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Design of energy harvesting wireless sensors using magnetic phase transition
Energy 180 (2019) 1001e1007
Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
Design of energy harvesting wireless sensors using magnetic phase transition Yasuki Kansha a, b, *, Masanori Ishizuka b a Organization for Programs in Environmental Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, 153-8902, Japan b Collaborative Research Center for Energy Engineering, Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 January 2019 Received in revised form 13 May 2019 Accepted 18 May 2019 Available online 20 May 2019
Many countries have recently become interested in the deployment of cyber-physical systems (CPS) in industry and society for sustainable development. A CPS comprises a data acquisition function, a data storage function, and a network to transfer data. The move to deploy CPS in society makes it necessary to increase their energy efficiency and to find new energy sources. To overcome these energy-related issues, in this research we investigated the possibility of integrating data acquisition sensors with a recently developed energy harvesting system that combines the magnetic phase transition resulting from changes in temperature, and electromagnetic induction resulting from changes in magnetic flux. The proposed system can provide wireless temperature or velocity sensors that directly measure electromotive forces generated by a solenoid following Faraday's law without any additional energy input. Our proposed energy harvesting sensors have the potential to contribute significantly to the development of CPS in the near future. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Wireless sensors Energy harvesting Cyber-physical systems
1. Introduction The principles of ‘Industry 4.0,’ which originated in Germany, are now widely known [1]. Following this trend, most industries are connected by cyber-physical systems (CPS), and data [2] and energy [3] in manufacturing technologies are exchanged and shared. Based on these principles, the Japanese government has developed ‘Society 5.0’ [4], under which, not only industry, but society itself will be changed by information and communication technology (ICT) and the internet of things (IoT) to allow sustainable development. To deploy CPS and the IoT in society, it is necessary to develop overall security systems [5], efficient ICT including data transfer systems in the network, and acquisition systems, such as sensors and actuators [6]. It is also important to find new energy sources to connect and transfer digital data to the cloud and to develop intelligent decision/control systems [7] and learning algorithms [8] for rational operation of the overall networks, incorporating the use of artificial intelligence. In fact, the
* Corresponding author. Organization for Programs in Environmental Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, 153-8902, Japan. E-mail address: [email protected] (Y. Kansha). https://doi.org/10.1016/j.energy.2019.05.128 0360-5442/© 2019 Elsevier Ltd. All rights reserved.
volume of data being transferred to the cloud is increasing rapidly and it is now necessary to have the ability to analyze these data immediately [9]. At the same time, electrical devices such as sensors and actuators have been developing rapidly, including quick response, high precision, and contactless/wireless energy systems [10]. In meeting the requirements for electricity for CPS, the term ‘energy harvesting’ is commonly used. Energy harvesting is the process of converting currently unused energy, such as vibrations [11], radio frequencies, light [12] and low temperature heat [13], into electricity [14]. Piezoelectric elements and antennae are common devices used for converting vibrations or radio frequencies into electricity [15]. Photovoltaics is also a familiar way of converting light into electricity to supply CPS [16]. Thermoelectric elements based on the Seebeck effect are also commonly used to convert heat into electricity [17]. However, the energy efficiency of many of these energy harvesting technologies is still low, and much research is focused on increasing their efficiency. Recently, the authors proposed an energy harvesting system design that uses magnetic phase transition as an isothermal phase transition to convert low temperature heat into electricity by electromagnetic induction based on Faraday's law of induction [18], and investigate its thermodynamic cycle to improve its electric power generation efficiency [19]. Although this system theoretically has high energy
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efficiency, power output is often small from the system due to comparatively slow heat transfer rate in the system. Thus, it is necessary to further improve the system to acquire stable electric power generation for CPS application. Electricity converted from low quality energy by energy harvesting is often used for sensors and actuators to measure information or data in CPS. These devices do not require large power while they work under normal conditions. However, they instantly requires large power at some conditions to measure or transmit data. To satisfy such electrical irregular demands of sensors and actuators, some researchers have developed an amplifier [20], a battery [21] or a networking algorithms [22] for energy harvesting. However, overall energy efficiency of the system decreases and the capacity of the system becomes large due to installation of such additional devices or online computing. Thus, much recent research has focused on the possibility of combining energy harvesting and sensors, including wearable sensors for human information [23] and wireless sensors for environmental information [24]. These sensors called energy harvesting sensors directly sense the measured target without any additional energy conversion, leading to increase overall energy efficiency of CPS. Furthermore, obtained electric energy can be used for the other usage such as data/signal transfer [25]. In this case, semi-permanently measured data can be obtained through CPS on a remote site [26]. Thus, the energy harvesting sensors are expected as a key technology for propagation of a CPS [27]. In this research, the possibility of integrating sensors with our proposed energy harvesting system, which uses magnetic phase transition and electromagnetic induction, was investigated. This paper is organized as follows: In the next section, the proposed energy harvesting temperature sensor is briefly presented. Subsequently, experimental configuration and results to evaluate the possibility for the proposed system as an energy harvesting sensor are shown. Next, the discussions of the results and potentials of the system are shown to conclude this paper. 2. Proposing energy harvesting sensors 2.1. Summary of energy harvesting system using magnetic phase transition The energy efficiency of currently-available systems that harvest energy from low temperature heat around ambient temperature is normally quite low due to the characteristics of exergy or the Carnot efficiency as shown in Fig. 1. In fact, Fig. 1 shows that the theoretical efficiency for energy harvesting from 273 to 323 K is less than 10%. Thus, it is necessary to increase energy efficiency of an energy harvesting system from low temperature heat as much as possible to apply CPS. The authors proposed an energy harvesting system for low temperature heat sources, such as exhaust heat from refrigerators or coolers, that uses magnetic phase transition integrated with electromagnetic induction [18]. In this system, a magnetic material such as gadolinium is cooled with a low temperature heat source to below the Curie temperature (about 292 K for gadolinium). The material magnetizes because it is ferromagnetic. It is then placed near a solenoid and heated by a high temperature heat source to its Curie temperature. The material is demagnetized, becoming paramagnetic, and the magnetic flux from the material that passes through the solenoid decreases as the temperature increases. An electromotive force is generated in the solenoid following Faraday's law. This series of actions generates electricity, harvesting the energy from the low temperature heat. This system uses the latent heat accompanying a magnetic phase transition at the Curie temperature. The theoretical thermodynamic cycle of this system
Fig. 1. Carnot efficiency around ambient temperature.
