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Sensors and Materials, Vol. 34, No. 7 (2022) 2771–2790
MYU Tokyo
S & M 3005

Hybrid Charging and Storage Design in Sustainable
Solar-powered Wireless Sensor Node
Lu Wang,1,2† Dongsheng Li,1,2† Yintao Ma,1,2 Libo Zhao,1,2*
Yao Chen,1,2 Junlong Zhang,1,2 Zhuangde Jiang,1,2 and Ryutaro Maeda1,2**
1State Key Laboratory for Manufacturing Systems Engineering,International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies,
Collaborative Innovation Center of Suzhou Nano Science and Technology,
Xi’an Jiaotong University, Xi’an 710049, China
2School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China(Received November 26, 2021; accepted February 9, 2022)

Keywords: solar energy harvesting, wireless sensor node, power management, illumination monitoring,lithium battery charging

In view of the large scale and distributed characteristics of transformers, which lead toinconvenient manual inspection and state monitoring, and the limited energy of batteries
restricting the development of wireless sensor nodes (WSNs), WSNs powered by solar energy
are a promising approach. We propose a power management circuit for dual energy storage anddual-channel charging of a supercapacitor and a lithium battery with four modes to deal with thedifferent charging currents of photovoltaic power generation under strong and weak lightillumination, as well as the time mismatch between the energy harvesting power and WSN
consumption power. We designed a low-power WSN with temperature, humidity, vibration, andillumination sensors in a low-duty-cycle operation mode with an average current consumption of
4.96 mA. Our solar power management circuit with undervoltage lockout (UVLO), maximum
power point tracking (MPPT), quick charging, and a sustainable output characteristic is designed
by LTspice simulation and verified by experiment in alternating light outdoors. The experimentdemonstrates sustainable autonomous wireless sensing by dual charging and the feasibility of
our storage design for solar energy harvesting, indicating its potential application in gridtransformers.

1.

Introduction

Monitoring the condition of transformers in a grid is valuable for improving the reliability
and economy of the power supply system.(1) With the rise of the industrial Internet of Things(IoT), various applications based on the IoT have improved our lives, such as smartphones, smart
speakers, home safety systems, and wearable electronic products.(2) Also, time- and laborconsuming manual inspections and troubleshooting methods are gradually being replaced bymonitoring using wireless sensor nodes (WSNs).(3) At present, most industrial monitoring WSNs
*Corresponding author: e-mail: libozhao@mail.xjtu.edu.cn**Corresponding author: e-mail: darwin.sea@gmail.com†These authors contributed equally.

https://doi.org/10.18494/SAM3740

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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are powered by a precharged battery with limited electric energy, making it difficult to ensure
their long-term sustainable operation.(4) Owing to the widespread distribution of nodes, as manyas 300 WSNs may need to be arranged in a 25 m2 area,(5) making it difficult to ensure that allnodes have sufficient battery charge. Therefore, a new energy supply approach for WSNs isurgently required to overcome the energy limitation of precharged batteries.(6) An environmental
energy harvesting technology is one possible approach.(7) Energy harvesting has beensuccessfully applied to WSNs employed in transportation,(8) human activities,(9–11) outdoor solar
generation,(12) and wind generation,(13) and it is becoming increasingly important for supporting
the extended use of distributed IoT devices.(14,15)
Energy harvesting is also popular in grid applications.(16) Electromagnetic energy harvestingfrom power transmission lines can power WSNs to sustainably monitor the three-phase voltage,current, and other power quality parameters.(17) For typical IoT sensors with power consumption
above 10 mW,(18) piezoelectric generators and triboelectric nanogenerators require a stablevibration power input and a large-area energy harvesting device,(19–22) which is not conducive tobeing arranged on the shell of a transformer, while solar energy harvesting is reliable forpowering the WSN to monitor environmental quantities such as temperature, humidity,
vibration, and illumination, which are also important indicators that reflect the operating
conditions of a transformer.
Photovoltaic panels(23) are most commonly used in solar energy harvesting owing to theirhigh efficiency, low cost, cleanliness, and easy access.(24) In outdoor applications, a photovoltaic
panel can generate a high power of 15 mW/cm2 with an efficiency of up to 30% on a sunnyday.(25) However, little or no power is generated in the evening or at night, which together makeup two-thirds of the 24 h day, or on cloudy or rainy days with weak light. Therefore, solar energy
harvesting under weak lighting conditions should also be utilized to ensure a sustainable power
supply.(26) The small area of photovoltaic panels that can be used for a WSN also limits theoutput power.
An energy management strategy involving a hybrid supercapacitor and lithium battery has
been demonstrated to be effective in solar-powered systems, especially solar vehicles.(27) Many
dynamic algorithms to optimize the energy management strategy have been proposed.
Liu et al.(28) designed a sponge supercapacitor rule-based energy management strategy for
WSNs, which was optimized using a dynamic programming algorithm. However, complex
algorithms consume more power, making them less viable for small-scale energy harvesting.
To address these challenges in small-scale solar energy harvesting, many power management
approaches have been utilized such as a low operation voltage,(29) low-dropout (LDO)(30)
undervoltage lockout (UVLO),(31) and maximum power point tracking (MPPT).(32) Some
commercial chips are recommended for harvesting weak solar energy such as LTC3105 (Linear
Technology)(33) and BQ25504 (Texas Instruments).(34) Wu et al.(35) designed an IoT platform andautonomous WSN for environmental monitoring. Their power management circuit was based on
an LTC3105 chip with a rechargeable battery as the energy storage device. However, the lifetimeof autonomous operation in total darkness of 81 h shows that the service life of such a WSN still
depends on the capacity of the energy storage elements. Vračar et al.(36) designed a telemetry
node optimized for low illumination, using a 1.5 F supercapacitor as the standby energy storage

