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Smart Materials Research
Volume 2012 (2012), Article ID 853481, 13 pages
Piezoelectric Energy Harvesting Devices: An Alternative Energy Source for Wireless Sensors
1Department of Physics, Midlands State University, P/Bag 9055, Gweru, Zimbabwe
2Department of Physics, University of Botswana, P/Bag 0022, Gaborone, Botswana
Received 14 December 2011; Revised 27 February 2012; Accepted 5 March 2012
Academic Editor: Mickaël Lallart
Copyright © 2012 Action Nechibvute et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The recent advances in ultralow power device integration, communication electronics, and microelectromechanical systems (MEMS) technology have fuelled the emerging technology of wireless sensor networks (WSNs). The spatial distributed nature of WSNs often requires that batteries power the individual sensor nodes. One of the major limitations on performance and lifetime of WSNs is the limited capacity of these finite power sources, which must be manually replaced when they are depleted. Moreover, the embedded nature of some of the sensors and hazardous sensing environment make battery replacement very difficult and costly. The process of harnessing and converting ambient energy sources into usable electrical energy is called energy harvesting. Energy harvesting raises the possibility of self-powered systems which are ubiquitous and truly autonomous, and without human intervention for energy replenishment. Among the ambient energy sources such as solar energy, heat, and wind, mechanical vibrations are an attractive ambient source mainly because they are widely available and are ideal for the use of piezoelectric materials, which have the ability to convert mechanical strain energy into electrical energy. This paper presents a concise review of piezoelectric microgenerators and nanogenerators as a renewable energy resource to power wireless sensors.
The advances in low power electronics, and wireless sensor networks (WSNs) in particular, have driven numerous researches in the field of energy harvesting in the past decade [1–3]. A wireless sensor node consists of low power microcontroller unit, radio frequency transceiver and microelectromechanical- (MEMS-) based sensor. The task of each node is to collect and transmit data to the outside world via a radio link. Thousands of spatially distributed wireless sensors can be developed which can be embedded virtually anywhere in civil structures, bridges, or in the human body. WSN technology has gained increasing importance in industrial automation [4, 5], structural health monitoring , healthcare , agriculture , and civil and military applications [9–11]. Traditionally, batteries are used as the electrical energy power sources to power wireless sensors and embedded electronics. However, batteries have a limited life span and they are expensive to maintain and hence they are not a long-term viable source of energy for WSNs and embedded systems. In fact, the limited capacity of batteries is one of the main factors constraining the performance and limiting the lifespan of a typical WSN [2, 3]. Energy harvesting is the most promising way of overcoming the challenges currently presented by finite life power sources like batteries. The process of energy harvesting involves the harnessing of ambient energy from within the vicinity of the sensor device and converting this energy into usable electrical energy. Compared to batteries, energy harvesting presents a potentially infinite source of energy for powering wireless sensor devices and embedded electronics in general. Energy harvesting is not a new concept; in essence it has been practiced for decades in the context of windmills to harness energy from wind, in hydroelectric generators to harvest energy from moving water, and in solar panels that power satellites using energy from the sun. However, what is new with energy harvesting technology is how to design and implement efficient energy harvesting capabilities into modern embedded systems while satisfying all their constraints. For any energy harvesting system to be attractive, it should allow miniaturization and integration using the present MEMS technology, otherwise it is not useful for embedded system applications. While solar energy harvesting is a fairly established technology, it is not the best choice for mobile, implantable, and embedded electronics where solar energy is not accessible. Mechanical energy in form of ambient vibrations, fluid flow, machine rotations, and biomotion presents a source of energy that is available widely and at all times. Piezoelectric materials can be used to harvest this energy since they have the unique ability of converting mechanical strain energy into useful electrical energy. Piezoelectric energy harvesting devices—in the form of MEMS generators or nanogenerators—are a novel technology that is a reliable alternative energy source for powering wireless sensor devices. Unlike conventional MEMS generators, nanogenerators have an added advantage of being flexible and foldable power sources which is ideal for applications such as implantable biomedical sensors [12, 13].
