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In this paper, an autonomous sensor system, with low-power electronics for radio-frequency (RF) communication, incorporating a thermoelectric energy-harvesting module for unattended operation is presented. A target application is proposed for temperature measurement of walled-in pipes. When the autonomous sensor is placed on the heat source, a ther...
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... R in is the internal electrical resistance of the TEG. The power density of the adopted TEG module is 4 μ W/mm 2 for a temperature difference Δ T of 5 ◦ C in matched-load conditions. The dc–dc converter TPS61200 is the used voltage boost (step-up) converter that switches on when the input voltage goes beyond 0.5 V, providing a regulated output voltage that is set to 2.1 V. The external readout unit consists of a read/write base station U2270B, which is able to supply power to the transponder driving the coil antenna and to demodulate the digital signal. The readout unit is supplied by a 5 V voltage supply. The operating voltage of the microcontroller is 5 V, and the bus frequency is 7.38 MHz. A timer unit is used to decode the demodulated signal, and the data collected are transferred to a personal computer using a serial communication interface. III. C HARACTERIZATION OF A UTONOMOUS S ENSOR S YSTEM The output voltage generated by the TEG as a function of the temperature difference applied was measured. The TEG characterization was performed by heating one side of the module by means of a heater, which was externally powered, while a heat sink was applied on the other side to maintain it at a lower temperature by using the setup described in [14]. The temperature difference Δ T between the two faces of the TEG was measured using two NTC thermistors, which were attached with thermoconductive grease. The output power generated by the TEG, which was measured under different load conditions and various applied temperature gradients, is shown in Fig. 2. As expected from (2), the output power has a maximum when R L matches R in , which, in our case, is about 8 Ω . The output voltage of the TEG was then measured versus the applied temperature difference Δ T in the range from 0 ◦ C to 13 ◦ C in both open-circuit and loaded conditions, which was reported as V G and V L , respectively. In the loaded condition, the dc–dc converter, temperature sensor, and microcontroller are powered by the TEG. The obtained experimental results are shown in Fig. 3, which reports both V G and V L as a function of the temperature difference Δ T . As expected from (1), the open-circuit output voltage V G and the temperature difference Δ T are directly proportional. When the temperature difference is lower than about 3 ◦ C, the TEG voltage is less than 0.3 V, and the dc–dc converter is off. In this case, there is no loading effect on the TEG, and the curves of V G and V L overlap. When Δ T goes beyond 4 ◦ C (“Intermediate phase”), the dc–dc converter attempts to switch on, sinking a comparatively high current that decreases the voltage V L . In this condition, the output voltage V CC of the dc–dc converter is lower than the set value of 2.1 V. When Δ T reaches a value of about 8.5 ◦ C and above, the dc–dc converter switches on completely, and its output voltage steadily goes to 2.1 V. The autonomous sensor has two different operational modes: measurement and data-saving mode, which is powered by the TEG module, and RF communication mode, which is powered by the magnetic field generated by the external readout unit. In Fig. 4, the main signals, which are the output voltage of the TEG module ( V L ) , the output voltage of the dc–dc converter ( V CC ) , and the microcontroller clock, are shown during the system wake-up due to the increase of the power-harvesting supply. In the initial time interval (“Start-up boost converter”), when V L exceeds 300 mV, the dc–dc converter begins to start up, and when V L exceeds 0.5 V, the dc–dc converter is completely on, generating a stable output voltage of 2.1 V. From this moment, the sensor and the microcontroller are powered, and the system works continuously as long as the temperature gradient is maintained. During the “Start-up μ C” time interval, when the dc–dc converter output voltage V CC reaches 1.8 V, the microcontroller begins to switch on. In the performed tests, the autonomous sensor system, when powered by the TEG, can save the measurement data into a nonvolatile memory every 2 s. In this situation, the microcontroller and the temperature sensor require a power supply of about 0.9 mW, with a current consumption of about 0.4 mA and a voltage level of about 2.1 V. The measured data can then be collected at subsequent times using the external readout unit by RF communication. In the reading operation, the presence of a heat source is not strictly necessary, since the power supply is provided via electromagnetic field through the coil antenna of the transponder interface. The wake-up signals when the RF field supplies the sensor are shown in Fig. 5. The signals are the differential voltage of the reader antenna, the voltage of the transponder antenna, the supply voltage of the microcontroller, and the microcontroller clock. In the time interval labeled as “Start-up transponder,” the reader is turned on, and the electromagnetic