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element. However, owing to the low energy harvesting level, the DC–DC converter was omitted
and an electrolytic capacitor was used as a direct power supply. Although their node could
stabilize a cold start, it could not provide a stable power supply voltage or ensure that thesupercapacitor voltage rises to a sufficient level when there is light. Abella et al.(37) designed anenergy-saving WSN platform using a battery as the only energy storage element, but did notconsider the response time or the low conversion efficiency. The peak current output by thebattery was only 10 mA, which may further decrease upon matching the high periodic pulse of
WSN power consumption. These power management designs cannot effectively solve thebalance between the fast start-up and continuous operation of a WSN in an environment withalternating strong and weak light.
In practical applications, WSNs consume a large amount of energy during data acquisition,
processing, and communication,(38) and a direct solar power supply is difficult to stabilize toensure the sustainable operation of WSNs. In particular, an environment with alternating strong
and weak light(39) leads to an unstable solar power supply.(40) The power management circuit
requires energy storage elements with sufficient capacity to deal with a working environment
without an external energy input, such as at night, and the high energy storage capacity will
inevitably lead to a long energy storage cycle, thus delaying the rapid start-up and reducing the
responsiveness of WSNs.
To solve the above problems, we have designed a power management circuit with dual-stage
energy storage comprising a supercapacitor and a lithium battery, and we propose an innovative
design for dual-channel lithium battery charging with switching control according to the lighting
conditions, as reported in this paper. The power management circuit can operate in four modes:
direct power supply mode, supplementary discharge mode, low-power surplus charging mode,and high-power surplus charging mode. In the direct power supply mode, the photovoltaic
energy is stored in the supercapacitor to quickly provide energy for the WSN. In the
supplementary discharge mode, the illumination input is insufficient, and the WSN continues tobe powered by the energy stored in the lithium battery. In the surplus charging modes, while thelight input supplies power to the WSN through the supercapacitor, the surplus photovoltaic
energy is stored in the lithium battery. The microcontroller of the WSN judges the light level andcharges the lithium battery in two channels of low power and high power to supplement theconsumption of the battery during discharge. The design can ensure a sustainable power supply
and the effective utilization of photovoltaic energy.
We also designed a multifunctional WSN with low power consumption for temperature,humidity, vibration, and illumination monitoring in grid applications. By modeling andsimulation of a solar-powered WSN in a whole system using LTspice, we determined andoptimized the parameters and the logic used for power management. In an experiment on thesolar-powered system, dual energy storage element charging curves of the supercapacitor andlithium battery were tested to verify the quick charging ability. The power management logic
was also validated outdoors under different lighting conditions to verify the sustainability of thepower supply to the WSN. The upper computer front panel for WSN autonomous monitoring
was evaluated in a laboratory, and was shown to have potential use in grid transformer
applications.

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2.