This paper discusses the recent advances in micro- and nanoscale energy generation using piezoelectric materials for ultra low power sensor applications.
The paper is organised as follows: Section 2 gives a brief overview of the energy sources for wireless sensor devices. Section 3 gives a brief discussion of energy harvesting from vibrations using piezoelectric smart materials. Section 4 presents the application context of energy harvesting devices, power management issues, and challenges and suggestions for future research efforts necessary for improvement of piezoelectric energy generators. Section 5 concludes the paper.
2. Energy Sources for WSNs
2.1. Overview of Power Requirements of a Typical Wireless Sensor Node
A wireless sensor node is designed to perform sensing, data acquisition, localized processing, and wireless communication and is usually powered by battery. A power generator which scavenges energy from the immediate environment of the sensor can potentially be used to recharge the battery or independently power the senor node. A typical wireless sensor node is shown in Figure 1, with each block showing a subsystem with its own power requirements.
The subsystems in Figure 1 essentially show the power consuming elements of the sensor node based on functionality, and Figure 2 shows the typical distribution of power consumption amongst these subsystems .
2.1.1. Sensing Subsystem
The sensing subsystem consists mainly of the sensors and an analog to digital conversion (ADC) unit and is responsible for converting the physical phenomena of interest into digital signal form. The power consumed in the sensing subsystem is used in sensor sampling, which includes the wake-up and stabilization time associated with the sensor and the data acquisition time. At all other times, the sensors are completely off and consume no power. The power consumption of the ADC is typically proportional to the amount of the samples acquired and the sampling rate (SR) used . With a low rate ADC and passive sensors, the sensing subsystem will be one of the least energy consumers. However, if higher rate ADC’s and/or energy hungry sensors are used for a particular application, the power consumption of the sensing subsystem can quickly rival that of the communications subsystem . Some power consumption specifications for various senor devices used in a building management system  are shown in Table 1 to give a general idea of the order of the energy requirements.
2.1.2. Computing Subsystem
The computing subsystem is comprised of a processing unit which is usually a microcontroller unit (MCU) and the supporting electronics. This subsystem controls all sensor node activities and performs some local processing. When not processing data and not controlling the system operation, the processor is in a low power sleep mode. Table 2 shows an overview of the power requirements of some of the self-microprocessor systems employed in wireless sensor technologies.
The energy required by the computing subsystem to complete a task, , is approximated by , where is the computational complexity, is the processor frequency, is the switching capacitance, and and are hardware specific constants.
2.1.3. Communications Subsystem
The communications subsystem comprises mainly the radio transceiver (RF transceiver) with the amplifiers and associated electronics. The RF transceiver enables the wireless module to communicate and transmit the processed sensor data. When the RF transceiver is not transmitting or receiving, the transceiver is in a low power sleep mode. As shown in Figure 2, communication subsystem—transmitting, receiving, and listening—dominates the scarce power budget with 60% consumption of the total available energy and determines the lifetime of sensor network. Its power consumption is evaluated by such parameters as voltage supply, transmitting current, receiving current, and current at power-down mode. Typical power parameters for some communication modules are shown in Table 3. The energy consumed by the communications subsystem, can be estimated by : where is the transmission distance, is the number of bits to be transmitted during transmission event i, is the sample resolution in bits per sample, b is the modulation setting for the transmitter, F, and are hardware specific constants.
Table 3 shows the power consumption specifications of some RF modules based on Zigbee/IEEE802.15.4 protocol specification.
From Table 3, CC2420 RF module has the lowest power parameters in terms of current in different operation modes and the supply voltage. Equivalently, the power consumption of the CC2420 RF module can be quoted as 36.5 mW in transmit mode, 41.4 mW in receive mode, 41.4 mW in idle mode, and 42 μW in sleep mode . The idle mode consumes nearly the same power as the transmit mode, and hence turning the radio to sleep mode is a critical technique in saving power.