transitions to a closed trilateral cycle suitable for sensible heat recovery to generate electric power [19]. Although this system theoretically has a high energy efficiency, it still generates too small power output to satisfy CPS demands. 2.2. Energy harvesting temperature sensors for CPS The supply of electricity from energy harvesting is often inconsistent with the electrical demands of sensors that measure data in a CPS. Thus, sensors that can directly measure the sensing target without needing any additional energy are appropriate for use in a CPS. From this aspect, energy harvesting sensors integrating with energy harvesting system with sensors have been recently paid attention. Considering conventional temperature sensors such as thermometers or thermocouples, most of sensing devices use the material characteristics based on their physical chemistry. To connect the sensors with CPS, it is necessary to convert the sensing value to digital/electrical signals. It can be said that thermocouples are inherently suitable for temperature sensors attached with CPS [28]. However, thermocouples requires two terminals made of different materials and connect them in an electric circuit based on Zeebeck effect [29]. Thus, it requires a reference to measure temperature and cannot be worked as wireless sensors. 2.3. Integrating temperature sensor with energy harvesting The above-mentioned energy harvesting system uses the change in entropy at the magnetic phase transition which strongly relates to the ambient temperature of the system. A study of the adiabatic temperature change due to the magnetic phase transition during magnetization of gadolinium from 0 T to 1 T indicates that the change is almost linear from 270 K to the Curie temperature (292 K) and from the Curie temperature to 320 K at a peak of 292 K [30]. From this aspect, the change in entropy at the magnetic phase transition around the Curie temperature may have a linear relationship with the temperature. Thus, observing the flux changes for magnetic materials at different temperatures could allow it to be used as a temperature sensor integrated with energy harvesting around the Curie temperature, without requiring any additional energy instead of thermocouples.
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Fig. 2. Experimental set-up.
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Before measuring the electromotive forces, a gadolinium nugget (2.73 g) was left at each temperature for more than 30 min until the nugget temperature became constant. A permanent magnet (275 mT) was positioned at the edge of a solenoid with 500 coils of iron wire. Using a gauss meter (HMMT-6J04-VF, Lake Shore Cryotronics Inc.), the magnetic flux density at the other end of the solenoid, the nearest point to the gadolinium, was 19 mT. The gadolinium nugget, which had a magnetic flux density of 0 mT without the magnetic field, was fixed to the end of a 40-mm arm, which was rotated horizontally by a motor (rotation speed: 545 /s). The gadolinium was passed over the solenoid. The minimum distance from the gadolinium to the solenoid was 3 mm. Thus, the magnetic flux through the solenoid changed with the position of the gadolinium. It is noted that the magnetic flux was changed by heat transfer to the magnetic materials to use the proposed system as an ambient temperature sensor. However, this change might be too sensitive to examine in the experiments. Therefore, the gadolinium was forced to move by motor for changing the distance to the solenoid at the constant temperature in order to sense the targeted temperature in this experiment. The electromotive force produced by electromagnetic induction and the current were measured by a voltmeter with a shunt resistance of 4.7 U.
3. Experiments
3.2. Experimental results
To examine the possibility of energy harvesting sensors, the relationship between the temperature of a magnetic material and the change in its magnetic flux was investigated by measuring the electromotive force. Gadolinium was selected as the magnetic material because of its Curie temperature.
The large electromotive forces generated are shown in Figs. 3e6. The gray trace shows the raw data and the black line is the 10 ms moving average. All the figures show positive and negative peaks. A positive peak was created when the gadolinium came close to the solenoid and the negative peak was created when the gadolinium passed. To determine the relationship between temperature and the generated electromotive force, the amplitude of the electromotive force was measured. Table 1 lists the amplitude of the electromotive force generated as a moving average at each temperature. At the same time, it lists the maximum power output during changes. Fig. 6 shows a comparison of the 10 ms moving averages of the electromotive forces generated for several motor rotation speeds (182, 363, and 545 /s) at 292 K; the amplitudes were 0.188, 0.309, and 0.386, respectively.