Sensors and Materials, Vol. 34, No. 7 (2022)

Overall Architecture of Power Management

The solar energy harvested in the actual environment is usually relatively weak and greatly
affected by external factors, making it difficult to directly provide a stable and sufficient energy
supply for sensor nodes and their functional modules, especially for sensor nodes that mustswitch between sleep, RF communication, and other working modes with large power
consumption differences. The instability of environment energy harvesting and the changes in
WSN power consumption often lead to a time mismatch between energy harvesting power and
WSN consumption power.(41) In serious cases, packet loss or even power loss occurs during nodedata transmission. This mismatch between environmental energy intake and node energy
consumption in terms of the time and total amount imposes requirements on the design of power
management circuits.
A general power management circuit is composed of functional links such as for energy
harvesting, energy conversion, energy storage, and power supply.(42) An innovative photovoltaic
energy harvesting power management circuit must take into account the WSN energy demand in
an environment with alternately strong and weak lighting, and ensure that the energy storage
elements at all levels can be effectively charged after power consumption. In this paper, we
propose the power management circuit design of a supercapacitor and lithium battery dual
energy storage with dual-channel charging of the lithium battery, whose overall architecture isshown in Fig. 1.
As shown in Fig. 1, the solar energy harvesting unit is a photovoltaic panel. To meet therequirements of high pulse power consumption and long-term standby for WSNs, asupercapacitor with high power density and long cycle life is used as the first energy storage
unit, and a lithium battery with high energy density and stable voltage is used as the second
energy storage unit. The supercapacitor has ideal power density and energy density
characteristics. It can not only store enough energy to support the operation of the node for aconsiderable period of time but also provide high pulse power. Owing to its unlimited charge anddischarge times and high charge and discharge efficiency, it is very suitable for application toself-powered WSNs. However, the existence of leakage current limits its long-term energy

Fig. 1. (Color online) Overall architecture of dual-channel power management in WSN powered by solar energy
harvesting.

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storage capacity. It is therefore used in combination with rechargeable lithium batteries, whichhave the advantages of high energy density and low leakage, to provide high pulse power, a short
start-up time, and the long-term storage of standby energy.
To deal with different charging currents of photovoltaic power generation under strong andweak light illumination, an LTC3331 dual-channel power management chip and a TP4056
lithium battery dedicated fast charging chip are used as the lithium battery charging circuit. The
LTC3331 chip(43) is an energy harvesting power management chip that provides an input
interface for the original DC output of the photovoltaic panel and stores the electric energy in the
supercapacitor. It can also charge the battery with a low current charging capacity of 0–10 mA
when there is excess input solar energy for the supercapacitor. In contrast, the TP4056 lithium
battery charging chip(44) has a current charging capacity of 10–1000 mA for the battery with acharge and discharge protection function. The DC-DC converter of the dual power management
chips provides a stable power supply voltage for the WSN. In addition to data acquisition,
processing, and transmission, the microcontroller unit (MCU) of the WSN also monitors theambient lighting conditions and controls the dual energy storage strategy accordingly. The
working mode and workflow of the circuit are shown in Fig. 2.
As shown in Fig. 2, the input energy of the photovoltaic cell first passes through a full bridge
rectifier circuit and a UVLO circuit. The rectifier bridge can prevent a countercurrent. The
UVLO circuit disconnects the rear stage circuit and stores the energy in the supercapacitor.
When the supercapacitor voltage rises to the hysteresis window threshold Vrise, the rear stage
path is opened. Through the buck converter circuit, the WSN is powered by a small-capacity
electrolytic capacitor Cout, which stabilizes and filters the voltage. A flowchart of the dualchannel energy path control process is shown in Fig. 3 and the specific circuit design is discussed
in Sect. 4. Our proposed power management circuit has four modes as follows:(1) Direct power supply mode: the voltage Vcin of the supercapacitor of the primary energy
storage element is between the UVLO rising threshold Vrise and the falling threshold Vfall, thephotovoltaic energy stored by the supercapacitor directly supplies power to the WSN throughthe buck converter, and the lithium battery charge and discharge circuits are closed.
(2) Battery power supply mode: Vcin is less than Vfall due to a lack of light, such as on rainy days
and at night, the buck-boost converter of the LTC3331 power management chip is turned on,

Fig. 2.

(Color online) Workflow of dual-channel power management.

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Fig. 3.

