example
of IEEE 802.15.4 wireless radio standard communication.
The GreenNet code has been enriched with debug infor-
mation helping in measurements of duration and energy con-
sumption of all activities performed by sensing, computing
and communication subsystems. The summary is presented
in Table 1. Power consumption waveform of the GreenNet
WSN node performing network synchronization is shown
in Fig. 1. The power consumption waveform of the Green-
Net WSN node performing sensor data acquisition, network
synchronization and data transmission is shown in Fig. 2.
As expected, power consumption of the GreenNet node is
significantly smaller when it is performing only network
synchronization (beacon reception) than in the case when it
is sensing and sending the values of sensors readouts.
TABLE 1. Power consumption during beacon reception and data transmission
within one beacon cycle of the GreenNet node working with IEEE 802.15.4
beacon-enabled radio transmission protocol (input voltage: 3 V).
Activity T [ms] I [mA] E [µJ]
MCU initialisation 3.5 1.42 14.91
Sensor data acquisition 4.2 5.34 67.28
Preparation to sleep 1.2 5.70 20.51
Inactivity 17.0 0.36 18.16
MCU initialisation 3.2 1.44 13.82
Preparation for beacon receiving 3.0 5.22 46.94
Radio set-up 0.7 6.23 13.08
Radio RX, sensing for a beacon 2.9 8.54 74.33
Radio RX, beacon header received 1.5 7.12 32.04
Radio RX, receiving beacon payload 1.8 8.46 45.66
Preparation to TX 1.4 6.76 28.41
Radio TX, sending data package 4.0 11.56 138.72
Radio RX, waiting for ACK 1.4 8.00 33.60
Radio off, preparation to sleep 1.3 5.70 22.21
Total 47.1 – 569.69
FIGURE 1. Power consumption profile of radio communication where there is
no sensor data transmission – only beacon reception (measured as voltage
drop over 5 shunt resistor, input voltage: 3 V).
FIGURE 2. Power consumption profile of radio communication where sensor
data is transmitted to the router (measured as voltage drop over 5 shunt
resistor, input voltage: 3 V). Initially the node wakes up just before expected
arrival of the beacon. Then it performs beacon reception, data transmission
and goes back to a sleep mode.
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Markiewicz et al.: Software controlled low cost thermoelectric energy harvester for ultra-low power wireless sensor nodes
The IEEE 802.15.4 defines low-complexity and low-cost
Reduced Function Devices (RFD) and more advanced Full
Function Devices (FFD) with routing capabilities. The proto-
col defines beacon-enabled mode which is critical for power
saving features of WSN nodes [1]. This mode is configured
by two parameters BO (beacon order) and SO (superframe
order), 0SO BO 14. Interval between beacons is
defined by equation:
BI = 2B O ·tbsf (1)
For a radio symbol lasting tsymbol = 16 µs in the 2.4 GHz
frequency band, and duration of the base superframe equals
to 960 symbols, we obtain tbsf = 960 ·ts= 15.36 ms. To
manage the communication, FFD should to be active for:
SD = 2S O ·tbsf (2)
Minimal activity time of a RFD in a single cycle is the sum
of time for beacon reception and transmission of data from
sensors. Without collision with other WSN nodes (which re-
quires retransmission), energy consumed during this activity
is about 570 µJ as shown in Table 1. The frequency of this
activity depends on BO parameter. For BO = 0 energy for
transmission is spent every 15.36 ms for BO = 1 – for every
30.72 ms, and so on. For larger BI, dominating component in
energy consumption equation is not transmission but energy
spent during sleep. The summary of energy expenses of a
RFD as a function of BO parameter is presented in Table 2.
TABLE 2. Energy consumption for different values of BO parameter of IEEE
802.15.4-compatible GreenNet WSN node. Energy spent on transmission is
equal to the value given in Table 1. We assume that he power consumption
during sleep time is equal to 3.58 µA.
BO BI [s] Esleep [µJ] Pavg [µW] Iavg [µA]
0 0.02 0.00 37 089 12 363.06
1 0.03 0.00 18 545 6 181.53
2 0.06 0.00 9 275 3 091.60
3 0.12 0.00 4 643 1 547.59
4 0.25 0.00 2 327 775.59
5 0.49 0.00 1 169 389.58
6 0.98 0.01 590 196.58
7 1.97 0.02 300 100.08
8 3.93 0.04 155 51.83
9 7.86 0.08 83 27.71
10 15.73 0.17 47 15.64
11 31.46 0.34 29 9.61
12 62.91 0.68 20 6.60
13 125.83 1.35 15 5.09
14 251.66 2.70 13 4.33
To summarise, as BO parameter is getting bigger, energy
consumed by a RFD during sleep mode prevails in total
energy budget. A FFD power budget is more complex to
analyse, because not only additional SI parameter has to be
taken into consideration but also network topology, support
of guaranteed time slots (GTS) and other factors. Having
power requirements of a RFD, we can proceed to the proper-
ties of the power source of an EH-WSN node.