3.1. Experimental set-up Using the following experimental set-up shown in Fig. 2, modified from the set-up that was used to investigate the energy harvesting potential in the previous study [17], the electromotive forces generated by Faraday's law of induction were monitored at four different temperatures (256, 280, 292, 296 K) by an oscilloscope (InfiniiVision DSO-X 2002A, Agilent Technologies Inc.).
Fig. 3. Generated electromotive force at 256 K during one cycle.
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Fig. 4. Generated electromotive force at 280 K during one cycle.
4. Discussion Figs. 3e6 obviously show that the amplitude of the electromotive force was closely related to the temperature of the gadolinium. In fact, peaks of the amplitude at 292 K (Fig. 3) are larger and sharper than those of the other figures. The relationship between the electromotive force generated and the change in the magnetic flux must be linear, because Faraday's law of induction is given by the following equation:
ε¼ N
dF dt
(1)
where ε is the electromotive force, N is the number of turns on the solenoid, F represents the magnetic flux, and t is time. Now, N was fixed to 500 in the experiments. From Eq. (1), it can be understood
that the amplitude of the electromotive force at each temperature only depends on magnetic flux change. Table 1 indicates that the electromotive force with the largest amplitude was generated at 292 K (Curie temperature) and the amplitude decreased with the temperature. Furthermore, the amplitude was smaller at 296 K than at 292 K. This trend, which has an amplitude peak of the electromotive force at the Curie temperature and has larger change below the Curie temperature than above it, seems to be consistent with the adiabatic temperature change (entropy change) [30]. Thus, it is inferred from these results that a change in the magnetic flux and the accompanying generation of an electromotive force are followed by a change in entropy around the Curie temperature. Thus, knowing the detailed relationship between the temperature and the corresponding change in magnetic flux, it is possible to
Fig. 5. Generated electromotive force at 292 K during one cycle.
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Fig. 6. Generated electromotive force at 296 K during one cycle.
Table 1 Relationship between temperature and amplitude of electromotive forces. Temperature [K] minimum value of electromotive force [mV] maximum value of electromotive force [mV] amplitude of electromotive force1) [mV] maximum power output2) [nW] 1) 2)
256 0.182 0.023 0.102 1.8
280 0.466 0.239 0.352 28.2
292 0.505 0.267 0.386 34.7
296 0.228 0.018 0.105 2.4
amplitude ¼ (maximum-minimum)/2 power output ¼ V2/R
use the gadolinium as a temperature sensor by measuring the electromotive force with high accuracy. It is well known that the power outputs are calculated by the following equation:
w¼
V2 R
(2)
where W is the power output, V is the voltage consistent with the
Fig. 7. Moving average of generated electromotive force at 292 K by different rotation speeds.
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Fig. 8. The image of the energy harvesting temperature/velocity sensor and magnetic flux change, a) ferromagnetic material below Curie temperature, b) ferro/paramagnetic material at Curie temperature, and c) paramagnetic material above Curie temperature.
observed electromotive force (ε) and R is the value of the shunt resistance. From this relationship, the producing energy amount was calculated to 1.1 nJ at 292 K. The reason why this value was quite small is that the experiments were conducted at the constant temperature. The system might be only affected by their magnetic entropy change. Although harvesting energy amount was small, the system can be observed the targeted temperature change. The conventional temperature sensors which do not have energy harvesting function often require an additional battery or rectifier for integrating with energy harvesting due to inconsistency with electrical demand among them, leading to deterioration of energy efficiency of the system. However, the proposed system can be directly observed the temperature change without any additional devices. Thus, it can be said that this system showed the large potential as a temperature sensor. Fig. 7 shows that the amplitude of the generated electromotive force and the intervals between the peaks (53.5, 40.5 and 31.5 ms) changed following the rotational speed of the gadolinium at a constant temperature. From Eq. (1), the electromotive force is proportional to the change in magnetic flux through the solenoid. Thus, the system can, at the same time, be used as a velocity sensor. For example, the velocity of the heating medium which moving uniform velocity in a tube such as a downer or riser of a circulating fluidized bed can be measured by the proposed energy harvesting sensor. In the experiment, gadolinium was used as a representative magnetic material because the Curie temperature of gadolinium is close to ambient temperature. However, different temperature or
