Energy Harvesting Technologies

This chapter provides the introductory information on piezoelectric energy harvesting covering various aspects such as modeling, selection of materials, vibration harvesting device design using bulk and MEMS approach, and energy harvesting circuits. All these characteristics are illustrated through selective examples. A simple step-by-step procedure is presented to design the cantilever beam based energy harvester by incorporating piezoelectric material at maximum stress points in first and second resonance modes. Suitable piezoelectric material for vibration energy harvesting is characterized by the large magnitude of product of the piezoelectric voltage constant (g) and the piezoelectric strain constant (d) given as (d· g). The condition for obtaining large magnitude of d·g has been shown to be as |d| =εⁿ, where ε is the permittivity of the material and n is a material parameter having lower limit of 0.5. The material can be in the form of polycrystalline ceramics, textured ceramics, thin films, and polymers. A brief coverage of various material systems is provided in all these categories. Using these materials different transducer structures can be fabricated depending upon the desired frequency and vibration amplitude such as multilayer, MFC, bimorph, amplified piezoelectric actuator, QuickPack, rainbow, cymbal, and moonie. The concept of multimodal energy harvesting is introduced at the end of the chapter. This concept provides the opportunity for further enhancement of power density by combining two different energy-harvesting schemes in one system such that one assists the other.

This chapter investigates electromechanical modeling of cantilevered piezoelectric energy harvesters excited by persistent base motions. The modeling approaches are divided here into two sections as lumped parameter modeling and distributed parameter modeling. The first section discusses the amplitude-wise correction of the existing lumped parameter piezoelectric energy harvester model for base excitation. For cantilevers operating in the transverse and longitudinal vibration modes, it is shown that the conventional base excitation expression used in the existing lumped parameter models may yield highly inaccurate results in predicting the vibration response of the structure. Dimensionless correction factors are derived to improve the predictions of the coupled lumped parameter piezoelectric energy harvester model. The second section of this chapter presents coupled distributed parameter modeling of unimorph and bimorph cantilevers under persistent base excitations for piezoelectric energy harvesting. Closed-form solutions are obtained by considering all vibration modes and the formal representation of the direct and converse piezoelectric effects. Steady state electrical and mechanical response expressions are derived for arbitrary frequency excitations. These multi-mode solutions are then reduced to single-mode solutions for excitations around the modal frequencies. Finally, the analytical expressions derived here are validated experimentally for a cantilevered bimorph with a proof mass.

This chapter summarizes several recent activities for fundamental understanding of piezoelectric vibration-based energy harvesting. The developed framework is able to predict the electrical behavior of piezoelectric power harvesting systems using either the standard or the synchronized switch harvesting on inductor (SSHI) electronic interface. In addition, some opportunities for new devices and improvements in existing ones are also pointed here.

Electromechanical equivalent circuits can be used to model the dynamics of piezoelectric systems. In the following, they will be applied for the modeling of piezoelectric bending generators for energy harvesting. Therefore, the basic analogies between electrical and mechanical systems will be discussed and a simple piezoelectric equivalent circuit model for a system which can be described by a single mechanical modal coordinate will be derived. In a next step, an experimentally based method for the determination of the model parameters will be presented. The modeling of additional mechanical degrees of freedom as well as the modeling of force and kinematic base excitation will also be addressed.

This chapter focuses on the use of electromagnetic transducers for the harvesting of kinetic (vibration) energy. The chapter introduces the fundamental principals of electromagnetism and describes how the voltage is linked to the product of the flux linkage gradient and the velocity. The flux linkage gradient is largely dependent on the magnets used to produce the field, the arrangement of these magnets, and the area and number of turns for the coil. The characteristics of wire-wound and micro-fabricated coils, and the properties of typical magnetic materials, are reviewed. The scaling of electromagnetic energy harvesters and the design limitations imposed by micro-fabrication processes are discussed in detail. Electromagnetic damping is shown to be proportional to the square of the dimension and analysis shows that the decrease in electromagnetic damping with scale cannot be compensated by increasing the number of turns. For a wire wound coil, the effect of increasing coil turns on EM damping is directly cancelled by an increase in coil resistance. For a planar micro-coil increasing the number of turns results in a greater increase in the coil resistance, resulting in an overall decrease in damping. Increasing coil turns will, however, increase the induced voltage which may be desirable for practical reasons. An analysis is also presented that identifies the optimum conditions that maximise the power in the load. Finally, the chapter concludes with a comprehensive review of electromagnetic harvesters presented to date. This analysis includes a comparison of devices that confirms the theoretical comparison between conventional wound and micro-fabricated coils and the influence of device size on performance.