III. THERMAL ENERGY HARVESTING FOR WSN
The main advantage of thermal energy harvesting for WSN
node applications is its abundance in various environments,
especially in a form of waste heat produced by machines
[7]. In comparison to the mechanical energy sources, like
vibrations, it is much more reliable as it does not require any
moving parts [8]–[10]. Its relatively low power conversion
efficiency (about 10%) could not match photovoltaic energy
harvesters, but is able to provide stable source of energy in
dark indoor industrial environments. Thermal energy could
be converted into electricity by a thermoelectric generator,
which principle of operation is described in the next subsec-
tion.
A. ELECTROTHERMAL MODEL OF A PELTIER MODULE
Operation of a thermoelectric module (TEG) could be de-
scribed by five phenomena: Seebeck, Peltier, Thomson, Joule
effects and thermal conductivity of materials [11]. The most
important – from the energy harvesting point of view –
is Seebeck effect, that expresses induced voltage VSas a
function of temperature difference across junction of two
different materials:
VS=S(ThTc)(3)
Symbol Sin this equation denotes the Seebeck coefficient,
i.e. the magnitude of this effect. For small changes in tem-
perature it could be approximated by SV
T.
From characteristics of thermoelectric generators [12]–
[14], we have other formulas describing power balance of a
working TEG:
Qc=SI Tc0.5·RI2K(ThTc)(4)
Qh=SI Th+ 0.5·RI2K(ThTc)(5)
P=QhQc=SI (ThTc) + RI2(6)
V=P
I=S(ThTc) + RI, (7)
where Qcdenotes amount of heat absorbed at the cold side,
Qh– amount of heat pumped over the TEG to the hot
side, P– electrical power delivered or received from/to the
TEG, I– electrical current flowing through the TEG, V
voltage over TEG’s terminals, S– Seebeck coefficient, K
Thomson coefficient. The value of internal resistance R can
be derived from equation (7), by making the temperature of
both sides the same: Th=Tc=T= 0. The value
of Seebeck coefficient can be obtained from equation (4) for
T= 0. The value of thermal conductivity coefficient K
can be derived from equation (4), assuming that no heat is
absorbed at the cold side, i.e. Qc= 0. This leads to formulas
for the three most important parameters which characterize
TEG as an energy harvesting power source:
R=V
I
T=0 (8)
S=Qc+ 0.5RI2
ITc
T=0 (9)
K=SI Tc0.5RI2
ThTc
Qc=0 (10)
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Markiewicz et al.: Software controlled low cost thermoelectric energy harvester for ultra-low power wireless sensor nodes
TABLE 3. Comparison of parameter and prices of selected Peltier modules. Unit prices for volume up to 100 pieces according to online catalogue of Digi-Key
electronic components distributor (accessed January 4th, 2020).
Model Manufacturer Dimensions [mm] IMAX [A] UMAX [V] QMAX [W] R [] Unit price [EUR]
TEC1-12706 MikroElektronika 40×40×4.0 6.4 14.4 50.0 [@25°C] 1.98 4.62
430856-500 Laird Thermal Systems, Inc. 40×40×3.3 15.4 25.0 236.6 [@25°C] 1.37 57.04
CP185039 CUI Devices 50×50×3.9 18.0 15.4 157.0 [@27°C] 0.66 50.92
LCC12-10-16LS Marlow Industries, Inc. 40×40×3.84 8.9 14.7 85.0 [@27°C] 1.32 215.87
CP90626257 CUI Devices 62×62×5.70 9.0 15.4 79.0 [@27°C] 1.31 84.46
TEC-30-40-127 Wakefield-Vette 30×30×4.0 2.5 15.4 21.4 [@25°C] 5.38 9.88
430857-501 Laird Thermal Systems, Inc. 52×52×3.3 15.4 36.0 340.6 [@25°C] 1.97 102.52
B. SELECTION OF A TEG MODULE
Commercially available Peltier modules cost between several
EUR and several hundreds EUR as shown in Table 3. The
best candidate should be able to provide enough energy to
power a RFD WSN node in scenarios where energy har-
vesting is performed with temperature difference of a few
degrees of Celsius (e.g. by being attached to an enclosure
of a working machine that dissipates some heat). Having
in mind that communication protocol that we are using can
be parametrized – as shown in the previous section – the
actual power consumption of a WSN node can be adjusted
according to the performance of the power source. In the
following sections we show how to make this adjustment, to
enable usage of the cheapest Peltier module available on the
market – TEC1-12706. According to Digi-Key, Mouser and
Botland parts distributors there are several manufacturers that
offer this model – some of them even for 2.85 EUR.
C. SIMULATIONS OF THE TEG PERFORMANCE
From performance curves shown in the data sheet of TEC1-
12706 and equations (8)-(10) we can estimate the values of
R= 2.76 Ω,S= 36.87 mV/K, and K= 333 mW/K.
It means that the open circuit voltage for the temperature
difference of a few degrees of Celsius is somewhere be-
tween one and two hundreds millivolts. Such a low voltage
is not sufficient to directly power any MCU. Determining
parameters of the power source is then critical for specifying
requirements of the voltage converter.
