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SPIE Defense,Security &Sensing2010


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  • 1. Autonomous energy harvesting embedded sensors for border security applications Abhiman Hande*a, Pradeep Shaha, James N. Falascob, Doug Weinerb a Texas Micropower Inc, 7920 Belt Line Rd, Suite 1005, Dallas, TX, USA 75254; b Crane Wireless Monitoring Solutions, 3301 Essex Drive, Richardson, TX USA 75082 ABSTRACT Wireless networks of seismic sensors have proven to be a valuable tool for providing security forces with intrusion alerts even in densely forested areas. The cost of replenishing the power source is one of the primary obstacles preventing the widespread use of wireless sensors for passive barrier protection. This paper focuses on making use of energy from multiple sources to power these sensors. A system comprising of Texas Micropower’s (TMP’s) energy harvesting device and Crane Wireless Monitoring Solutions’ sensor nodes is described. The energy harvesters are suitable for integration and for low cost, high volume production. The harvesters are used for powering sensors in Crane’s wireless hub and spoke type sensor network. TMP’s energy harvesting methodology is based on adaptive power management circuits that allow harvesting from multiple sources making them suitable for underground sensing/monitoring applications. The combined self-powered energy harvesting solutions are expected to be suitable for broad range of defense and industry applications. Preliminary results have indicated good feasibility to use a single power management solution that allows multi-source energy harvesting making such systems practical in remote sensing applications. Keywords: Energy harvesting, vibrations, piezoelectric, multi-source, power management, wireless sensor networks, seismic sensors, border security. 1. INTRODUCTION Border incursion is a problem that is growing in scope and sophistication. Resources are limited and must be optimized, leveraging today’s most advanced technology, to protect a nation’s borders. Any law enforcement or homeland security related agency (federal, state and local) tasked with border surveillance is faced with the growing reality that individuals and groups seeking illegal entry are becoming increasingly sophisticated in their methods of operation. Multiple tunnels have even been created along the US Mexico border to enable crossing the border unnoticed. Tasked with securing our nation’s vast borders, the Department of Homeland Security’s Customs and Border Protection (CBP) is responsible for operating 327 official ports of entry and protecting more than 4,000 miles of border with Canada and 1,900 miles of border with Mexico. In order to offset the human and physical aspects of border surveillance and security, Unattended Ground Sensors (UGS) have helped provide 24-7 autonomous surveillance, over parts of the border area. UGS typically utilize seismic, infrared and magnetic modalities, either singularly or in combination. Seismic sensors detect ground movement (footsteps, tire roll, track slap, etc.); infrared sensors respond to the breakage of spatial planes and magnetic sensors recognize metal in passing vehicles or on a person. To be an effective solution for border security, UGS must be physically small and unobtrusive, support a long battery life, provide reliable detection and tracking with a very low false alarm rate and be easy to deploy, integrate, maintain and scale. Ultimately, a highly effective UGS border security solution must be cost- effective with low required maintenance, providing real-time actionable intelligence. Sensors have been used for years to detect motion as a border security solution, but this application is evolving to utilize new technologies that provide a more comprehensive picture of the border, such as Unmanned Aerial Vehicles (UAVs) along with enhanced ground surveillance technology. Newer, advanced UGS systems, such as the Crane WMS MicroObserver® Unattended Ground Sensor system, provide classification, tracking and velocity information on targets of interest while rejecting animals that cause nuisance alarms, all extracted from a single sensing modality with long, multi-year battery lifetimes. * ahande@texasmicropower.com; phone 1 972 804-3502; fax 1 972 985-1290; texasmicropower.com
  • 2. Border security, however, requires striking a balance between personnel and force multiplication tools such as infrastructure, technology tools and air assets. Utilizing wirelessly networked UGS systems for detection and classification, in conjunction with UAVs for mobile surveillance and relay communications, allows the sensors to be more valuable in the interdiction and apprehension of illegal entries into the United States and to be operated with fewer personnel. Through the effective utilization of advanced wireless networked UGS technology and energy harvesting techniques, larger geographic areas can be protected at reasonable costs with almost no maintenance for years, providing covert awareness of cross-border activity at the lowest total cost of ownership. While structured deployment of wireless sensor networks with careful placement of nodes and pre-configured topologies is a possibility in some applications, larger scale border protection applications are better suited to ad-hoc deployments, as much of the deployment takes place in remote or hostile environments and pre-configuration is costly and not practical. It would therefore seem that a totally Ad-Hoc networking architecture, using an energy harvesting sensor, is a natural choice for border protection, with no-priori infrastructure requirements, self-organizing capability, quick deployment capability, highly scalable nature and low maintenance requirements. However, an UGS network for