field begins to power the transponder. In the second time interval labeled as “Start-up μ C,” the transponder is turned on, while the microcontroller begins to be powered. The total wake-up time is about 100 ms. The data communication starts after 20 ms from microcontroller clock wake-up, as shown from the transponder antenna signal. A transmission of the measured temperature data that were previously stored in the flash memory between the autonomous sensor and the readout unit is shown in Fig. 6. The low- power microcontroller sends the digital signal to the transponder (“Digital signal transponder”) for the transmission. In the lower graph of the figure, the sensor antenna signal is reported, and the OOK modulation of the digital signal is visible. The base station receives the back-reflected signal (“Output Signal Base Station”) and extracts the Manchester- encoded information (“Digital Signal Base Station”). In this experimental configuration, by turning off the unnecessary modules of the microcontroller and keeping a distance between the autonomous sensor and the readout unit of about 15 mm, the current consumption during RF communication is about 190 μ A at 2.58 V, corresponding to a power level of 0.5 mW. The power required by the autonomous system in the two operational states are reported in Table I, while Table II shows the values of the current for each device of the autonomous sensor in the two operational states. The measurements are obtained using two multimeters (Fluke 8840A). Different tests to evaluate the maximum operating distance between the autonomous sensor and the readout unit were performed, also varying the dimensions of the sensor antenna. Three antennas, whose characteristics are reported in Table III, have been tested. The inductances have been measured with an impedance analyzer (HP4194A). The transponder voltage supply has been measured with a multimeter (Fluke 8840A) when the autonomous sensor system is transferring data, and the results are shown in Fig. 7. The distance z between the antenna of the readout unit and the autonomous sensor is changed from 50 to 100 mm using a micrometer screw. The correct working operation is achieved for a supply voltage that is higher than 1.8 V. The antenna A1 presents the best readout distance, and it has the largest diameter. IV. E XPERIMENTAL S YSTEM An experimental setup has been arranged to evaluate the capability of the autonomous sensor system to measure the temperature along a walled-in pipe of a heating plant. The experimental system is shown in Fig. 8. It consists of a pipe, throughout which hot air is fluxed, two autonomous sensors, and an external readout unit separated by a dummy wall. The heater system represents a simplified model of a generic building heating plant. A round burner with a diameter of 65 mm has been used as heater, which, when supplied with GPL gas, is able to produce a heating power of about 1 kW. When the gas burner is turned on, it heats the air above it, which generates a hot air flux along the pipe from bottom to top. The hot air is conveyed into a metallic pipe, heating its surfaces. The pipe is made of enameled iron and has a thickness of 1 mm, a square cross section with sides of 100 mm, and a length of 1 m. The external temperature distribution along the pipe was measured using five NTC thermistors placed every 20 cm from the lower end of the pipe, while for the internal hot air temperature, a thermocouple was positioned inside at the center of the pipe, as shown in Fig. 9. The NTC sensors were fixed with thermoconductive grease and sealed with silicon glue. The burner gas flame was set at three different intensity levels. Typical steady-state values of temperatures are shown in Fig. 10, which were measured about 500 s after burner ignition. Two autonomous sensors were placed on the external side of the pipe at 20 and 60 cm from the lower end of the pipe, near NTC 2 and NTC 4, respectively. One side (“hot side”) of the TEG of the autonomous sensor was placed on the external side of the pipe, while on the other (“cold side”), a heat sink was mounted to permit heat exchange with the environment. This allows us to maintain the cold side temperature as low as possible and, as a consequence, a temperature gradient across the TEGs as high as possible. The temperature sensor LM94022 was placed near the reference NTC thermistor. Thermoconduc- tive grease was used to improve thermal contact between the pipe wall, TEG, heat sink, and sensor. The output voltages of the dc–dc converters placed near NTC2 and NTC4, which are labeled Vcc1 and Vcc2, respectively, and the time trend of temperatures were measured with the burner ...
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Citations
... Energy storage devices can be used effectively to power technological devices by converting waste heat into electrical energy [11,12]. Vahid et al. [13] in their research work demonstrated the measurement and evaluation of the energy produced by a thermoelectric generator of heat flux generated in the brake disc and other parts of vehicles. ...
... resistance R in is important in generating high current in a thermoelectric generator. The simulation result is determined by equation (11). And measured model electrical current I = 0.282A temperature difference ∆T 40 °C, TEG1, I = 0.193A, TEG2, I = 0.253A. ...