The Texas instruments MSP430 family of microcontrollers (see MSP430CG4618 power parameters in Table 2) represents a competitive choice of ultralow power core to run a typical computing subsystem of a wireless sensor node. It consumes 2 mA at 8 MHz and 3.0 V and consumes a few microamps of current in low power sleep mode. This corresponds to an energy consumption of roughly 750 pJ per instruction. Therefore, a current larger than 30 mA and a supply voltage of at least 3 V is enough to support the routine work of a typical wireless sensor node. Power consumption can be minimised by optimising the relative amount of time spent in low-power sleep mode and reducing the active mode time. That is, wireless sensor nodes spend most of their time in sleep mode. The only part of the system that stays awake is the real time clock (RTC) and is responsible for keeping the time and waking up the wireless sensor node to measure a sensor input. A fast processing core enables the microprocessing unit to execute the control algorithm very quickly, enabling a rapid return to low-power sleep mode and thereby minimizing the power-hungry area under the current consumption curve. The power consumption of commercial wireless sensor nodes is shown in Table 4. With a well managed power control management, an ideal wireless sensor node has a power consumption of about 100 μW for a life time operation [3, 5, 21, 22].
2.2. Energy Sources for WSNs
At present, batteries still dominate energy source for low power electronics in general. Typical characteristics of Li-ion and thinfilm batteries are shown in Table 5. The values for supercapacitor are given for comparison.
Batteries, particularly Li-ion and thin films variants, are considerably a cheap and convenient and the best solution available in terms of energy density. Over the past two decades, research and development in battery technology has resulted in an increased battery energy density by a factor of three. Still, battery technology has evolved very slowly compared to electronic technology. For example, while computer disk storage density has increased over 1,200 times since 1990, battery’s energy density has increased only about 3 times . On average, the computational or processing power doubles every 2 years while battery capacity doubles every 10 years. Thus, it is battery life that can forestall the deployment and lifespan of WSNs and embedded systems. Replacement, recharging, and disposal of batteries present costly challenges [23, 24]. Furthermore, the size of the batteries is often larger compared to the devices they are meant to power while at the same time reducing the battery dimensions compromises the power density. For these reasons, alternative solutions to batteries need to be sought, and ambient energy harvesting devices are the potential alternatives.
2.2.2. Ambient Energy Sources
To provide a reliable source of energy for a wireless sensor system, one can consider extracting energy from the environment in order to complement the battery energy storage or even replace it. The process by which energy from the physical environment is captured and converted into usable electrical energy is called energy harvesting. Table 6 shows some of the potential ambient sources, their corresponding energy densities, and some of the current challenges associated with each source . The most common, for which promising results have already been achieved, is the extraction of power from the following sources:(i)light energy: captured from sunlight or room light via photo sensors, or solar panels,(ii)mechanical vibrations: from sources such as car engine compartment, trains, ships, helicopters, bridges, floors (offices, train stations, nightclubs), speakers, window panes, walls, household appliances (fridges, washing machines, microwave ovens), pumps, motors, compressors, chillers, conveyors). Table 7 shows the characteristics of some of the vibration sources,(iii)thermal energy from furnaces, domestic radiators, human skin, vehicle exhausts, and friction sources,(iv)radio frequency: microwaves, infrared, cell phones, and high power line emissions.