velocity sensors can be created for different temperature ranges using different magnetic materials such as iron, cobalt and their composites. In addition, gadolinium is a rare-earth element. It is, therefore, necessary to develop suitable magnetic materials which show large magnetic flux changes in high temperature range or cryogenic temperature range to propagate the proposed energy harvesting sensors for application from economic point of view. Moreover, gadolinium can be used to create sensors that convert information to magnetic flux changes and a solenoid can work as a receiver/converter of signals to electromotive forces. Thus, it is not necessary for the sensors and receivers to make contact; in other words, the system is wireless. Fig. 8 shows a summarized image of this system used as a temperature/velocity sensor in the case of sensing the targeted heating medium. The system works in three modes: a) below, b) at, and c) above the Curie temperature to create a change in the magnetic flux followed by a change in entropy. It can be seen that magnetic flux change is maximized and the electromotive forces becomes the largest when the sensing target is at the Curie temperature of the magnetic material. Thus, this system can be sensed the temperature and velocity after calibration of the sensors. From these points of view, it can be said that the proposed sensor has a large potential of CPS applications. 5. Conclusion This paper proposes conceptual designs for energy harvesting temperature sensors around the Curie temperature of the magnetic material. By integrating magnetic phase transition with
Y. Kansha, M. Ishizuka / Energy 180 (2019) 1001e1007
electromagnetic induction, these sensors can sense without needing any additional energy, leading to an increase in the energy efficiency of CPS. In the experiments, measureable difference of electromotive force at each temperature (256, 280, 292, and 296 K) and velocity (0.127 0.253, and 0.380 m/s) were acquired. This means that the proposed energy harvesting sensor can work as not only temperature but also velocity sensors. Furthermore, as the sensor itself is not attached to a receiver, it functions as a wireless sensor. To increase accuracy and numbers of applications of the proposed sensors, it is necessary to conduct further investigation such as obtaining more data for calibration and creating magnetic composite materials for application at wider temperature range. In addition, it would be desirable that the proposed sensors generate higher electrical power output for transmitting the measured data. It summarized that our proposed energy harvesting wireless sensors can measure the targeted temperature and velocity after calibration and transmit the measured data by harvesting energy. Therefore, the proposed energy harvesting sensors have the great potential to contribute significantly to the development of CPS in the near future. Acknowledgements This work was supported by the Japan Society for the Promotion of Science, (KAKENHI Grant Number 16K14544) and the Tonen General Sekiyu Research & Development Encouragement & Assistance Foundation. References [1] Hermann M, Pentek T, Otto B. Design principles for industrie 4.0 scenarios. In: 49th Hawaii international conference on system sciences. IEEE Computer Society; 2016. p. 3928e37. https://doi.org/10.1109/HICSS.2016.488. [2] Faheem M, Shah SBH, Butt RA, Raza B, Anwar M, Ashraf MW, Ngadi MdA, Gungor VC. Smart grid communication and information technologies in the perspective of industry 4.0: opportunities and challenges. Comput. Sci. Rev. 2018;30:1e30. [3] Shi L, Dai Q, Ni Y. Cyber-physical interactions in power systems: a review of models, methods, and applications. Electr Power Syst Res 2018;163:396e412. [4] Website of public relations office, Government of Japan https://www.govonline.go.jp/cam/s5/eng/index.html (4/Jan./2019 accessed). [5] Alguliyev R, Imamverdiyev Y, Sukhostat L. Cyber-physical systems and their security issues. Comput Ind 2018;100:212e23. [6] Patil K, Fiems D. The value of information in energy harvesting sensor networks. Oper Res Lett 2018;46:362e6. [7] Shen B, Wang Z, Wang D, Luo J, Pu H, Peng Y. Finite-horizon filtering for a class of nonlinear time-delayed systems with an energy harvesting sensor. Automatica 2019;100:144e52. [8] Zhang X, Yu T, Xu Z, Fan Z. A cyber-physical system with parallel learning for distributed energy management of a microgrid. Energy 2018;165:205e21.
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[9] Horcas JM, Pinto M, Fuentes L. Context-aware energy-efficient applications for cyber-physical systems. Ad Hoc Netw 2019;82:15e30. [10] Huang TC, Leu YG, Huang CW. Powering IoTs with a feedforward quasi universal boost converter energy harvester. Energy 2017;133:879e86. [11] Wang H, He C, Lv S, Sun H. Anew electromagnetic vibrational energy harvesting device for swaying cables. Appl Energy 2018;228:2448e61. [12] Yu H, Yue Q. Multi-source energy Harvesting System for underwater wireless sensor networks. Procedia Comput. Sci. 2015;46:1041e8. [13] Guan M, Wang K, Xu D, Liao WH. Design and experimental investigation of a low-voltage thermoelectric energy harvesting system for wireless sensor nodes. Energy Convers Manag 2017;138:30e7. [14] Babar M, Rahman A, Arif F, Jeon G. Energy-harvesting based on internet of things and big data analytics for smart health monitoring. Sust. Comput. 