To learn how power output of the selected TEG depends
on the electrical load, and temperature difference between
hot and cold side, we performed a series of simulations.
As a starting point we used an equivalent electrothermal
circuit presented in Fig. 3. Using LTSpice simulator with a
customized model of a TEG from [11], we observed how
changes in the temperature of cold and hot side influence
open circuit voltage. The results are presented in Fig. 4. We
can see that for small changes in temperature, plots are indeed
linear, which means that we can treat Seebeck coefficient in
the given temperature range as a constant.
The second set of simulations is about investigating TEG
behaviour under load. The equivalent electrothermal circuit
used in LTSpice is presented in Fig. 5. The results presenting
output voltage VOUT as a function of load ILfor different
Tare shown in Fig. 6. From the plot we can read the
internal resistance of the TEG, which is equal to the slope of
FIGURE 3. The equivalent electrothermal circuit of TEC1-12706 Peltier
module used in LTSpice simulations. Ambient temperature Ta, temperature of
the cold side Tcand the hot side Thare controlled by functions VTa,VTc,
and VThrespectively, according to a electro-thermal analogy.
20 22 24 26 28 30
0
0.1
0.2
0.3
0.4
Hot side temperature Th[C]
Open circuit voltage VOC [V]
Tc= 20C
Tc= 21C
Tc= 22C
Tc= 23C
Tc= 24C
Tc= 25C
Tc= 26C
Tc= 27C
FIGURE 4. Relationship between open circuit voltage of the TEC1-12706
Peltier module and the temperature difference between its cold and hot side.
For temperature difference of 5°C across TEG’s sides, we have approximately
190 mV output voltage.
the plots. Having the output voltage as a function of current
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Markiewicz et al.: Software controlled low cost thermoelectric energy harvester for ultra-low power wireless sensor nodes
load we can also compute output power – as shown in Fig. 7.
FIGURE 5. The equivalent electrothermal circuit of TEC1-12706 Peltier
module used in LTSpice simulations now with load connected to the output
terminals. Ambient temperature Ta, temperature of the cold side Tcand the
hot side Thare controlled by functions VTa,VTc, and VThrespectively. The
load is controlled by IL.
0 10 20 30 40 50
0
0.1
0.2
0.3
0.4
Current load IL[mA]
Output voltage VOUT [V]
T= 2.5C
T= 5.0C
T= 7.5C
T= 10.0C
FIGURE 6. Relationship between output voltage of the TEC1-12706 Peltier
module and the temperature difference between its cold and hot sides T,
and load current. For T= 5°C(marked by grey triangles) this TEG is
capable of providing load current up to 30 mA while maintaining output voltage
above 100 mV.
From the simulations we learned, that for temperature dif-
ference of 5°C between TEG’s sides, we have approximately
190 mV open circuit voltage, and the load up to 30 mA does
not cause the output voltage to drop of below 100 mV. This
information will be very helpful in the selection of the power
management module, which is described in the next section.
IV. ENERGY STORAGE AND VOLTAGE CONVERTER
The storage element for harvested energy must have capacity
and internal resistance at levels sufficient to handle power
0 10 20 30 40 50
0
2
4
6
8
10
12
Current load IL[mA]
Output power POUT [mW]
T= 2.5C
T= 5.0C
T= 7.5C
T= 10.0C
FIGURE 7. Output power in function of the current load at different
temperature differences between TEG’s sides T. Smart power management
module of a WSN node could employ maximum power point tracking algorithm
(MPPT) to find the optimal load to maximize power extraction.
peaks occurring during radio transmission. The other factors
that have to be taken into account in selection of the energy
storage are: charging and discharging characteristics, self dis-
charge rate, cycling stability, and price. Energy management
subsystem of a WSN node typically store harvested energy
in either a rechargeable battery or a super-capacitor [15].
Their advantages and disadvantages are briefly presented
in Table 4. A rechargeable battery has this advantage over
supercapacitor, that it keeps the charge much longer. It is
critical feature in industrial applications, where a EH-WSN
node could be cut off for a while from the source of thermal
energy. For that reason we decided to use a rechargeable
battery instead of a super-capacitor. We selected VL-2020
vanadium-lithium battery which has nominal voltage of 3 V,
internal resistance of 30 , and nominal capacity of 20 mAh
(2.5 V cut off voltage).
TABLE 4. Comaparison of selected parameters of rechargeable batteries and
super-capacitors after [15].
Parameter Rechargeable batteries Super-capacitors
Charge/discharge efficiency Low High
Self discharge rate Low High
Energy density High Low
Power density Low High
Charging circuit complexity High Low
Price vs. capacity Low High
The energy acquired from a TEG cannot be directly trans-
ferred to the selected energy storage. The voltage has to be
increased, and the charging process has to be supervised,
to avoid overcharging. Hardware architecture of a typical
WSN node provides a specialized circuit – often an integrated
circuit – that takes care of those activities. This circuit
is called power management module, voltage converter, or
charge controller, and might also perform additional tasks
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Markiewicz et al.: Software controlled low cost thermoelectric energy harvester for ultra-low power wireless sensor nodes
like preventing the battery form deep discharging. We will
discuss its functionalities in the next subsection.