From Table 6, mechanical vibrations have a sufficiently high energy density and may potentially outperform solar harvesting systems in applications where embedded wireless sensor nodes are deployed indoors or overcast areas such as buildings, and forestry terrains, where access to direct sunlight is often not available, solar energy source may not be a suitable choice. In addition, vibrations are an attractive choice because they are one of the most prevalent sources of energy as they represent much of the “mechanical” category of energy source found in the environment. Mechanical vibrations are an energy source that is easily accessible through MEMS technology and is ubiquitous in applications at microscale level [2, 6, 26]. There are three main mechanisms by which vibrations can be converted into electrical energy: electromagnetic, electrostatic, and piezoelectric. Among them, piezoelectric vibration-to-electricity converters have received much attention, as they have high electromechanical coupling and no external voltage source requirement, and they are particularly attractive for use in MEMS [26–28]. Energy harvesting using piezoelectric materials allows for a device that is self-contained, that is, does not require any external supporting accruements. Furthermore, piezoelectric energy harvesting devices have a minimum of moving parts and are capable of generating power with voltage levels that can be easily conditioned (e.g., converted to DC or boosted) [2, 26, 29].
3. Piezoelectric Energy Harvesting from Vibrations
3.1. Piezoelectric Materials
Piezoelectricity stems from the Greek word “piezo” for pressure and the word “electric” for electricity. When a force or stress is applied to a piezoelectric material, it leads to an electric charge being induced across the material. This is known as the direct piezoelectric effect. Conversely, the application of a charge or electric field to the same material will result in a change in strain or mechanical deformation. This is known as the indirect piezoelectric effect. It is the direct piezoelectric effect that is employed in energy harvesting. Examples of ceramics which exhibit the piezoelectric effect are lead-zirconate-titanate (PZT), lead-titanate (PbTiO2), lead-zirconate (PbZrO3), and barium-titanate (BaTiO3). To date, the most commonly used piezoelectric ceramic is PZT mainly because it has very high electromechanical coupling ability. However, PZT is an extremely brittle material and hence this presents limitations to the strain that it can safely withstand without being damaged [26, 27]. Polyvinylidenefluoride (PVDF) is another common piezoelectric polymer which is more flexible and can be employed in energy harvesting applications .
Research in nanoscience has outputted novel piezoelectric material systems used to fabricate next generation nanogenerators employed in energy harvesting technology. The notable work of Wang and Song introduced piezoelectric nanogeneration using a single zinc oxide (ZnO) nanowire by atomic microscopy . Since then, piezoelectric semiconductor materials such as ZnO [31–33], indium nitride (InN) [34, 35] gallium nitride (GaN) , and zinc sulphide (ZnS) , and piezoelectric insulator materials, such as PVDF , BaTiO3  and PZT , have been studied for potential electrical power generation.
3.2. Piezoelectric MEMS Generator System
In piezoelectric energy harvesting from vibration, a mass is suspended by a beam, with a piezoelectric layer on top of the beam. When the mass vibrates, the piezoelectric lever is mechanically deformed and a voltage is generated. The most common energy harvesting systems are cantilever structures that are mainly designed to operate at their resonance frequencies. Such structures (unimorph or bimorph cantilevers) are popular because they enable relatively high stress levels on the piezoelectric material while minimizing the dimensions of the devices [1, 2, 26, 28]. Figure 3 shows such a system composed of a piezoelectric patch which is bonded to the host cantilever beam surface, which is under alternating deformation. When the beam is excited by mechanical vibration in the host structure, a large strain is induced in the piezoelectric material and an alternating voltage (AC) is generated between the electrodes. The AC voltage is then conditioned by interface circuits for proper delivery of the harvested energy to a storage element or compatibility with load specifications. Nonlinear interface circuits are the latest state of the art conditioning circuits employed to enhance the output power . Figure 4 shows a prototype device made at Clarkson University . A summary of MEMS energy harvesters is given in Table 8.
3.3. Piezoelectric Nanogenerator System
The ground breaking work by Zhong L. Wang and his Nano Research Group at the Georgia Institute of Technology, USA, has greatly influenced the current research efforts in the conversion of nanoscale mechanical energy into usable electrical energy using nanogenerators. In their original paper Wang and Song first introduced piezoelectric nanogeneration by examining the piezoelectric properties of a single ZnO nanowire (NW) by atomic force microscopy in 2006 .