2018;20:155e64. [15] Wang DW, Mo JI, Wang XF, Ouyang H, Zhou ZR. Experimental and numerical investigations of the piezoelectric energy harvesting via friction-induced vibration. Energy Convers Manag 2018;171:1134e49. [16] Gunduz H, Jayaweera D. Reliability assessment of a power system with cyberphysical interactive operation of photovoltaic systems. Int. Electr. Power Energy Syst. 2018;101:371e84. [17] Ando Junior OH, Maran ALO, Henao NC. A review of the development and application of thermoelectric microgenerators for energy harvesting. Renew Sustain Energy Rev 2018;91:376e93. [18] Kansha Y, Ishizuka M. Efficient power generation from low temperature heat source. Chem Eng Trans 2017;56:1519e24. https://doi.org/10.3303/ CET1756254. [19] Kansha Y, Ishizuka M. Power generation from low temperature waste heat using magnetic phase transition. Chem Eng Trans 2018;70:43e8. https:// doi.org/10.3303/CET1870008. € Baghaee S, Uysal-Biyikoglu E, Külah H. [20] Chamanian S, Ulus¸an H, Zorlu O, Wearable battery-less wireless sensor network with electromagnetic energy harvesting system. Sensor Actuator 2016;249:77e84. [21] Baranov AM, Akbari S, Spirjakin D, Bragar A, Karelin A. Feasibility of RF energy harvesting for wireless gas sensor nodes. Sensor Actuator 2018;275:37e43. [22] Shen B, Wang Z, Wang D, Luo J, Pu H, Peng Y. Finite-horizon filtering for a class of nonlinear time-delayed systems with an energy harvesting sensor. Automatica 2019;100:144e52. [23] Myers A, Hodges R, Jur JS. Human and environmental analysis of wearable thermal energy harvesting. Energy Convers Manag 2017;143:218e26. [24] Babayo AA, Anisi MH, Sli I. A review on energy management schemes in energy harvesting wireless sensor networks. Renew Sustain Energy Rev 2017;76:1176e84. [25] Qiao G, Sun G, Hong Y, Qiu Y, Ou J. Remote corrosion monitoring of the RC structures using the electrochemical wireless energy-harvesting sensors and networks. NDT&E Int 2011;44:583e8. [26] Aliyu F, Sheltami T. Development of an energy-harvesting toxic and combustible gas sensor for oil and gas industries. Sensor Actuator B 2016;231: 265e75. [27] Kausar ASMZ, Rezan AW, Saleh MU, Ramiah H. Energizing wireless sensor networks by energy harvesting systems: scopes, challenges and approaches. Renew Sustain Energy Rev 2014;38:973e89. €hnert T, Paz E, Ferreira R, Freitas PP. Magnetic tunnel junction thermo[28] Bo couple for thermoelectric power harvesting. Phys Lett A 2018;382:1437e40. [29] Bouaanani Y, Baucour P, Gavignet E, Lanzetta F. Performance of a thermocouple subjected to a variable current. Int J Therm Sci 2018;134:440e52. [30] Kotani Y, Kansha Y, Tsutsumi A. Conceptual design of an active magnetic regenerative heat circulator based on self-heat recuperation technology. Energy 2013;55:127e33.
Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
Design of energy harvesting wireless sensors using magnetic phase transition Yasuki Kansha a, b, *, Masanori Ishizuka b a Organization for Programs in Environmental Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, 153-8902, Japan b Collaborative Research Center for Energy Engineering, Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 January 2019 Received in revised form 13 May 2019 Accepted 18 May 2019 Available online 20 May 2019
Many countries have recently become interested in the deployment of cyber-physical systems (CPS) in industry and society for sustainable development. A CPS comprises a data acquisition function, a data storage function, and a network to transfer data. The move to deploy CPS in society makes it necessary to increase their energy efficiency and to find new energy sources. To overcome these energy-related issues, in this research we investigated the possibility of integrating data acquisition sensors with a recently developed energy harvesting system that combines the magnetic phase transition resulting from changes in temperature, and electromagnetic induction resulting from changes in magnetic flux. The proposed system can provide wireless temperature or velocity sensors that directly measure electromotive forces generated by a solenoid following Faraday's law without any additional energy input. Our proposed energy harvesting sensors have the potential to contribute significantly to the development of CPS in the near future. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Wireless sensors Energy harvesting Cyber-physical systems
1. Introduction The principles of ‘Industry 4.0,’ which originated in Germany, are now widely known [1]. Following this trend, most industries are connected by cyber-physical systems (CPS), and data [2] and energy [3] in manufacturing technologies are exchanged and shared. Based on these principles, the Japanese government has developed ‘Society 5.0’ [4], under which, not only industry, but society itself will be changed by information and communication technology (ICT) and the internet of things (IoT) to allow sustainable development. To deploy CPS and the IoT in society, it is necessary to develop overall security systems [5], efficient ICT including data transfer systems in the network, and acquisition systems, such as sensors and actuators [6]. It is also important to find new energy sources to connect and transfer digital data to the cloud and to develop intelligent decision/control systems [7] and learning algorithms [8] for rational operation of the overall networks, incorporating the use of artificial intelligence. In fact, the
* Corresponding author. Organization for Programs in Environmental Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, 153-8902, Japan. E-mail address: [email protected] (Y. Kansha). https://doi.org/10.1016/j.energy.2019.05.128 0360-5442/© 2019 Elsevier Ltd. All rights reserved.