Energy path control flowchart.

and the lithium battery used as the secondary energy storage element supplies power to the
WSN.
(3) Low-power surplus charging mode: the MCU of the WSN obtains the ambient illumination
through the illumination sensor while it is in operation. The illumination threshold of theTP4056 lithium battery charging circuit is 40000 lx, which is also the illumination value used
under the standard test conditions of photovoltaic panels. When the photovoltaic input causes
Vcin to exceed Vrise, if the ambient illumination E does not reach 40000 lx, the MCU outputs ahigh-level signal to the metal oxide semiconductor (MOS) gate through the input/output (IO)
port to turn it off. At this time, the LTC3331 chip slowly charges the lithium battery throughits charge pin.
(4) High-power surplus charging mode: the illuminance is higher than 40000 lx, indicating thatthe illumination is sufficient to quickly charge the lithium battery while maintaining theoperation of the WSN. The MCU outputs a low-level signal to turn on the MOS gate. Theenergy output by the solar panel flows to the lithium battery through the high-power charging
channel provided by the TP4056 chip dedicated to the fast charging of the lithium battery to
improve the charging speed of the lithium battery while the LTC3331 chip still charges thelithium battery.

3.

Environmental Monitoring WSN Design

3.1

Multifunctional WSN design

Transformer environmental monitoring includes temperature, humidity, and vibration
monitoring. Changes in the ambient temperature cause heating and cooling anomalies in theSensors and Materials, Vol. 34, No. 7 (2022)

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windings and affect oil temperature measurement. Humidity affects the cooling state of theequipment. Vibration characteristics reflect a variety of problems, including short-circuiting,winding breaks, overpressure, and overcurrent. Therefore, in addition to monitoring transformer
electrical parameters, environmental parameter monitoring is also indispensable.
To meet the need for transformer environmental monitoring, we have designed a low-power
multifunctional WSN using the very low power ATmega328P microcontroller as the main
control unit. The sensing unit of the WSN uses a DHT22 integrated temperature and humidity
sensor and an ADXL345 three-axis vibration sensor. A BH1750FVI light sensor is also added tomonitor light for dual-channel power management. The multifunctional WSN for monitoringtransformers operating under abnormal conditions can measure vibration in the range of ±16 g
and has a sensitivity of 3.9 mg/LSB, a temperature measurement range from −40 to 80 ℃ with aresolution of 0.1 ℃, and a humidity measurement range from 0 to 100 %RH with a resolution of
0.1% RH.
The wireless transceiver of the WSN uses an NRF24L01+ module based on enhanced
ShockBurst protocol, where the microcontroller converts the data read by the sensors into
single-precision floating points and hexadecimal according to the IEEE 754 floating-point
calculation standard. As the humidity, temperature, x-axis vibration, y-axis vibration, z-axis
vibration, and battery voltage are sequenced into the sending load register and packaged fortransmission, the data are received by the upper receiver and displayed on the upper computer inreal time. A working diagram of the WSN is shown in Fig. 4.
3.2

Low-power-consumption design of WSN

There is a mismatch between the unstable and low solar energy harvesting power and thehigh power consumption required for the WSN to collect and send data. To solve this problem, in
addition to configuring dual energy storage links on the hardware circuit, it is also important tocontrol the WSN operation duty via software. The WSN operating cycle can be divided into ahigh-power operating state and a very low power sleep state, giving it the power consumption
characteristics of periodic pulses. The energy storage element provides sufficient power for data

Fig. 4.

(Color online) Working diagram of WSN.

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transmission when the WSN enters the operating state. This overcomes the conflict between the
power characteristics of the energy harvester and the WSN, thus realizing a stable sustainableself-power supply and autonomous monitoring.
The ATmega328P processor supports six low-power modes to reduce consumption by
shutting down the function modules not in use, where the power-down mode consumes the least
power among the modes. In this design, four bits (0101) are written to the slave mode
control register to put the WSN into power-down mode. The WSN is then woken up after acertain period of time through a watchdog interruption. After entering the power-down mode,the external oscillator stops working and only the external interrupter and the watchdog keep
working. The consumption current can be as low as 0.36 mA. The sleep time depends on the
charging time of the energy storage element. To avoid the operating state of the WSN triggering
UVLO falling, the energy stored by the energy storage element during the sleep time shouldreach a sufficiently high level. In the power management circuit design, the WSN is set to workfor 10 s in a cycle and sleep for 1 min. The workflow of the WSN in the low-power mode isshown in Fig. 5.
3.3

WSN power consumption test

An LPA375 low-power analyzer is used to test and compare the power consumption curves ofthe WSN in the normal mode and low-power mode. The power consumption curves are analyzed
using Otii power consumption test software. As shown in Fig. 6(a), in the normal mode, the
WSN consumes an average current of 18.9 mA under a 4.5 V supply voltage. Such a high power

Fig. 5.

Low-power-mode workflow.