A. POWER MANAGEMENT MODULE
Ultra low power charging regulators suitable for EH-WSN
applications most often contain a DC/DC converter capable
of stepping up input voltage. Usually it is a boost con-
verter with an embedded switching transistor. Thresholds
controlling module behaviour (e.g. cut off voltage, battery
overcharging protection, battery discharging protection) are
almost always set up by resistor dividers that are connected
to internal analog comparators. Some modules offer extra
functionalities like maximum power point tracking (MPPT)
or low dropout regulators which deliver 1.8 V or 3.3 V output
voltage for the MCU. The most important parameters of
power management module are: quiescent current, minimum
input voltage for cold start (VCOLD ), minimum input voltage
(VIN ), conversion efficiency and price.
From dozens of commercially available ultra-low power
boost converters with battery management for energy har-
vesting applications, we selected three for further analysis:
ADP5090, SPV1050 and LTC3108. Comparison of their
features is presented in Table 5.
TABLE 5. Comparison of selected power management modules suitable to
work with a thermoelectric energy harvester.
Parameter ADP5090 SPV1050 LTC3108
Minimum input voltage for cold-start [mV] 380 550 20
Input voltage operation range [V] 0.1-3.3 0.15-18 0.02-0.5
Battery terminal charging threshold [V] 2.2-5.2 2.2-5.3 5.25
Leakage current at BAT pin [nA] 15 800 100
Leakage current at BAT pin VSYS =0V [nA] 0.5 1 -
Standby current [µA] 0.8 1 6
Efficiency @ VIN=0.02V [%] - - 35
Efficiency @ VIN=0.1V [%] 40 - 25
Efficiency @ VIN=1V [%] 85 85 -
Cost @ 1 unit [EUR] 4.9 2.8 6.3
Cost in volume (quantity) [EUR] 2.5 (1.5k) 1.3 (4k) 3.3 (2.5k)
SPV1050 has more functionalities in comparison to
ADP5090 and LTC3108. Furthermore, it may operate in
buck as well as in boost mode, and it is the best choice
in terms of cost. The main drawback is its relatively high
leakage current (0.8 µA). Both LTC3108 and ADP5090
have lower input voltage than SPV1050, so they are more
suitable for our scenario (for T= 5°C, the selected TEG
gives approximately 190 mV). LTC3108 has excellent cold
start voltage – only 20 mV. But unfortunately it requires
an external step-up transformer connected to a TEG, which
significantly increases the cost. ADP5090 module is not as
good as LTC3108 in terms of input voltage parameters, as it
has 100 mV minimum input voltage and 380 mV cold start
voltage (equivalent of T= 10°C). Moreover, it is also
relatively expensive.
For a reference we built a EH-WSN node with ADP5090
and tested it with the selected TEG as a power source. This
kind of solution – with separated power management module
– without a doubt has important advantages including clean,
well structured hardware architecture, very low quiescent
current, high reliability and additional features like discharg-
ing protection that cuts of power supply for the MCU and all
peripherals to prevent battery damage.
In the next section we will propose a novel power manage-
ment architecture, and show what benefits gives resigation
from a dedicated power management module.
V. A NOVEL SOFTWARE CONTROLLED LOW COST
ENERGY HARVESTER
Block diagrams of power management modules described
in the previous section have many elements in common.
All of them contain one or more analogue comparators,
internal voltage reference, DC/DC converter, and control
logic. The same components can be also found in various
SoC, including those with integrated IEEE 802.15.4 RF like
STM32WB55 or NXP MKW41Z. Pushing downwards the
cost of a WSN node with a thermoelectric energy harvester,
it is tempting to propose an architecture that utilizes compo-
nents already available in SoC to manage charging process.
This operation would simplify and minimize the bill of
material (BOM) but obviously makes programming of the
MCU more complex and challenging task.
A. POWER MANAGEMENT CONTROL LOOP
Moving the responsibility for the charging process to the
MCU requires a dedicated piece of software (firmware).
Unlike hardware implementation, where many things can be
done in parallel, MCU executes instructions in a sequence.
The control program can be presented in a form of infinite
loop with the following steps:
Measure the input voltage of the TEG,
If the voltage across the TEG is too low then switch to a
deep sleep for a while (after wake up restart the loop),
Otherwise, measure the battery voltage,
If the battery voltage is smaller than the maximum
allowed level then start DC/DC conversion,
Perform normal WSN operations as long as the energy
budget is balanced.
Voltage measurements can be made using analog to digital
converters (ADCs) commonly present in SoCs. Duration of
the sleep time can be controlled from the MCU by inter-
nal timers. However, performing DC/DC conversion by the
MCU with quiescent current at micro-ampere level is the
biggest challenge.