volume of data being transferred to the cloud is increasing rapidly and it is now necessary to have the ability to analyze these data immediately [9]. At the same time, electrical devices such as sensors and actuators have been developing rapidly, including quick response, high precision, and contactless/wireless energy systems [10]. In meeting the requirements for electricity for CPS, the term ‘energy harvesting’ is commonly used. Energy harvesting is the process of converting currently unused energy, such as vibrations [11], radio frequencies, light [12] and low temperature heat [13], into electricity [14]. Piezoelectric elements and antennae are common devices used for converting vibrations or radio frequencies into electricity [15]. Photovoltaics is also a familiar way of converting light into electricity to supply CPS [16]. Thermoelectric elements based on the Seebeck effect are also commonly used to convert heat into electricity [17]. However, the energy efficiency of many of these energy harvesting technologies is still low, and much research is focused on increasing their efficiency. Recently, the authors proposed an energy harvesting system design that uses magnetic phase transition as an isothermal phase transition to convert low temperature heat into electricity by electromagnetic induction based on Faraday's law of induction [18], and investigate its thermodynamic cycle to improve its electric power generation efficiency [19]. Although this system theoretically has high energy
1002
Y. Kansha, M. Ishizuka / Energy 180 (2019) 1001e1007
efficiency, power output is often small from the system due to comparatively slow heat transfer rate in the system. Thus, it is necessary to further improve the system to acquire stable electric power generation for CPS application. Electricity converted from low quality energy by energy harvesting is often used for sensors and actuators to measure information or data in CPS. These devices do not require large power while they work under normal conditions. However, they instantly requires large power at some conditions to measure or transmit data. To satisfy such electrical irregular demands of sensors and actuators, some researchers have developed an amplifier [20], a battery [21] or a networking algorithms [22] for energy harvesting. However, overall energy efficiency of the system decreases and the capacity of the system becomes large due to installation of such additional devices or online computing. Thus, much recent research has focused on the possibility of combining energy harvesting and sensors, including wearable sensors for human information [23] and wireless sensors for environmental information [24]. These sensors called energy harvesting sensors directly sense the measured target without any additional energy conversion, leading to increase overall energy efficiency of CPS. Furthermore, obtained electric energy can be used for the other usage such as data/signal transfer [25]. In this case, semi-permanently measured data can be obtained through CPS on a remote site [26]. Thus, the energy harvesting sensors are expected as a key technology for propagation of a CPS [27]. In this research, the possibility of integrating sensors with our proposed energy harvesting system, which uses magnetic phase transition and electromagnetic induction, was investigated. This paper is organized as follows: In the next section, the proposed energy harvesting temperature sensor is briefly presented. Subsequently, experimental configuration and results to evaluate the possibility for the proposed system as an energy harvesting sensor are shown. Next, the discussions of the results and potentials of the system are shown to conclude this paper. 2. Proposing energy harvesting sensors 2.1. Summary of energy harvesting system using magnetic phase transition The energy efficiency of currently-available systems that harvest energy from low temperature heat around ambient temperature is normally quite low due to the characteristics of exergy or the Carnot efficiency as shown in Fig. 1. In fact, Fig. 1 shows that the theoretical efficiency for energy harvesting from 273 to 323 K is less than 10%. Thus, it is necessary to increase energy efficiency of an energy harvesting system from low temperature heat as much as possible to apply CPS. The authors proposed an energy harvesting system for low temperature heat sources, such as exhaust heat from refrigerators or coolers, that uses magnetic phase transition integrated with electromagnetic induction [18]. In this system, a magnetic material such as gadolinium is cooled with a low temperature heat source to below the Curie temperature (about 292 K for gadolinium). The material magnetizes because it is ferromagnetic. It is then placed near a solenoid and heated by a high temperature heat source to its Curie temperature. The material is demagnetized, becoming paramagnetic, and the magnetic flux from the material that passes through the solenoid decreases as the temperature increases. An electromotive force is generated in the solenoid following Faraday's law. This series of actions generates electricity, harvesting the energy from the low temperature heat. This system uses the latent heat accompanying a magnetic phase transition at the Curie temperature. The theoretical thermodynamic cycle of this system
Fig. 1. Carnot efficiency around ambient temperature.
transitions to a closed trilateral cycle suitable for sensible heat recovery to generate electric power [19]. Although this system theoretically has a high energy efficiency, it still generates too small power output to satisfy CPS demands. 2.2. Energy harvesting temperature sensors for CPS The supply of electricity from energy harvesting is often inconsistent with the electrical demands of sensors that measure data in a CPS. Thus, sensors that can directly measure the sensing target without needing any additional energy are appropriate for use in a CPS. From this aspect, energy harvesting sensors integrating with energy harvesting system with sensors have been recently paid attention. Considering conventional temperature sensors such as thermometers or thermocouples, most of sensing devices use the material characteristics based on their physical chemistry. To connect the sensors with CPS, it is necessary to convert the sensing value to digital/electrical signals. It can be said that thermocouples are inherently suitable for temperature sensors attached with CPS [28]. However, thermocouples requires two terminals made of different materials and connect them in an electric circuit based on Zeebeck effect [29]. Thus, it requires a reference to measure temperature and cannot be worked as wireless sensors. 2.3. Integrating temperature sensor with energy harvesting The above-mentioned energy harvesting system uses the change in entropy at the magnetic phase transition which strongly relates to the ambient temperature of the system. A study of the adiabatic temperature change due to the magnetic phase transition during magnetization of gadolinium from 0 T to 1 T indicates that the change is almost linear from 270 K to the Curie temperature (292 K) and from the Curie temperature to 320 K at a peak of 292 K [30]. From this aspect, the change in entropy at the magnetic phase transition around the Curie temperature may have a linear relationship with the temperature. Thus, observing the flux changes for magnetic materials at different temperatures could allow it to be used as a temperature sensor integrated with energy harvesting around the Curie temperature, without requiring any additional energy instead of thermocouples.