Sensors and Materials, Vol. 34, No. 7 (2022)(a)Fig. 6.

2779(b)(Color online) Comparison of WSN power consumption curves. (a) Normal mode and (b) low-power mode.

consumption of 85.05 mW is not conducive to energy harvesting, and the system cannot easily
store excess power. However, after low-duty-cycle programming, the average current consumed
is only 4.96 mA, reducing the overall power consumption by about 73.6%. Thus, the low-power
design of the WSN is effective, making a major contribution to realizing the self-power supply of
the system.

4.

Design of Solar Power Management Circuit

4.1

MPPT design of the photovoltaic cell

A polysilicon photovoltaic panel with an area of 105 × 66 mm2 is chosen as the solar energy
harvesting unit. The output characteristic curves of the photovoltaic cell are measured at 2:00
p.m. on a sunny day.
As shown in Fig. 7, the photovoltaic cell generates a high power between 4 and 6 V, making it
a suitable energy harvesting source for power management circuits. According to the principleof the open-circuit voltage method in photovoltaic MPPT,(45) the ratio of the maximum power
point voltage to the open-circuit voltage is usually between 0.71 and 0.78. Therefore, when
designing the circuit, keeping the output voltage of the photovoltaic cell within this range canrealize an output near the maximum power. Although this method has low accuracy and does notconsider the influence of temperature changes on the maximum power point, it does not need acomplex control circuit and can reduce the cost and the power consumed by the MCU tracking
calculation.
4.2

Power management design of dual energy storage element

The LTC3331 chip has a full bridge rectifier and a buck converter circuit with an input rangeof 3 to 19 V, which is sufficient for solar energy harvesting applications. The input side is2780

Sensors and Materials, Vol. 34, No. 7 (2022)(a)Fig. 7.

(b)(Color online) Output characteristic curves of the photovoltaic cell. (a) I–V curve and (b) P–V curve.

controlled by a UVLO that can be programmed by the pin logic input. By configuring the highand low levels of the pin, the UVLO can control the input voltage to near the MPPT voltage ofthe energy harvesting source. On the basis of the high power range of the photovoltaic cellmeasured in Fig. 7, a UVLO range of 4–5 V is chosen in the LTC3331 circuit design. Controlling
the UVLO consumes a static current of only 450 nA; thus, low-power energy harvesting sources
can efficiently store energy in the input supercapacitor, the primary energy storage element.
When the voltage of the primary energy storage element first reaches the UVLO risingthreshold of 5 V, the buck converter turns on with a very low quiescence current and outputs astable voltage of 3.3 V to power the WSN until the voltage falls to the UVLO falling threshold of
4 V.
If the voltage of the primary energy storage element rises above the UVLO rising thresholdof 5 V, a shunt starts charging the second energy storage element, which is a lithium battery,through a charging pin. If the voltage of the primary energy storage element drops below theUVLO falling threshold of 4 V, the buck converter is switched off, the boost converter is turned
on, and the battery powers the WSN. An internal PMOS switch is disconnected to protect thebattery from damage during over-release.
A supercapacitor with a capacity of 330 mF and a withstand voltage of 7.5 V is selected as theprimary energy storage element. A 602030 model lithium battery with a capacity of 300 mAh
and a nominal voltage of 3.7 V is selected as the second energy storage element in the power
management circuit design. According to the average current consumption in the low-powermode shown in Fig. 6, the WSN can operate continuously in a dark environment for
approximately 76 h when the battery is fully charged.
To verify the logic of the self-powered system, a system model including the LTC3331 power
management circuit model and WSN time-varying load model is established in LTspice, and thetime-varying curves of the input and output voltages are obtained through simulation. As shownin Fig. 8, the input voltage first rises quickly and provides a regulated output for the WSN after itreaches 5 V, then it fluctuates between 4 and 6 V as the power consumption of the WSN
alternates. When the input voltage is above 5 V, the circuit starts to charge the battery while
simultaneously supplying power to the WSN.

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(a)

(b)

Fig. 8. (Color online) Model and simulation of LTC3331 circuit. (a) LTspice model of LTC3331 circuit and (b)simulation curve of LTC3331 circuit.