The insulating properties of piezoelectric insulator materials do not permit carrier transport from metal electrodes into the insulating active materials. As a result, the nanogenerators fabricated from these materials produce alternating (AC) power. On the other hand, the power generation mechanisms of nanogenerators fabricated from piezoelectric semiconductor materials produce both AC and direct power (DC). The coupled semiconducting and piezoelectric properties are in essence responsible for the DC and AC power, respectively. When piezoelectric semiconducting nanowires are subjected to an external force perpendicular to the nanowires, a piezoelectric potential is generated along the nanowires owing to the relative displacement of cations with respect to anion under uniaxial strain . DC power from the nanogenerators is attributed to the force exerted perpendicular to the axis of superconducting nanowires; as a result, the nanowires bend laterally . The generation of direct current (DC) from piezoelectric semiconductor nanomaterials is attributed to the force exerted perpendicular to the axis of the semiconducting nanowire—typically by subjecting a vertically grown nanowire to the laterally moving tip. When a nanowire is subjected to an external force by the moving tip, deformation occurs throughout the nanowire and an electric field is created inside the nanostructure due to the piezoelectric effect. A piezoelectric potential is generated along the width of NW owing to the relative displacement of the cations with respect to anions. The stretched part with the positive strain will exhibit a positive electrical potential, whereas the compressed part with the negative strain will show a negative electrical potential. As a result, the tip of the NW will have an electrical potential distribution on its surface, whereas the bottom of the NW is neutralized because it is grounded. The maximum voltage generated in the NW can be calculated using the following equation [44, 45]: where is the permittivity in a vacuum, is the dielectric constant, , , and are the piezoelectric coefficients, is the Poisson ratio, is the radius of the NW, is the length of the NW and is the maximum deflection of the NW’s tip.
The electrical contact plays a crucial in role pumping out charges on the surface of the tips. The effective Schottky contact must be formed between the counter electrode and the tip of the NW because the Ohmic contact will neutralize the electrical field generated at the tips. Owing to formation of the Schottky contact, the electrons will pass to the counter electrode from the surface of the tip when the counter electrode is in contact with the region of the negative potential and the current will be measured, whereas no current will be generated when it is in contact with the regions of a positive potential, resulting in the generation of the DC output . This mechanism suggests that the rectifying characteristic of the Schottky barrier formed between the metal tip and the NW leads to generation of a DC voltage, the principle is shown in Figure 5. Wang et al.  introduced the first ZnO NW-based DC power nanogenerator that was driven by an ultrasonic wave with a frequency of 41 kHz. They used zigzag trenches as the top electrode to replace the atomic force microscopy (AFM) tip. This work formed the basic platform for optimizing and improving the performance of the nanogenerators by integrating them into layered structures. Since then, several vertical NWs integrated nanogenerators and lateral NWs-integrated nanogenerators were fabricated by integrating them into layered structures to improve their performance [46, 63–67].
3.4. Comparison of Conventional Microgenerators and Nanogenerators
A simple nanogenerator is principally a nanowire which is a one-dimensional nanomaterial that has a typical diameter less than 100 nm and a length of 1 μm (see Figures 5, 6 and 7.) The majority of ceramic nanowires are in fact single crystal materials. Compared to the conventional ceramic/thin film-based piezoelectric cantilever energy harvesting devices, nanogenerators offer three distinct advantages as reported by Wang  as follows.(1)Enhanced Piezoelectric Effect. When a strain gradient is experienced by a ferroelectric nanowire, 400–500% enhancement of the piezoelectric effect can be achieved.(2)Superior Mechanical Properties. The lattice perfection of nanowires enables much larger critical strain, higher flexibility, and longer operational lifetime [42, 47]-compared to conventional ceramic microgenerators [44, 48].(3)High Sensitivity to Small Forces. Large aspect ratio and small thickness allow the creation of significant strain in the nanowires under a force at the nanonewton or piconewton level.