The modeling of an energy harvesting device consisting of a piezoelectric based stack is presented. In addition, the optimization of the power acquired from the energy harvester is considered. The harvesting device is a piezoceramic element in a stack configuration, which scavenges mechanical energy emanating from a 1D-sinusoidal-base excitation. The device is connected to a harvesting circuit, which employs an inductor and a resistive load. This circuit represents a generalization of the purely resistive circuit, which has received considerable attention in the literature. The optimization problem is formulated as a nonlinear programming problem, wherein the Karush-Kuhn-Tucker (KKT) conditions are stated and the various resulting cases are treated. One of these cases is that of a purely resistive circuit. For resistive circuits, researchers usually neglect the effect of mechanical damping in their optimization procedures. However, in this chapter, we specifically explore the role of damping and electromechanical coupling on the optimization of circuit parameters. We show that mechanical damping has a qualitative effect on the optimal circuit parameters. Further, we observe that beyond an optimal coupling coefficient, the harvested power decreases. This result challenges previously published results suggesting that larger coupling coefficients culminate in more efficient energy harvesters. As for the harvesting circuit, the addition of the inductor provides substantial improvement to the performance of the energy harvesting device. More specifically, at the optimal circuit parameters, optimal power values obtained through a purely resistive circuit at optimal excitation frequencies can be obtained at any excitation frequency. Moreover, simulations reveal that the optimal harvested power is independent of the coupling coefficient (within realistic values of the coupling coefficient); a result that supports our previous findings for a purely resistive circuit.

Breaking down the barriers of traditional sensors, MicroStrain’s energy harvesting wireless sensors eliminate long cable runs as well as battery maintenance. Combining processors with sensors, the wireless nodes can record and transmit data, use energy in an intelligent manner, and automatically change their operating modes as the application may demand. Harvesting energy from ambient motion, strain, or light, they use background recharging to maintain an energy reserve. Recent applications include piezoelectric powered damage tracking nodes for helicopters as well as solar powered strain and seismic sensor networks for bridges.

Recent progresses in both microelectronic and energy conversion fields have made the conception of truly self-powered, wireless systems no longer chimerical. Combined with the increasing demands from industries for left-behind sensors and sensor networks, such advances therefore led to an imminent technological breakthrough in terms of autonomous devices. Whereas some of such systems are commercially available, optimization of microgenerators that harvest their energy from their near environment is still an issue for giving a positive energy balance to electronic circuits that feature complex functions, or for minimizing the amount of needed active material. Many sources are available for energy harvesting (thermal, solar, and so on), but vibrations are one of the most commonly available sources and present a significant energy amount. For such a source, piezoelectric elements are very good agents for energy conversion, as they present relatively high coupling coefficient as well as high power densities. Several ways for optimization can be explored, but the two main issues concern the increase of the converted and extracted energies, and the independency of the harvested power from the load connected to the harvester.

Particularly, applying an original nonlinear treatment has been shown to be an efficient way for artificially increasing the conversion potential of piezoelectric element applied to the vibration damping problem. It is therefore possible to extend such principles to energy harvesting, allowing a significant increase in terms of extracted and harvested energy, and/or allowing a decoupling of the extraction and storage stage.

The purposes of the following developments consist in demonstrating the ability of such microgenerators to convert ambient vibrations into electrical energy in an efficient manner. As well, when designing an energy harvester for industrial application, one has to keep in mind that the microgenerator also must be self-powered itself, and needs to present a positive energy balance. Therefore, in addition to the theoretical developments and experimental validations, some technological considerations will be presented, and solutions to perform the proposed processing using a negligible part of the available energy will be proposed. Moreover, the behavior of the exposed technique under realistic vibrations will be investigated.

Many environmental and industrial monitoring scenarios require wireless instrumentation with a small form factor and a long service life, a combination that forces designers to move beyond batteries and into energy harvesting techniques. This chapter considers the average requirements of wireless sensor networks, and assesses the suitability of modern thermal, photonic, and vibration-harvesting methods to power such networks across various application spaces.