4.3

Design of dual-channel battery charging circuit

The LTC3331 power management chip has a lithium battery charging function that charges alithium battery when the supercapacitor voltage is above the UVLO rising threshold. On thebasis of the chip data sheet, its charging current is determined as
I charge =4.8V − VLBD.
Rcharge + 60

(1)

Icharge: charging current (A)VLBD: low-battery disconnect (V)Rcharge: charging pin current limit resistance (Ω)
In the chip design, VLBD is 2.7 V, and to maximize the battery charging current, we short theresistor Rcharge to reach the theoretical maximum charging current of 35 mA. However, owing toa shunt current limitation, the measured lithium battery charging current is only 1.7 mA.
Assuming that solar energy harvesting is available for 13 h from 6:00 a.m. to 7:00 p.m. on asunny day, the shunt in the LTC3331 chip can charge the lithium battery during this time. In theremaining 11 h without solar energy harvesting, the WSN is powered by the lithium battery.
Assuming that the lithium battery is linearly charged and discharged, we roughly estimate thecharging energy input to the battery and its discharging energy in 24 h to be about
1.7 mA × 13 h =22.1 mAh and 4.96 mA × 11 h = 54.56 mAh , respectively. Because thedischarging energy is greater than the charging energy, the energy in the lithium battery will
gradually be consumed, and the WSN is unsustainable. Therefore, another lithium battery
charging circuit must be designed to provide a higher charging current to the lithium battery
when the light is sufficient. When the light is insufficient, the lithium battery still supplies power
to the WSN through the buck-boost converter of the LTC3331 chip.
The improved dual-channel lithium battery circuit uses a TP4056 integrated chip to build ahigh-speed charging path for the lithium batteries. The classic TP4056 low-cost lithium battery

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Fig. 9.

(Color online) Dual-channel lithium battery charging circuit.

charging chip provides a stable charging voltage of 4.2 V and a charging current of up to 1 A. It
also integrates a DW01 battery protection chip and a 8205A dual MOS power tube on the
charging side to protect the lithium battery from overcharging and overdischarging.
The charging path of the TP4056 chip is controlled by the MCU, and an FQP7P06 P-channelenhanced MOS tube is used as a circuit switch in this design. The ambient lighting is detected bya BH1750FVI light sensor for switching control. The connection diagram of the dual-channel
lithium battery charging circuit is shown in Fig. 9.
To determine a light threshold as the condition for charging path switching control, theillumination from 6:00 a.m. to 8:00 p.m. and the voltage of the solar cell during this time were
measured on a June day at a temperature between 21 and 35 ℃. As shown in Fig. 10, thephotovoltaic cell maintains a voltage of higher than 4 V between 6:00 a.m. and 7:00 p.m.,providing continuous energy to the WSN, and the lithium battery can power the WSN during theremaining 11 h. If a condition of illumination higher than 40000 lx for the opening of theTP4056 charging path is set, then the path will be continuously open for about 6 h a day tocharge the lithium battery. From the battery consumption of 54.56 mAh calculated above, theTP4056 charging current must be at least 9 mA to replenish the lithium battery and make the
WSN sustainable.

5.

Test of Solar-powered System

5.1

Test of energy storage element charging(1) Supercapacitor charging test
The charging speed of the supercapacitor mainly depends on the photovoltaic cell’s output
power and the efficiency of the power management circuit. Figure 11 shows the measured
supercapacitor voltage charging curve. The supercapacitor voltage reaches the UVLO risingthreshold of 5 V in 30 s when the illumination is sufficient, which indicates that the WSN has a
st a r t-up. T he ave r age cha rg i ng powe r before t he volt age rea ches 5 V is
0.5 × 0.33 F × 5 V × 5 V / 13.7 s =301.09 mW .

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Fig. 10. (Color online) Sunlight and photovoltaic voltage during the day.

Fig. 11. (Color online) Supercapacitor charging curve.

(2) Lithium battery charging test
The power management circuit has different charging currents in the low-power surplus
charging mode and high-power surplus charging mode. We used the resistor divider method tomeasure the charging currents. A small resistance of 1 Ω was added to the lithium battery
charging circuit in series, and the voltage across the resistance was read by a multimeter so thatthe charging currents could be calculated using Ohm’s law. The charging currents of the lowpower surplus charging mode and high-power surplus charging mode are shown in Table 1.
In the high-power surplus charging mode, the lowest charging current is 9 mA and the
maximum current is 20.2 mA, thus providing sufficient energy for the lithium battery. The
power curves of the lithium battery over 24 h when it is charged with and without dual channels
are shown in Fig. 12. If only the LTC3331 chip is used for lithium battery charging, the total
power of the battery drops after 21 h and eventually runs out. However, when the TP4056 chip is
added to form a two-channel charge, the battery can make up for its losses and even store surplus
power generated during the day.