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Fig. 2. Experimental set-up.
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Before measuring the electromotive forces, a gadolinium nugget (2.73 g) was left at each temperature for more than 30 min until the nugget temperature became constant. A permanent magnet (275 mT) was positioned at the edge of a solenoid with 500 coils of iron wire. Using a gauss meter (HMMT-6J04-VF, Lake Shore Cryotronics Inc.), the magnetic flux density at the other end of the solenoid, the nearest point to the gadolinium, was 19 mT. The gadolinium nugget, which had a magnetic flux density of 0 mT without the magnetic field, was fixed to the end of a 40-mm arm, which was rotated horizontally by a motor (rotation speed: 545 /s). The gadolinium was passed over the solenoid. The minimum distance from the gadolinium to the solenoid was 3 mm. Thus, the magnetic flux through the solenoid changed with the position of the gadolinium. It is noted that the magnetic flux was changed by heat transfer to the magnetic materials to use the proposed system as an ambient temperature sensor. However, this change might be too sensitive to examine in the experiments. Therefore, the gadolinium was forced to move by motor for changing the distance to the solenoid at the constant temperature in order to sense the targeted temperature in this experiment. The electromotive force produced by electromagnetic induction and the current were measured by a voltmeter with a shunt resistance of 4.7 U.
3. Experiments
3.2. Experimental results
To examine the possibility of energy harvesting sensors, the relationship between the temperature of a magnetic material and the change in its magnetic flux was investigated by measuring the electromotive force. Gadolinium was selected as the magnetic material because of its Curie temperature.
The large electromotive forces generated are shown in Figs. 3e6. The gray trace shows the raw data and the black line is the 10 ms moving average. All the figures show positive and negative peaks. A positive peak was created when the gadolinium came close to the solenoid and the negative peak was created when the gadolinium passed. To determine the relationship between temperature and the generated electromotive force, the amplitude of the electromotive force was measured. Table 1 lists the amplitude of the electromotive force generated as a moving average at each temperature. At the same time, it lists the maximum power output during changes. Fig. 6 shows a comparison of the 10 ms moving averages of the electromotive forces generated for several motor rotation speeds (182, 363, and 545 /s) at 292 K; the amplitudes were 0.188, 0.309, and 0.386, respectively.
3.1. Experimental set-up Using the following experimental set-up shown in Fig. 2, modified from the set-up that was used to investigate the energy harvesting potential in the previous study [17], the electromotive forces generated by Faraday's law of induction were monitored at four different temperatures (256, 280, 292, 296 K) by an oscilloscope (InfiniiVision DSO-X 2002A, Agilent Technologies Inc.).
Fig. 3. Generated electromotive force at 256 K during one cycle.
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Fig. 4. Generated electromotive force at 280 K during one cycle.
4. Discussion Figs. 3e6 obviously show that the amplitude of the electromotive force was closely related to the temperature of the gadolinium. In fact, peaks of the amplitude at 292 K (Fig. 3) are larger and sharper than those of the other figures. The relationship between the electromotive force generated and the change in the magnetic flux must be linear, because Faraday's law of induction is given by the following equation:
ε¼ N
dF dt
(1)
where ε is the electromotive force, N is the number of turns on the solenoid, F represents the magnetic flux, and t is time. Now, N was fixed to 500 in the experiments. From Eq. (1), it can be understood
that the amplitude of the electromotive force at each temperature only depends on magnetic flux change. Table 1 indicates that the electromotive force with the largest amplitude was generated at 292 K (Curie temperature) and the amplitude decreased with the temperature. Furthermore, the amplitude was smaller at 296 K than at 292 K. This trend, which has an amplitude peak of the electromotive force at the Curie temperature and has larger change below the Curie temperature than above it, seems to be consistent with the adiabatic temperature change (entropy change) [30]. Thus, it is inferred from these results that a change in the magnetic flux and the accompanying generation of an electromotive force are followed by a change in entropy around the Curie temperature. Thus, knowing the detailed relationship between the temperature and the corresponding change in magnetic flux, it is possible to
Fig. 5. Generated electromotive force at 292 K during one cycle.
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Fig. 6. Generated electromotive force at 296 K during one cycle.
Table 1 Relationship between temperature and amplitude of electromotive forces. Temperature [K] minimum value of electromotive force [mV] maximum value of electromotive force [mV] amplitude of electromotive force1) [mV] maximum power output2) [nW] 1) 2)
256 0.182 0.023 0.102 1.8
280 0.466 0.239 0.352 28.2
292 0.505 0.267 0.386 34.7
296 0.228 0.018 0.105 2.4
amplitude ¼ (maximum-minimum)/2 power output ¼ V2/R
use the gadolinium as a temperature sensor by measuring the electromotive force with high accuracy. It is well known that the power outputs are calculated by the following equation:
w¼
V2 R
(2)
where W is the power output, V is the voltage consistent with the
Fig. 7. Moving average of generated electromotive force at 292 K by different rotation speeds.