... Different systems powered by multi sources energy harvesting have been discussed in the literature. In 2009 Dalola et al. proposed an autonomous sensor system with power harvesting to measure the temperature along a walled-in pipe of a heating plant [180]. The system uses a TEG module (40 × 40 × 3.6 mm 3 with 254 thermocouples) to scavenge power from the temperature gradient along the pipe and generates a 2.1 V DC voltage to supply a low-power microcontroller (9S08QE128, Freescale Semiconductor, Austin, TX, USA) and a temperature sensor (LM94022, Texas Instruments, Dallas, TX, USA). ...
... Georgiadis et al. in 2011 presented an energy harvesting system that scavenges power from solar and RF sources [183]. The transducer is built on a flexible polyester substrate that combines a proposed an autonomous sensor system with power harvesting to measure the temperature along a walled-in pipe of a heating plant [180]. The system uses a TEG module (40 × 40 × 3.6 mm 3 with 254 thermocouples) to scavenge power from the temperature gradient along the pipe and generates a 2.1 V DC voltage to supply a low-power microcontroller (9S08QE128, Freescale Semiconductor, Austin, TX, USA) and a temperature sensor (LM94022, Texas Instruments, Dallas, TX, USA). ...
Wireless sensor network nodes and mobile devices are normally powered by batteries that, when depleted, must be recharged or replaced. This poses important problems, in particular for sensor nodes that are placed in inaccessible areas or biomedical sensors implanted in the human body where the battery replacement is very impractical. Moreover, the depleted battery must be properly disposed of in accordance with national and international regulations to prevent environmental pollution. A very interesting alternative to power mobile devices is energy harvesting where energy sources naturally present in the environment (such as sunlight, thermal gradients and vibrations) are scavenged to provide the power supply for sensor nodes and mobile systems. Since the presence of these energy sources is discontinuous in nature, electronic systems powered by energy harvesting must include a power management system and a storage device to store the scavenged energy. In this paper, the main strategies to design a wireless mobile sensor system powered by energy harvesting are reviewed and different sensor systems powered by such energy sources are presented.
... La línea de bisagra define el inicio la deformación plástica. 43 Caso contrario se observó para los perfiles con base hexagonal (H-HE) y circular (H-CI) donde el valor P max no se registró al inició de la gráfica sino durante el proceso de deformación plástica, aproximadamente a 18 mm de desplazamiento. Respecto al proceso de compresión en todos los casos se presenta una estabilidad de la fuerza de aplastamiento (P m ). ...
... Sistemas de calentamiento solar [39,40]. Sistemas de telemetría [41][42][43]. Calor de desecho de plantas de energía y de procesos industriales [2,44,45]. Plataformas micro auto energizadas sin cables [46,47] . ...
En este trabajo se presenta el diseño y optimización de implantes craneofaciales
personalizados utilizando tecnologías de diseño y manufactura modernas. El objetivo es
introducir una metodología que permita el desarrollo de implantes craneofaciales
personalizados, así como analizar los beneficios que tiene la optimización de un implante
de este tipo.
... As the charges of the batteries depleted over time due to power consumption, the energy harvesting system is needed to recharge these batteries to sustain the functionality of the devices. Numerous techniques have been proposed to recharge the batteries such as photovoltaic cells, wind energy, kinetic energy, radio frequency, magnetic field and thermoelectric [3][4][5][6][7][8][9][10][11][12][13]. Although these renewable power sources have been tested to be able to activate the device, however, they normally face a problem in its scalability, cost of installation and applicability in a wide range of topography. ...
It is well proven that electrical energy can be harvested from the living plants which can be used as a potential renewable energy source for powering wireless devices in remote areas where replacing or recharging the battery is a difficult task. Therefore, harvesting electrical energy from living plants in remote areas such as in farms or forest areas can be an ideal source of energy as these areas are rich with living plants. The present paper proposes a design of a power management circuit that can harness, store and manage the electrical energy which is harvested from the leaves of Aloe Barbadensis Miller (Aloe Vera) plants to trigger a transmitter load to power a remote sensor. The power management circuit consists of two sections namely; an energy storage system that acts as an energy storage reservoir to store the energy harvested from the plants as well as a voltage regulation system which is used to boost and manage the energy in accordance to a load operation. The experimental results show that the electrical energy harvested from the Aloe Vera under a specific setup condition can produce an output of 3.49 V and 1.1 mA. The harvested energy is being channeled to the power management circuit which can boost the voltage to 10.9 V under no load condition. The harvested energy from the plants boosted by the power management circuit can turn ON the transmitter automatically to activate a temperature and humidity sensor to measure the environmental stimuli periodically with a ton of 1.22 seconds and toff of 0.46 seconds. This proves that this new source of energy combined with a power management circuit can be employed for powering the wireless sensor network for application in the Internet of Things (IoT).