B. VOLTAGE CONVERTER CIRCUIT
To step up the input voltage we used a boost converter
topology [16] as shown in schematics in Fig. 8. The output
signal from the MCU is connected to the gate of the switching
transistor Q1. Turning on and off Q1 causes current flow
through L1. The inductor temporarily stores energy in its
magnetic field, and releases it through D1 to capacitor C2 and
battery B1. The MCU cyclically measures battery voltage to
prevent overcharging.
6VOLUME 8, 2019
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Markiewicz et al.: Software controlled low cost thermoelectric energy harvester for ultra-low power wireless sensor nodes
1
1
2
2
3
3
4
4
D D
C C
B B
A A
Title
Number RevisionSize
A
Date: 8/15/2019 Sheet of
File: C:\Users\..\BoostConverter.SchDoc Drawn By:
C1
L1
C2
GND
VDD
PWM
Liliana Kowalczyk
BoostConverter
1
VOC
GND
VDD
ADC1
PWM
ADC0
U1
MCU
VDD
GND
PWM
VOC
VDD
TEG
Q1
D1
B1
FIGURE 8. Simplified schematics of DC/DC boost converter controlled by a
MCU. If VOC measured by the MCU is below a threshold that guarantees
battery charging during WSN operation, then the MCU goes back to a deep
sleep mode just after measurement. Otherwise, the MCU starts generation of
PWM that switches Q1 transistor and charges the battery.
C. POWER MANAGEMENT ALGORITHM
The program, that controls power management process, im-
plemented on NXP MKW41Z SoC is presented as Algo-
rithm 1.
Algorithm 1 Power management algorithm
1: VOC getT E GOpenC ircuitV oltag e()
2: VBAT getB atteryV oltage()
3: if VOC > VOC MI N and VBAT < VBAT M AX then
4: Start PWM generator at low leakage mode
5: end if
6: if VBAT > VBAT M AX then
7: Stop PWM
8: end if
9: if VOC VF> VBAT and VOC < VBAT MAX then
10: Switch OFF Q1 to directly charge the battery
11: end if
12: if VOC VF> VBAT M AX then
13: Switch ON Q1 to save the battery
14: end if
15: if VOC > VOC RADIO and VBAT > VB AT MI N then
16: Normal WSN activity (turn on sensors and the radio)
17: end if
18: Sleep()
At the beginning, when the MCU wakes up, it measures
the TEG voltage and battery voltage (lines 1-2). If the TEG
voltage is greater than VOCM I N = 0.04 V, and the battery
voltage is smaller than VBAT MAX = 3.4V, then the MCU
starts switching the transistor Q1, initiating charging process
of the battery. If the battery voltage is greater than VBAT M AX ,
then transistor switching is suspended to prevent battery
overcharging (line 7). If the TEG voltage decreased by the
voltage drop VF= 0.375 Vover the Schottky diode D1
is greater than the battery voltage, then the transistor Q1 is
turned on, so the battery could be directly charged from the
TEG (line 10). If the TEG voltage minus the voltage drop
over D1 diode exceeds VBAT MAX , then the transistor Q1
is turned on to protect the battery (in practical applications
this case should not occur). Finally, if the TEG voltage is
greater than VOCRAD IO = 0.18 V, then power budget of a
EH-WSN node is balanced, so normal radio activity could be
resumed (line 16). Finally, the MCU sets up internal wake up
timer, and goes into an ultra-low power sleep mode (line 18).
All threshold voltages have been set according to the results
obtained from experiments described in the next section.
D. LOW POWER MODES
The proposed power management architecture cannot com-
pete in terms of quiescent current with dedicated power
management chips without a special stop modes offered by
some ultra-low power microcontrollers. In a very-low-power
stop mode (VLPS) current drawn by the MCU used for
tests was only 3.58 µA (buck mode operation for 3 V input
at 25°C) [17]. Other low power modes, such as very-low-
leakage stop mode (VLLSx), allow decreasing the current
down to 0.46 µA (buck mode operation for 3 V input at
25°C) [17]. These low power modes are characterized by
significant reduction of MCU functionalities, but fortunately
still allow some operations on GPIOs, including gating off
selected peripheral clocks. The clock that we used for driving
Q1 switching transistor of DC/DC boost converter has a fre-
quency of only 1 kHz. It was operational in all power modes
up to VLLS1. This clock source – called LPO (low-power
oscillator) – is a part of the internal power management
controller (PMC) of the selected SoC [18].
E. IMPLEMENTATION
Table 6 contains a list of parts required to build a low cost
thermoelectric energy harvesting wireless sensor node (TEH-
WSN). The cost of all electronic components – if purchased
in volume – is about 9 EUR. For prototyping, instead of
MKW21Z256VHT SoC (2.68 EUR @1k pcs.), we used
Rigado R41Z-TA (6.85 EUR @1k) module that contains
MKW41Z512VHT (3.25 EUR @1k), 32 MHz crystal os-
cillator, balun, and some other components. It increases the
cost of electronic components used in the prototype to around
12 EUR. Switching to a different RF SoC with more powerful
core (ARM Cortex M4) and lower power consumption in low
leakage modes like STM32WB55 (2.97 EUR @ 1k, 600 nA
in standby mode with RTC and 32 KB RAM retention [19])
should not greatly impact the total price.