4. Energy Harvesting and WSN Operation: Challenges and Opportunities
4.1. Energy Harvesting and Wireless Sensor Nodes Operation
Ambient mechanical vibrations are harvested and converted to useful electrical energy which is either stored in a storage element or is supplied directly to the load. Energy storage is a key element of the energy harvesting system because it is a bridge of stability between the energy source and the load that provides a constant energy flow from an otherwise variable environmental source. The power interface circuits condition the harvested energy to enable the charging of low capacitor batteries or supercapacitors and also provide compatibility with the load requirements. For a sensor node fully powered by ambient energy, the generated mean power () must be greater than or equal to the mean consumed power () : As noted earlier in Section 2.1, the power consumed by a wireless sensor node is typically a few tens to hundreds of milliwatts. Compared to the power output of MEMS piezoelectric energy harvesters such as shown in Table 8 and nanogenerators in Table 9 which have a range of a microwatt to tens of microwatts, it is quite evident that energy harvesting is not able to power the sensor node continuously.
The key question is how can the power consumption of the wireless sensor node be reduced so that energy harvesting can handle the supply requirements? The answer to the question is practically realized by what is called duty cycling, which allows the sensor to operate in an intermittent regime instead of a continuous form. In the duty cycling approach, wireless sensor nodes are designed to operate in a very low duty cycle (D), with moderate power consumption in active mode , and very low power consumption while in sleep (or idle) mode, (). The intermittent operation of the sensor node is designed in such a way that the monitoring process of the WSN is not compromised . The average power consumed by the sensor node is given by (2) From (4) and (5) it can be observed that if the duty-cycle (D) is made small, the sensor node is put to the sleep mode most of the time and it is activated to perform sensing and communication when needed. This results in a great reduction in the average power consumption of the wireless sensor node. Thus, for a given application requirement and characteristic of energy harvesting source, careful choice of the appropriate duty cycle is critical in designing power management algorithms. Ambient energy source like vibrations are variable in nature and there are times and situations when . To address such a situation, a storage element such as a supercapacitor or a thin film battery is needed. For any arbitrary long period of time, T, a long-term storage element must be designed to fulfill the condition of 
Since WSNs operate on a strict power budget, ultralow power microcontroller units (MCUs) are required for processing and power management. A typical MCU like the Texas instruments MSP430 is ideal for energy harvesting since it has a low standby current of less than 1 μA and low active current 160 μA/MHz, and quick wakeup time of less than 1 μs, and operate on the range 1.8 V to 3.6 V . The development of advanced power management techniques, together with ultra-low power MCU and novel storage elements, is likely to result in self-sustained embedded and wireless sensor networks.
4.2. Duty Cycling and Advanced Power Management Techniques
As discussed earlier in sections, most embedded sensor systems support sleeping modes, making a direct approach to duty cycling an attractive choice to power management. This direct implementation of the duty cycling technique, though popular in energy harvesting for wireless sensor networks, is not always the best choice [51–54]. Other than being a too simplistic approach, a fixed duty cycle implies that if the energy source is supplying more energy than is being consumed, the system will waste excess energy once the storage reservoir is fully charged. To get over some of these challenges, advanced power management techniques with strict hardware specifications have been proposed. These techniques are dynamic voltage scaling (DVS), dynamic frequency scaling (DFS), and a combination of DVS and DFS.