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Table 1
Lithium battery charging currents.
Low-power surplus
charging mode
Charging current (mA)
1.7

High-power surplus
charging mode
9–20.2

Fig. 12. (Color online) Lithium battery power curves over 24 h.

5.2

Verification of power management logic

To verify the logic of the dual-channel power management design, the supercapacitor voltage
curves under different lighting conditions and power supply modes are measured and observed
using an oscilloscope. The supercapacitor voltage curves and output voltage curves under
different lighting conditions are shown in Fig. 13.
The photovoltaic output power is very high on sunny days. The Vout pin of the LTC3331 chip
immediately outputs a voltage of 4.5 V when the capacitive voltage rises to 5 V, and the WSN is
powered on and remains stable. When the WSN is operating, the charging curve slope is low but
continuously rises, and the voltage increases quickly and can reach up to 6 V when the WSN
sleeps.
On cloudy days, the measured capacitor charging speed is lower than that on sunny days, andthe capacitive voltage is significantly reduced when the WSN is powered on. Then, within 1 minof the WSN entering sleep mode, the capacitive voltage slowly rises to about 5.36 V, then quickly
drops to about 4.72 V when the WSN is woken. The capacitive voltage then stably remains
between these two values, i.e., within the optimal power generation range of the photovoltaic
cell, indicating full use of the photovoltaic power harvesting.
In the lightless environment, the measured supercapacitor voltage gradually drops. When it
reaches 4 V, the voltage of the EH_ON pin of the LTC3331 chip jumps to a low level, indicatingthat the energy harvesting power supply has switched to battery power, while the output voltage
remains stable at about 4.5 V and the WSN continues to operate stably. The above tests verified
the validity of the dual energy storage element design.

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(a)

(b)

(c)
Fig. 13. (Color online) Voltage curves under different lighting conditions. (a) Sufficient light on sunny days, highpower surplus charging mode. (b) Weak light on cloudy days, low-power surplus charging mode. (c) No light, WSN
powered by lithium battery, supplementary discharge mode.

5.3

Solar-powered wireless sensing system

To visualize and monitor WSN acquisition data in real time, an upper computer front panel
was written in Labview2018 as shown in Fig. 14. An NRF24L01+ receiver module is connectedto the computer via a serial port, which receives data from the WSN wirelessly and sends thedata to the computer for processing and display via serial communication. The front panel canmonitor the temperature, humidity, and vibration data and the battery level received by the WSN
in real time, and can store the historical data as CSV files. In addition, the upper computer candirectly collect the three-axis vibration data of the WSN for Fourier spectrum analysis. The
upper computer can also detect anomalies in the temperature, humidity, or vibration signal of themonitored transformer and provide an alarm.
When the transformer is operating normally, the vibration base frequency is around 100 Hz.
In a laboratory experiment, whose setup is shown in Fig. 15, a vibration signal of 100 Hz is
generated with a JDS6600 signal generator, and the ADXL345 sensor of the WSN is pasted onto
an E-JZK-10 vibration exciter. The vibration amplitude is adjusted to 0.5, 1, or 1.5 g via an SAPA010 power amplifier. The exciter generates vibration along the z-axis of the sensor, and the
WSN wirelessly transmits the vibration data to the receiver. The frequency domain informationof the vibration displayed by the upper computer is shown in Fig. 16. The upper computer front

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Fig. 14. (Color online) Upper computer front panel.

Fig. 15. (Color online) Experimental setup.

(a)

(b)

(c)
Fig. 16. (Color online) Demonstration of monitoring by upper computer showing spectra for 100 Hz vibration withamplitudes of (a) 0.5 g, (b) 1 g, and (c) 1.5 g.

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Fig. 17. (Color online) Solar-powered WSN in grid transformer application.

panel can accurately identify the vibration frequency. When the amplitude exceeds 1 g, thevibration alarm indicator turns red to sound an alarm. The deviation in the amplitude
measurement may be due to the sub-optimal installation location of the sensor or the low
thickness of the sensor PCB plate. Similarly, the upper computer sounds an alarm when thetemperature exceeds 40 ℃ or the humidity exceeds 60% RH.
The above experiment demonstrates sustainable and autonomous wireless sensing by thedesigned dual-channel charging and dual storage elements during solar energy harvesting. The
solar-powered WSN designed in this study is promising for use in grid transformer applications
as shown in Fig. 17.

6.