TABLE 6. List of electronic parts of a low cost TEH-WSN node with a
software controlled power management module. Netto prices of electronic
components are given for quantities above one thousand pieces (except TEG,
for which price is given for a single piece). Prices according to Digikey, Farnell,
Botland and TME electronic parts distributors.
Part Model Price [EUR]
SoC with IEEE 802.15.4 RF MKW21Z256VHT 2.80
Thermoelectric generator TEC1-12706 2.85
Rechargeable Battery VL-2020/F2N 2.04
Transistor (N-MOSFET) AO3400 0.05
Schottky diode MBR0530 0.04
Inductor 330 µH, <1.5 0.09
Capacitors (min. 2x) SMD, Low ESR, 16 V 0.11
Other Crystal, balun, resistors 1.00
Total 8.98
VOLUME 8, 2019 7
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Markiewicz et al.: Software controlled low cost thermoelectric energy harvester for ultra-low power wireless sensor nodes
VI. EXPERIMENTS & RESULTS
To verify the concept of the energy harvesting architecture
described in the previous section we conducted a series of ex-
periments. At first we verified the correctness of open circuit
voltage estimation calculated by LT Spice for different Peltier
modules, as shown in Fig. 9. Tests confirmed results obtained
from simulations. To ensure stability of test conditions –
including proper temperature on both sides – tested modules
were put between a pair of other Peltier modules which were
connected to a pair of laboratory power supplies. Current
flowing through them generated a temperature difference
between their inner sides. Heat and cold from their outer sides
were dissipated by large heat sinks as shown in Fig. 10.
0 5 10 15 20 25
0
0.2
0.4
0.6
0.8
1
Temperature difference [C]
Open circuit voltage [V]
TEC1-12706
Laird NF28
Laird 4532
SuperCool
ZT-812F1
FIGURE 9. Measured open circuit voltages of several different Peltier
modules as a function of temperature difference T. For low values of T,
all tested modules behave similarly.
FIGURE 10. Each TEC1-12706 Peltier module is connected to a separate
Twintex TPM-3005 programmable power supply. Heat and cold from two
auxiliary Peltier modules are dissipated by large heat sinks. Between these
two modules there is a third one working as a thermoelectric generator.
Thermocouples placed between modules are connected to the Pico TC-08
thermocouple data logger. Open circuit voltage is measured by the PicoLog
1216 data logger.
The second set of experiments was about verification of
the idea of having net power gain with a DC/DC boost
converter driven by the MCU at low power mode. Initially the
switching transistor has been driven by an arbitrary function
generator to find the best configuration of inductor value
and duty cycle of pulse-width modulated signal. The same
experiments have been conducted using LTSpice simulations.
We used a testbed with a Peltier module as shown on Fig. 11,
and more convenient laboratory power supply with a resistor
connected in series that mimics internal resistance of a Peltier
module.
FIGURE 11. Testbed for measurements of efficiency of thermoelectric
generator connected to the power management module under constant load.
On the left hand side, a computer with a software that shows values of
thermocouples and voltage of a TEG; next to it Rigol MSO1105 oscilloscope
connected to the TEG, and displaying a TEG voltage in time; under it Fluke
8846A ammeter displaying current flowing into battery; next to them stack of
heat sinks and Peltier modules connected to two Twintex TPM-3005
programmable power supplies. Thermocouples are connected to the Pico
TC-08 data logger and TEG voltage is measured by the PicoLog 1216 Data
Logger.
The experiments ended up with the selection of AO3400
N-channel MOSFET (with VGS 1.45 V) as a switching
transistor, PWM signal with 50% duty cycle, frequency of
1 kHz, and an inductor of 330 µH and series resistance
below 1.5 . An inductor with significantly lower resistance
(440 ) offered much better performance, but because it was
almost seven times more expensive, we used the cheaper one.
Fig. 12 shows waveforms of charging circuit with the final
configuration. More power could be drawn from a TEG with
higher duty cycle, but generation of a simple, symmetrical,
low frequency signal was possible to obtain using the se-
lected SoC working in a low power mode.
The aim of the third set of experiments was to measure
the energy balance of the EH-WSN node. We connected a
potentiometer RLOAD in parallel to the MCU to mimic the
average energy consumption of the radio and node sensors.
Greater Tmeans greater open circuit voltage of the TEG.
By altering the resistance of RLOAD we were able to find the
maximum average power that could be consumed by the ra-
dio activity and sensors. The results are presented in Table 7.