This chapter focuses on how to most efficiently transfer and condition harvested energy and power with emphasis on the imposed requirements of microscale dimensions. The driving objective is to maximize operational life by reducing all relevant power losses. The chapter therefore briefly reviewes the electrical characteristics and needs of available harvesting sources and the operational implications of relevant conditioners. It then discusses the energy and power losses associated with transferring energy and useful schemes for maximizing system efficiency. It ends by presenting a sample microelectronic harvester circuit.

Temperature gradients and heat flow are omnipresent in natural and human-made settings and offer the opportunity to harvest energy from the environment. Thermoelectric energy harvesting (or energy scavenging) may one day eliminate the need for replacing batteries in applications such as remote sensor networks or mobile devices. Particularly, attractive is the ability to generate electricity from body heat that could power medical devices or implants, personal wireless networks or other consumer devices. This chapter focuses on the design principles for thermoelectric generators in energy harvesting applications, and the various thermoelectric generators available or in development. Such design principles provide good estimates of the power that could be produced and the size and complexity of the thermoelectric generator that would be required.

This chapter highlights the design characteristics of thermoelectric generators (TEGs), electronic devices capable of harvesting power from small temperature differences. Power produced from these generators is low, typically in the microwatt to low milliwatt range. Due to the TEG’s unique nature, not only does the TEG’s electrical resistance have to be matched to the connected electrical load, the TEG also needs to be thermally matched to the attached heat sink, which is used to dissipate heat to the surrounding ambient. Proper TEG-to-heat sink thermal matching is required to produce sufficient power and voltage to continuously power small wireless sensors, switches, and other wireless devices from temperature differences as small as 5-10,°. Traditional bulk thermoelectric devices and thin film thermoelectric devices are not well suited for these low Δ T, low heat flux applications. When used with small, natural convection heat sinks, TEGs containing hundreds of thermocouples with extreme length-to-area ratios are necessary. New TEG device structures which incorporate thin, adhesive-filled gaps to separate the TEG elements are the best TEG device configuration for small Δ T energy harvesting applications.

Batteries are one solution for charge accumulation and storage of energy from harvesting and scavenging devices. Because many harvesting devices capture low levels of ambient energy, only very small batteries are required for most applications requiring energy storage and intermittent use. This chapter highlights the fabrication and performance of research batteries and recently commercialized thin film batteries (TFB) including the energy and power densities, charging requirements, cycle-life and shelf-life, and also oerformance at both high and low temperatures. With flexible charging models and excellent cycle life, thin batteries are very promising for paring with a variety of energy harvesting devices including solar cells and piezoelectrics.

Lithium-ion batteries have emerged as the choice of rechargeable power source as they offer much higher energy density than other systems. However, their performance factors such as energy density, power density, and cycle life depend on the electrode materials employed. This chapter provides an overview of the cathode and anode materials systems for lithium-ion batteries. After providing a brief introduction to the basic principles involved in lithium-ion cells, the structure-property-performance relationships of cathode materials like layered LiMO₂ (M = Mn, Co, and Ni) and their soiled solutions, spinel LiMn₂O₄, and olivine LiFePO₄ are presented. Then, a brief account of the carbon, alloy, oxide, and nanocomposite anode materials is presented.

A piezoelectric energy generator that is driven by stimulated muscle and is\break implantable into the human body is under development for use as a self-replenishing power source for implanted electronic medical devices. The generator concept includes connecting a piezoelectric stack generator in series with a muscle tendon unit. The motor nerve is electrically activated causing muscle contraction force to strain the piezoelectric material resulting in charge generation that is stored in a load capacitor. Some of the generated charge is used to power the nerve stimulations and the excess is used to power an implanted device. The generator concept is based on the hypothesis that more electrical power can be converted from stimulated muscle contractions than is needed for the stimulations, a physiological phenomenon that to our knowledge has not previously been utilized. Such a generator is a potential solution\break to some of the limitations of power systems currently used with implanted devices.