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Fig. 8. The image of the energy harvesting temperature/velocity sensor and magnetic flux change, a) ferromagnetic material below Curie temperature, b) ferro/paramagnetic material at Curie temperature, and c) paramagnetic material above Curie temperature.
observed electromotive force (ε) and R is the value of the shunt resistance. From this relationship, the producing energy amount was calculated to 1.1 nJ at 292 K. The reason why this value was quite small is that the experiments were conducted at the constant temperature. The system might be only affected by their magnetic entropy change. Although harvesting energy amount was small, the system can be observed the targeted temperature change. The conventional temperature sensors which do not have energy harvesting function often require an additional battery or rectifier for integrating with energy harvesting due to inconsistency with electrical demand among them, leading to deterioration of energy efficiency of the system. However, the proposed system can be directly observed the temperature change without any additional devices. Thus, it can be said that this system showed the large potential as a temperature sensor. Fig. 7 shows that the amplitude of the generated electromotive force and the intervals between the peaks (53.5, 40.5 and 31.5 ms) changed following the rotational speed of the gadolinium at a constant temperature. From Eq. (1), the electromotive force is proportional to the change in magnetic flux through the solenoid. Thus, the system can, at the same time, be used as a velocity sensor. For example, the velocity of the heating medium which moving uniform velocity in a tube such as a downer or riser of a circulating fluidized bed can be measured by the proposed energy harvesting sensor. In the experiment, gadolinium was used as a representative magnetic material because the Curie temperature of gadolinium is close to ambient temperature. However, different temperature or
velocity sensors can be created for different temperature ranges using different magnetic materials such as iron, cobalt and their composites. In addition, gadolinium is a rare-earth element. It is, therefore, necessary to develop suitable magnetic materials which show large magnetic flux changes in high temperature range or cryogenic temperature range to propagate the proposed energy harvesting sensors for application from economic point of view. Moreover, gadolinium can be used to create sensors that convert information to magnetic flux changes and a solenoid can work as a receiver/converter of signals to electromotive forces. Thus, it is not necessary for the sensors and receivers to make contact; in other words, the system is wireless. Fig. 8 shows a summarized image of this system used as a temperature/velocity sensor in the case of sensing the targeted heating medium. The system works in three modes: a) below, b) at, and c) above the Curie temperature to create a change in the magnetic flux followed by a change in entropy. It can be seen that magnetic flux change is maximized and the electromotive forces becomes the largest when the sensing target is at the Curie temperature of the magnetic material. Thus, this system can be sensed the temperature and velocity after calibration of the sensors. From these points of view, it can be said that the proposed sensor has a large potential of CPS applications. 5. Conclusion This paper proposes conceptual designs for energy harvesting temperature sensors around the Curie temperature of the magnetic material. By integrating magnetic phase transition with
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electromagnetic induction, these sensors can sense without needing any additional energy, leading to an increase in the energy efficiency of CPS. In the experiments, measureable difference of electromotive force at each temperature (256, 280, 292, and 296 K) and velocity (0.127 0.253, and 0.380 m/s) were acquired. This means that the proposed energy harvesting sensor can work as not only temperature but also velocity sensors. Furthermore, as the sensor itself is not attached to a receiver, it functions as a wireless sensor. To increase accuracy and numbers of applications of the proposed sensors, it is necessary to conduct further investigation such as obtaining more data for calibration and creating magnetic composite materials for application at wider temperature range. In addition, it would be desirable that the proposed sensors generate higher electrical power output for transmitting the measured data. It summarized that our proposed energy harvesting wireless sensors can measure the targeted temperature and velocity after calibration and transmit the measured data by harvesting energy. Therefore, the proposed energy harvesting sensors have the great potential to contribute significantly to the development of CPS in the near future. Acknowledgements This work was supported by the Japan Society for the Promotion of Science, (KAKENHI Grant Number 16K14544) and the Tonen General Sekiyu Research & Development Encouragement & Assistance Foundation. References [1] Hermann M, Pentek T, Otto B. Design principles for industrie 4.0 scenarios. In: 49th Hawaii international conference on system sciences. IEEE Computer Society; 2016. p. 3928e37. https://doi.org/10.1109/HICSS.2016.488. [2] Faheem M, Shah SBH, Butt RA, Raza B, Anwar M, Ashraf MW, Ngadi MdA, Gungor VC. Smart grid communication and information technologies in the perspective of industry 4.0: opportunities and challenges. Comput. Sci. Rev. 2018;30:1e30. [3] Shi L, Dai Q, Ni Y. Cyber-physical interactions in power systems: a review of models, methods, and applications. Electr Power Syst Res 2018;163:396e412. [4] Website of public relations office, Government of Japan https://www.govonline.go.jp/cam/s5/eng/index.html (4/Jan./2019 accessed). [5] Alguliyev R, Imamverdiyev Y, Sukhostat L. Cyber-physical systems and their security issues. Comput Ind 2018;100:212e23. [6] Patil K, Fiems D. The value of information in energy harvesting sensor networks. Oper Res Lett 2018;46:362e6. [7] Shen B, Wang Z, Wang D, Luo J, Pu H, Peng Y. Finite-horizon filtering for a class of nonlinear time-delayed systems with an energy harvesting sensor. Automatica 2019;100:144e52. [8] Zhang X, Yu T, Xu Z, Fan Z. A cyber-physical system with parallel learning for distributed energy management of a microgrid. Energy 2018;165:205e21.
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