... As the charges of the batteries depleted over time due to power consumption, the energy harvesting system is needed to recharge these batteries to sustain the functionality of the devices. Numerous techniques have been proposed to recharge the batteries such as photovoltaic cells, wind energy, kinetic energy, radio frequency, magnetic field and thermoelectric [3][4][5][6][7][8][9][10][11][12][13]. Although these renewable power sources have been tested to be able to activate the device, however, they normally face a problem in its scalability, cost of installation and applicability in a wide range of topography. ...
It is well proven that electrical energy can be harvested from the living plants which can be used as a potential renewable energy source for powering wireless devices in remote areas where replacing or recharging the battery is a difficult task. Therefore, harvesting electrical energy from living plants in remote areas such as in farms or forest areas can be an ideal source of energy as these areas are rich with living plants. The present paper proposes a design of a power management circuit that can harness, store and manage the electrical energy which is harvested from the leaves of Aloe Barbadensis Miller (Aloe Vera) plants to trigger a transmitter load to power a remote sensor. The power management circuit consists of two sections namely; an energy storage system that acts as an energy storage reservoir to store the energy harvested from the plants as well as a voltage regulation system which is used to boost and manage the energy in accordance to a load operation. The experimental results show that the electrical energy harvested from the Aloe Vera under a specific setup condition can produce an output of 3.49 V and 1.1 mA. The harvested energy is being chan-neled to the power management circuit which can boost the voltage to 10.9 V under no load condition. The harvested energy from the plants boosted by the power management circuit can turn ON the transmitter automatically to activate a temperature and humidity sensor to measure the environmental stimuli periodically with a t on of 1.22 seconds and t off of 0.46 seconds. This proves that this new source of energy combined with a power management circuit can be employed for powering the wireless sensor network for application in the Internet of Things (IoT).
... Efficient energy harvesting methods and techniques are becoming mature. Current research involves a variety of environmental energy sources, such as thermal energy [6][7][8][9], photovoltaic (PV) [10][11][12], wind energy, vibration energy [13], and so forth. However, most of the environmental energy in forests is extremely weak, making it difficult to independently supply WSNs. ...
... The collection and utilization of thermal energy technology has developed rapidly and has been studied and applied in the field of WSNs [6][7][8][9]. Mateu and Moll [6] designed an energy harvesting circuit to power sensors by using the electrical energy generated by the temperature difference between the human body and the surrounding environment. The experiments verified the feasibility of of collecting microthermal energy. ...
... Under the action of thermal excitation, holes and electrons in the pn junction diffused toward the low temperature port to form an electromotive force, which was the thermoelectric potential [9]. Figure 1 shows the equivalent circuit of the TEG. The characteristic equation for this TEG module was given by [7,8], and the meaning of the formula elements is shown in Appendix A: ...
Wireless sensor nodes (WSNs) are widely used in the field of environmental detection; however, they face serious power supply problems caused by the complexity of the environmental layout. In this study, a new ultra-low-power hybrid energy harvesting (HEH) system for two types of microenergy collection (photovoltaic (PV) and soil-temperature-difference thermoelectric (TE)) was designed to provide stable power to WSNs. The power supply capabilities of two microenergy sources were assessed by analyzing the electrical characteristics and performing continuous energy data collection. The HEH system consisted of two separated power converters and an electronic multiplexer circuit to avoid impedance mismatch and improve efficiency. The feasibility of the self-powered HEH system was verified by consumption analysis of the HEH system, the WSNs, and the data analysis of the collected microenergy. Taking the summation of PV and TEG input power of 1.26 mW (PPV:PTEG was about 3:1) as an example, the power loss of the HEH system accounted for 33.8% of the total input power. Furthermore, the ratio decreased as the value of the input power increased.
... TEGs have been used in numerous applications where they exploit the occurrence of process or waste heat. Temperature monitoring of hot pipes by extracting energy from the large temperature gradient they produce was demonstrated in [2]. In [3] a wireless sensor node for building energy management that harvests from wall heaters was developed. ...