This chapter presents an analysis of piezoelectric unimorph diaphragm energy harvesters as a potential tool for generating electrical energy for implantable biomedical devices. First the chapter discusses current and developing biomedical devices that require energy, and the need for capture of energy from the environment of the implant. Next, a general discussion of piezoelectric harvesters is presented, and a case is made for the use of 31 mode diaphragm harvesters for conversion of energy from blood pressure variations within the body. The chapter then presents derivations of available electrical energy for unimorph diaphragm harvesters, starting with general boundary conditions, and then proceeding to simply supported and clamped conditions of various piezoelectric and electrode coverage. Using these analytical results, the chapter ends with by presenting a brief set of numerical results illustrating the amount of power that could be harvested for a particular size of device, and how that power may be used as a source for a given implanted medical device. In summary, it is shown that the harvester could potencially provide enough power to operate a 10 mW device at reasonable intermittent rates. The relationships provided here may enable other optimal designs to be realized, for medical or for many other applications.

Over the past few decades the use of portable and wearable electronics has grown steadily. These devices are becoming increasingly more powerful; however, the gains that have been made in the device performance have resulted in the need for significantly higher power to operate the electronics. This issue has been further complicated due to the stagnate growth of battery technology over the past decade. In order to increase the life of these electronics, researchers have begun investigating methods of generating energy from ambient sources such that the life of the electronics can be prolonged. Recent developments in the field have led to the design of a number of mechanisms that can be used to generate electrical energy, from a variety of sources including thermal, solar, strain, inertia, etc. Many of these energy sources are available for use with humans, but their use must be carefully considered such that parasitic effects that could disrupt the user’s gait or endurance are avoided. These issues have arisen from previous attempts to integrate power harvesting mechanisms into a shoe such that the energy released during a heal strike could be harvested. This chapter will present research into a novel energy harvesting backpack that can generate electrical energy from the differential forces between the wearer and the pack. The goal of this system is to make the energy harvesting device transparent to the wearer such that his or her endurance and dexterity is not compromised, therefore to preserve the performance of the backpack and user, the design of the pack will be held as close to existing systems as possible.

This chapter highlights the importance and significance of energy harvesting in applications involving use of active RF sensors and ID tags. The chapter begins by providing a basic overview on radio frequency identification (RFID) operation, various types of RFID tags, and the need for energy harvesting, especially for active RFID tags. Unlike passive tags, active tags utilize a battery to emit rather than reflect or backscatter RF energy. Advantages of active tags include improved range and read rate in electromagnetically unfriendly environments and improved link quality. Typical applications include monitoring enterprise/supply chain assets (e.g. laptops, computers, peripherals, electronic equipment, pallets, inventory items, etc.), personnel, patients, vehicles, and containers. Although a battery can substantially improve performance, it limits maintenance-free operational life. Therefore, harvesting energy from sources such as vibration or light has been shown to address this shortcoming but these sources must be adequate, available throughout the life of the application, and highly efficient. These available energy harvesting technologies are described, and basic design procedures and components for such systems are identified. This includes three key components namely, the energy harvesting transducer, power management circuit, and energy storage device. Each component of the energy harvesting system is described and important design criteria are highlighted. Specific emphasis is placed on the design of the power management component and the available energy storage device technologies. Disadvantages of using off-the-shelf DC-DC converters and rectifiers are emphasized and possible power management solutions for solar and vibrational energy harvesting are explained. Finally, the chapter concludes by describing the future directions and scope including development of integrated multiple source energy harvesting systems on thin-film substrates.

The concept of wireless sensor nodes and sensor networks has been widely investigated for various applications, including the field of structural health monitoring (SHM). However, the ability to power sensors, on board processing, and telemetry components is a significant challenge in many applications. Several energy harvesting techniques have been proposed and studied to solve such problems. This chapter summarizes recent advances and research issues in energy harvesting relevant to the embedded wireless sensing networks, in particular SHM applications. A brief introduction of SHM is first presented and the concept of energy harvesting for embedded sensing systems is addressed with respect to various sensing modalities used for SHM and their respective power requirements. The power optimization strategies for embedded sensing networks are then summarized, followed by several example studies of energy harvesting as it has been applied to SHM embedded sensing systems. The paper concludes by defining some future research directions that are aimed at transitioning the concept of energy harvesting for embedded sensing systems from laboratory research to field-deployed engineering prototypes.