The operation principle of DVS technique is that increasing a circuit’s voltage allows it to switch faster, but with an increase in energy consumption, and conversely the decrease in circuit voltage causes the circuit to have a low switching time with an accompanied decrease in energy consumption [55, 56]. A similar phenomenon is observed when the clock frequency is increased or decreased and is the basic premise of the DFS technique. Unlike duty cycling where all tasks stop during the sleeping mode, the advanced power management techniques allow the system to keep running at a low pace and with reduced energy consumption, without compromising the execution of important tasks [1, 56, 57]. Besides DBS, DFS, and duty cycling, the maximum power point tracking (MPPT) technique may be applied in the energy harvesting system. In the MPPT technique, the object is to transfer maximum power to the load. This technique is traditionally used in solar systems which have a dynamic voltage-current characteristic where the optimal load for maximum power depends on the operating point. The technique requires constant monitoring of incoming energy, to determine the optimal operating point, and an adaptive load.
4.3. Fundamental Material Issues
The performance of a piezoelectric energy harvesting systems primarily depends on the piezoelectric properties used to fabricate the generators. Generally, thin film piezoelectric materials show better piezoelectric properties compared to bulk piezoelectric materials. The use of single crystals and nanomaterials (nanowires) has, in principle, improved the power density and energy conversion efficiency hence the advance the miniaturization of device size while maintaining a reasonable power output. Despite great research efforts on these nanomaterials, there is lack of fundamental scientific understanding of and experimental research on piezoelectric and flexoelectric effects in single crystalline nanowires. This lag in research at this fundamental level compromises fidelity of the mathematical algorithms used in modeling and predicting the piezoelectric potential, mechanical to electrical energy conversion efficiency and device material optimization.
The other challenge relates to the coupling of piezoelectric and semiconducting effect—resulting in the so-called piezotronic effect. The scientific understanding of the interaction of electron distribution and semiconductor band structures requires additional research efforts. The research will potentially present an opportunity to facilitate in situ rectification of the potential output by making use of the Schottky barrier formed between ZnO and metal electrodes. While single crystal materials offer better piezoelectric performance and give better power density compared to their bulk material counterparts, costs of these materials are still very high and at times very inhibitive. The current fabrication methods and the associated device integration techniques at nanoscale are not yet suited for large scale processing, and research efforts along this line will substantially reduce fabrication costs and help translate piezoelectric energy harvesting from mere experimental curiosity into real engineered device realisations to power wireless sensors.
4.4. Design and Power Management Issues
The design of piezoelectric micropower generators and nanogenerators is in itself a multidisciplinary area with challenges based in fundamental physics, material science, mechanical engineering, and electrical engineering. Different researchers from different discipline and background have reported several researches in the area of piezoelectric energy harvesting. The multidisciplinary approach and a holistic paradigm is perhaps the most promising way of designing piezoelectric energy harvesting device.
As can be observed from the review, there is still a need to improve the power output of piezoelectric generators to match the requirements of wireless sensor devices. This challenge can be addressed by using piezoelectric material with the best piezoelectric properties, the best device geometries, and the best power electronics to condition and manage the power output. This is arguably calls for a holistic design and optimization regime, together with an established international metrology standard of piezoelectric energy harvesting (which currently does not exist). Latest advances in synchronised switching techniques have been reported as the latest achievements in power conditioning interface circuits to date [29, 48]. Further research and intergation of efficient interface circuits, advances in power conditioning power management techniques and development in ultralow power wireless microcontroller units will greatly drive energy harvesting technology to heights never envisaged before.
Vibration energy harvesting using piezoelectric generators was discussed and its potential as an alternative energy source for wireless sensor devices overviewed. The maturity of piezoelectric energy harvesting as technology entails that WSNs are energy efficient and their dependence on batteries is limited. With advancement in ultralow microelectronics and ultra-low power wireless microcontroller units, power consumption of sensor nodes is getting lower and hence the harvested ambient energy may be sufficient to eliminate batteries completely. In addition, piezoelectric nanogenerators open new avenues for ambient power harvesting through foldable power options and miniaturization of power packages thus enabling implantable medical sensing capabilities. Energy harvesting using piezoelectric generators is an attractive alternative energy source that has the potential to provide energy autonomy to wireless sensor devices.
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