... Equivalent to the electrical matching, thermal matching, i.e. K TEG = K ambient + K ground , maximizes heat flux through the TEG for a given overall temperature gradient [14], resulting in maximum electrical output power (2). The presented thermal model is general to allow using more advanced thermal designs, e.g. ...
We introduce an energy harvesting system capable of converting bipolar thermal gradients to electrical energy. An active rectification circuit, electrical impedance matching and commodity thermoelectric generators (TEGs) are used to efficiently extract energy from very small temperature gradients found at the natural ground-to-air boundary. The full harvesting system is modeled in detail from thermal radiation to the electrical load. This end-to-end model enables system dimensioning to meet specific application requirements. A multi-year deployment of the harvesting system supplying a wireless sensor network (WSN) for environment monitoring demonstrates the applicability of this system in a real application. The case study confirms self-sustainable operation of an application with a 550 uW power footprint. With a maximum harvested power of up to 27.2 mW during the day and 6.3 mW during the night, a significant improvement in both average and maximum harvested power is demonstrated compared to the state-of-the-art.
... They succeeded to harvest about 110 mV/°C and increase the system lifespan by 130 hours. Dalola et al. [20] presented another application of TEH, where they made use of the heat gradients (above 9 ºC) generated by hot walledin pipes to power up an autonomous sensor system for Radio Frequency (RF) communication. The harvested power was about 130 mW. ...
Energy Harvesting (EH) has become a crucial part of self-powered autonomous systems, particularly for Wireless Sensor Network (WSN) nodes and the Internet of Things (IoT) sensors. The main advantages of exploiting Energy Harvesting approach lies on its portability, scalability, and low maintenance, as it reduces the dependency on batteries, therefore offers a sustainable and long-term solution for wireless monitoring over a wide area and a large number of sensor nodes. This paper discusses the use of Thermal Energy Harvesting (TEH) approach to power up a wireless sensor node for IoT applications. Wireless node WSN_1_TEH consists of MEGA328P as the main MCU, nRF24L01 wireless module, and DHT22 sensor. The TEH system consists of two thermoelectric generators with DC-DC boost converter based on MAX757 and an 8200µF storage capacitor. In the experiment, the TEH system was set to function as the only power source for the sensor node; for comparison of its performance with a 7.4 V rechargeable lithium polymer battery-powered counterpart, by operating for 40 hours continuously. In order to reduce power consumption, the WSN_1_TEH node was equipped with an energy-aware Event-Priority-Driven Dissemination algorithm. It was developed to manage the WSN_1_TEH operation and to make the sink station able to detect a missing wireless node within the network, which will guarantee the nodes integrity detection. This algorithm will send out data packet based on event or priority, every 20 s of sleeping period. Besides power saving, it also reduces the overall network traffic. Based on the findings, the overall power consumption of this node is 39 mW in "active with transmission" mode, 28-32 mW in "active without transmission" mode, and only 23 mW when operating in "sleep" mode.
... Unlike energy reservoirs, power scavenging sources are characterised by their power density; the energy provided from these sources depends on the amount of time each source is in operation [26][27][28][29]. One of the popular power scavenging sources is via temperature gradients [30,31]. Energy can be scavenged from the environment using the temperature variations that naturally occur. ...
... Thermoelectric generator can also be an alternative to other energy sources when they are not available. In [26], a TEG is used to power autonomous sensors located in walls, where no light is available. The sensors are powered by the heat dissipated by walled-in pipes of a heating plant where hot air is fluxed. ...
The advent of the Internet of Things has enabled the roll-out of a multitude of Wireless Sensor Networks. These networks can be used in various fields, such as agriculture, industry or the smart city, where they facilitate fine optimization of processes. These devices are often powered by primary or rechargeable batteries, which limits their battery life. Moreover, it is sometimes not possible or financially viable to change and/or recharge these batteries.A possible solution is to harvest energy from the environment to power these sensors. But these energy sources are unreliable, and the sensor must be able to prevent the complete depletion of its energy storage. In order to adapt its energy consumption, the node can match its quality of service to its energetical capabilities. Thus, the device can continuously operate without any service interruption.This thesis presents the methods used for the conception of a completely autonomous sensor, powered by energy harvesting and communicating through a long range LoRa network. In order to ensure its power supply, a board has been designed to harvest energy from multiple energy sources simultaneously. A power management software module has then been developed to calculate an energy budget the sensor can use, and to choose the best way to spend this budget over one or multiple tasks. This work has enabled the development of an energy autonomous industrial sensorprototype.