At T= 1°C, the MCU can only charge the battery because
the energy supply is not sufficient to establish radio commu-
nication. At T= 1.28°C the radio can be activated with
about 4 minutes 11 seconds time interval between sending
messages. As Tincreases, the amount of harvested energy
allows to support not only more frequent communication
but also external sensors. For increasing input voltages, we
observed that the DC/DC converter maximum instantaneous
output voltage was getting close to the maximum absolute
value of the MCU. To minimize the impact of voltage spikes
8VOLUME 8, 2019
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Markiewicz et al.: Software controlled low cost thermoelectric energy harvester for ultra-low power wireless sensor nodes
FIGURE 12. Waveforms related to the MCU-controlled DC/DC boost
converter working in discontinuous mode. The first probe is connected to the
gate of the transistor Q1 (designators according to the schematics shown in
Fig. 8). The second probe is connected to the drain of Q1. After driving the
gate low, the voltage at the inductor L1 is increasing very rapidly and goes
through the Schottky diode D1 to the capacitor C2. The third probe is
connected to C2. This experiment was conducted without the battery B1, so
the energy that keeps the MCU working comes only from the capacitor C2
(2200 µF, 16 V).
to the MCU and the battery, a low-pass filter is recommended
between the capacitor C2 and the battery B1.
TABLE 7. Energy balance for different T. The load resistor RLOAD is
connected in parallel to the MCU to mimic the energy consumed by the radio
activity and some additional sensors. Each row in this table should be read as
following: for example at T=5°C, the open circuit voltage of the TEG is about
180 mV. For this input voltage, the circuit shown in Fig. 8 has a RMS voltage of
3.09 V. With resistive load RLOAD=327 kconnected in parallel to the MCU,
the average power dissipated by the RLOAD resistor is equal to 1mW (because
of current of 327 µA passing through it). This amount of power corresponds to
the situation when the IEEE 802.15.4 beacon-enabled radio is working with
BO6 (according to the Table 2 for BO=6 the device is transmitting every
second, and consumes in average 590 µW – less than 1012 µW dissipated by
the RLOAD resistor).
T VOC VRMS RLOAD Iavg Pavg BO
[°C] [V] [V] [k] [µA] [µW]
1.00 36 3.09 2104 1.47 4.54
1.28 46 3.09 702 4.40 13.60 14
1.50 54 3.10 250 12.42 38.52 10
2.00 72 3.11 88 35.54 110.54 8
2.50 90 3.05 45 68.00 207.41 8
3.00 108 3.10 29 106.68 330.70 7
3.50 126 3.09 20 152.97 472.68 7
4.00 144 3.09 17 184.48 570.04 7
4.50 162 3.09 12 259.45 801.69 6
5.00 180 3.09 9 327.68 1012.52 6
5.50 198 3.09 8 406.04 1254.68 5
6.00 216 3.09 6 484.33 1496.57 5
Finally, we tested the wireless sensor node as a fully func-
tional element of a wireless sensor network. We used WSN
node with a sample sensor – STMicroelectronics VL53L0X
Time-of-Flight (ToF) laser-ranging module, which can mea-
sure absolute distances up to 2 m, and Rigado R41Z module
with MKW41Z512 SoC as is shown in Fig. 13.
The experiments proved that the proposed solution works
as designed.
FIGURE 13. The wireless sensor node that could be powered either from a
thermoelectric generator (connector located at bottom left) working with
VL-2020 lithium-vanadium rechargeable battery, or from a non-rechargeable
lithium battery. The node contains of Rigado R41Z module with SoC ARM
Cortex-M0+ MCU with 512 KB Flash and 128 KB SRAM, and IEEE 802.15.4
RF (at the top), VL53L0X time of flight sensor (in the middle), and
MCU-controlled DC/DC boost converter (at the bottom).
VII. CONCLUSIONS AND FUTURE WORK
The architecture of the EH-WSN node described in this
paper is the answer to a challenge formulated in PRIME
project made within Electronic Components and Systems
for European Leadership JU Programme. The goal was to
propose a low cost wireless sensor node (< 9 EUR target),
communicating wirelessly using one of the IEEE transmis-
sion standards, fully autonomous with thermoelectric energy
harvester working with temperature difference 5°C. It was
intended to push the firmware optimization to the extreme
and to enforce a tight link between hardware and software.
In this sense the proposed architecture achieved the goal. We
hope, that it would be also useful in exploration of software
and hardware architectures of WSN, and will contribute to
spreading of autonomous Internet of Things devices.
In Table 8 we summarized advantages and disadvantages
of the proposed software controlled power management ar-
chitecture in relation to the architecture with a dedicated
power management chip.
TABLE 8. Comparison of two EH-WSN nodes architectures: with a dedicated
power management chip and with software controlled power management.
Parameter Dedicated chip Software supervision
Reliability Higher Lower
Complexity Lower Higher
Flexibility Lower Higher
Battery protection Higher Lower
Hardware cost Higher Lower
The most fundamental drawback of the proposed solution
is that the program executed by the MCU has to be very care-
fully written. A potential consequence of a software bug that
causes malfunction of the power management control loop
is permanent destruction of the battery. Another downside is
VOLUME 8, 2019 9
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Markiewicz et al.: Software controlled low cost thermoelectric energy harvester for ultra-low power wireless sensor nodes
that once the rechargeable battery is drained (which might
happen when a node is stored for too long without access
to thermal energy), then it is no longer possible to start the
device without charging the battery from an external source.