Conclusions

Multifunctional WSNs for sensing and state monitoring play an important role in thedevelopment of intelligent manufacturing and the industrial IoT, and energy harvesting is a key
approach to address the energy limitations of WSNs and ensure sustainable autonomous
applications. On the basis of the wireless environmental monitoring application in gridtransformers, we designed a solar-powered WSN and its power management circuits. The power
management circuits of the dual energy storage elements and dual-channel lithium battery
responded to changes in the light characteristics. This design balances the requirements of a fast
start and an energy reserve for the WSN, provides trickle charging and relatively fast charging,and guarantees that the energy consumed by lithium batteries can be replenished to achieve
long-term sustainable operation. To achieve low power consumption, the WSN operates for 10 sthen sleeps for 1 min in one cycle, i.e., the duty cycle is 14.3%, and the average current
consumption is 4.96 mA. The solar-powered WSN can quickly start in less than 30 s from anempty energy state, and the lithium battery charging current has a maximum of 20.2 mA and aminimum of 1.7 mA. Our proposed design achieves stable dual-channel lithium battery charging
and can provide sustainable power supply to a WSN.

Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (Grant
Nos. 51890884), Jiangsu Province Foreign Expert Program (Grant No. BX2020032), the2788

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Recruitment Program of Global Experts (Grant No. WQ2017610445), and the 111 Program(Grant No. B12016). Data supporting the findings of this study are available from thecorresponding author upon reasonable request.

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About the Authors
Lu Wang received his B.S. degree in mechanical engineering and automationin 2013 and his Ph.D. degree in 2021, both from Xi’an Jiaotong University. The
Ph.D. degree included one year as a visiting student at the National Universityof Singapore. His research interests include energy harvesting, MEMS sensors
and actuators, and structural vibration.

Dongsheng Li received his B.S. degree in instrument science and technology
from Xi’an Jiaotong University in 2021. His research interests are solar energy
harvesters and self-powered sensors.

Yintao Ma was born in Henan, China, in 1996. He received his B.S. degree inmeasurement and control technology and instruments from Sichuan University
in 2018. In the same year, he joined Xi’an Jiaotong university as a graduate
student. His research interests include atomic magnetometer, and MEMS/
NEMS technology.

Libo Zhao was born in Yantai, China, in 1978. He received his M.S. and Ph.D.
degrees in instrument science and technology in 2003 and 2007, respectively,
both from Xi’an Jiaotong University, Xi’an, China. He is currently working as
an associate professor in Xi’an Jiaotong University. His research interests
include micro- and nanofabrication technology, MEMS sensor technology, andprecision machining technology.

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Yao Chen is an assistant professor of Xi’an Jiaotong University and is fromthe Department of Mechanical Engineering. His research areas include chipscale atomic devices such as atomic magnetometers and atomic spin
gyroscopes. He received his B.S. degree in the field of applied physics in 2009
from Beihang University. He received his PhD degree in the field of precision
instrumentation and mechanical engineering in 2017 also from Beihang
University. From 2017 to 2019, he was a postdoctoral researcher in the Physics
Department of Harvard University. He is interested in quantum precision
measurement based on trapped ions.

Junlong Zhang is currently a staff member of the International Joint
Laboratory for Micro/Nano Manufacturing and Measurement Technologies
and the School of Mechanical Engineering, Xi’an Jiaotong University (XJTU),
China. He received his MSc degree from Okayama University, Japan.

Zhuangde Jiang, Ph.D, is a professor of Xi’an Jiaotong University and an
academician of the Chinese Academy of Engineering. He is currently the
director of the Institute of Precision Engineering (IPE), the vice director of theChinese Society of Micro-Nano Technology (CSMNT), and the executive
director of the Chinese Mechanical Engineering Society (CMES). His researchareas include micro- and nanofabrication technology, MEMS and sensor
technology, ultraprecision machining technology and equipment, andphotoelectric testing technology and instrumentation.

Ryutaro Maeda joined the Mechanical Engineering Laboratory, Agency ofAdvanced Industrial Science and Technology (AIST) in Tsukuba in 1980 after
earning his M.S. degree from the Graduate School of Tokyo University. He is
the founder of UMEMSME (Research Centre of Ubiquitous MEMS andMicro Engineering) in AIST, was director there from 2010 to 2014, and served
as the leader of several national projects, including the Green Sensor Network
project and the JST CREST program. He has been a professor at Xi’an Jiaotong
University since 2018. His current research interests include the application of
wireless networked sensing for energy and safety management. He has
published more than 300 papers in MEMS and related fields.