The main advantage of the proposed solutions is that it
helps in decreasing hardware cost of a WSN node. It also
takes the flexibility of power management to another level –
the node could dynamically decide about voltage thresholds
related to the sensor activity. In the context of industrial or
wearable applications, it might offer additional functionali-
ties. For example the node can temporarily decrease cut off
voltage, to ensure that all critical data will be transmitted,
when detecting an unusual condition. Similarly, it might tem-
porarily increase overcharging protection threshold (while
remaining in the safe operation area of the battery) to store
more energy, that will be spend on scheduled bulk radio
transmission like firmware update, or other energy intensive
activities like sensor calibration.
In the future we would like to investigate possibilities
of making power management process independent of user
application, possibly by integrating it with an embedded real
time operating system (RTOS) with a preemptive scheduler.
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MICHAŁ MARKIEWICZ received M.Sc. from
Jagiellonian University in Cracow, Poland in 2006
and Dr.-Ing. from Bremen University, Bremen,
Germany in 2015. He is an assistant professor
at Faculty of Mathematics and Computer Sci-
ence, Jagiellonian University. His scientific inter-
ests cover: sensor networks, traffic management
and power electronics.
PIOTR DZIURDZIA received M.Sc. and Ph.D.
degrees from AGH University of Science and
Technology (AGH UST) in Cracow, Poland, in
1995 and 2000, respectively. He is currently with
the Department of Electronics AGH UST. He
is working on energy harvesting applications in
wireless sensor networks and modelling of elec-
trothermal processes in thermoelectric modules.
TOMASZ KONIECZNY received M.Sc degree in
Electronics and Telecommunications at the Sile-
sian University of Technology in Gliwice, Poland
in 1994. Since 1995 he has been working as a
designer (chief designer or project manager) in
research and development departments.
10 VOLUME 8, 2019
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.2975424, IEEE Access
Markiewicz et al.: Software controlled low cost thermoelectric energy harvester for ultra-low power wireless sensor nodes
MAREK SKOMOROWSKI (MS’75) is a full pro-
fessor (since 2015) and Director of the Computer
Science and Computer Mathematics Institute of
the Jagiellonian University, Cracow, Poland. He
received Ph.D (in 1984) in computer science from
the AGH University of Science and Technology,
Cracow, Poland. He specializes in image recogni-
tion and computer simulation.
THOMAS SKOTNICKI (F’10) received the
B.Eng. and MSc. degrees in electronics from War-
saw Universityof Technology, Warsaw, Poland,
in 1979, the Ph.D. degree from the Institute of
Electron Technology, Warsaw, in 1985, and the
Habilitated for Directing Research degree from
the Institut National Polytech-nique de Grenoble,
Grenoble, France, in 1993. Full professor from
2007 (Poland).
PASCAL URARD (M’07) received the Engineer-
ing degree from ISEN, Lille, France, in 1991. In
2000, he joined ST Crolles Central R&D, Crolles,
France. In 2010, he initiated the first autonomous
IPv6 wireless network for sensors and actuators
(GreenNet), demonstrating bidirectional secured
IPv6 communications over the air powered by
energy harvesters. As an ST Fellow, he is currently
working on ultra-low-power and energy-efficient
solutions for IoT.
VOLUME 8, 2019 11
... These EH techniques are used by IoT devices to get energy and recharge their batteries from renewable sources. A green Internet-based energy harvested wireless sensor network (EH-WSN) [40] architecture allows the WSN node to be powered by a thermoelectric generator to reduce the cost of hardware and improve energy efficiency. Sleep scheduling and wake up protocol are used to prohibit direct communication between two sensor nodes. ...
... These schemes are used to reduce the cost based on estimated energy consumption. An EH-WSN [40] scheme uses EH to keep low cost and utilizes renewable resources for efficient power usage. Content courtesy of Springer Nature, terms of use apply. ...
... Mixed-integer nonlinear programming (MINLP) is applied to the minimum cost problem. • An EH-WSN [40] architecture allows the WSN node to be powered by a thermoelectric generator to reduce the cost of hardware and improve energy efficiency. EH is performed with temperature difference and uses a rechargeable battery which keeps the charge much longer. ...
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... ZigBee has a number of characteristics, some of which are shown in Table 2, including its maximum data rate and geographic coverage. Table 2. ZigBee in different frequency bands and their characteristics [8] In compared to the other two, the 2.4GHz PHY offers faster data transfer rates, and it is also available in more locations throughout the world, as seen in the [18][19][20][21], The network (NWK) layer and the application (APL) layer are two new levels introduced by ZigBee. Basic communication capabilities are provided by the physical radio layer, but the Media Access Control layer (MAC layer) provides a special service, that is, it allows single-hop communication between devices. ...
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