border protection is more demanding than other sensor networks, such as home automation where latency is not critical and the information rate within the overall system is extremely low. The border protection application is data-centric versus control-centric, which is common in most industrial, machine or habitat monitoring networks. One of the most important tasks in a border sensor network is information extraction. Due to finite energy resources, this data gathering process must be energy-efficient in order to extend the lifetime of the network. A fundamental issue in these tactical wireless sensor networks is the coupling of the distributed nature of parameter measurement with the need for timely data fusion combined with finite energy resources, which greatly impacts practical Ad-Hoc topologies. In this paper, we focus on describing feasibility of energy harvesting (EH) for powering remote sensors in an UGS network from multiple sources to increase remote sensor lifetime. Multi-source EH is important because any one source might not have adequate energy density to cater to the power requirements of the sensors. While significant progress has been made in improving EH transducer efficiency, limited research has been done with respect to improving efficiency and the power consumption of the power management circuits utilized in the energy conversion process significantly reducing the impact on overall available energy. The result is either an impractical harvester design and/or overly sized transducers to compensate for the power management circuit inefficiencies primarily faced by the large power consumption in existing circuits and discrete implementations. Consequently, system form factors are large and costs are high, making energy harvesters in several applications impractical. Also, most current solutions focus on EH from a single source with power management focused on maximizing energy harvested from that specific source. This paper addresses the above problem by using an EH system that is capable of harvesting energy from multiple sources with intelligent power management. This coupling provides the potential for maximum power harvesting and efficient energy delivery under naturally occurring continuously varying environmental conditions that affect the harvesting transducers and the time varying load demands. Multi-source EH is extremely important to prolong sensor life especially if applications are interrupt-driven and require critical sensor data to be relayed from time to time. We evaluate such EH devices for UGS for border security applications and determine feasibility. 2. ENERGY HARVESTING WIRELESS SENSOR NETWORK FRAMEWORK In order for energy harvesting techniques to be fully utilized we must first look at different network topologies to determine which most effectively manage energy while maintaining demanding performance capabilities required in a border security application. Mesh network nodes generally allow transmission only to one’s nearest neighbors as shown in Figure 1a. Normally, there are multiple routing paths between nodes, making this topology robust to failure of individual nodes. An advantage of mesh networks is that, although all nodes may be identical and have the same computing and transmission capabilities, certain nodes can be designated as aggregation nodes performing additional functions such as data fusion. If an aggregation node fails, another node may take over these functions. The propagation of sensor data through the mesh allows a sensor network to be extended, in theory to an unlimited range. However, this limits powering down radios on nodes, as it renders such nodes unavailable for multi-hop communications. The dynamic management of complex routing information, including information about gateways to external networks, is arguably the biggest challenge for mesh networks. Mesh networks are not extremely practical where power or latency is a critical
  • 3. issue, but are useful when the ability to expand the distance of the network is extremely important. Figure 1. Network topologies. The star topology (Figure 1b) delivers the lowest overall power consumption but is limited by the transmission distance of the radio in each sensor endpoint. This typically limits the range of the sensor field to about a 100-meter radius. A disadvantage is that no alternate communication routes between the endpoints exist; should a path become obstructed, information from the associated sensor would be lost. A hub and spoke topology (Figure 1c) is the superior choice for a sensor network that will implement data fusion while taking advantage of the significant energy gain due to data aggregation. Unlike a mesh network, there is an orderly and predictable flow of data through the network. Data is naturally aggregated at the hubs, which serve as a convenient point for data fusion. Data fusion has the effect of limiting the data flow through the mesh section of the network, requiring lower bandwidth, fewer transmissions and decreased probability of detection by hostile forces and the potential for lower power usage. Unlike the star topology, a reasonable level of range extension can be realized without a significant reduction in the overall transmitted bandwidth and without a considerable increase in total energy usage. 3. SENSOR POWER USAGE Even with a suitable energy efficient topology chosen, close attention must be paid to the sensor design to minimize energy usage. An energy harvesting powered sensor consists of five elements (Figure 2): • Environmental Transducer – detection of environmental parameters • Energy Harvesting Transducer – conversion of ambient energy • Power Module – collect, store and deliver energy • MCU – signal analysis • Radio Link – exfiltration of data Figure 2. Sensor components.
  • 4. With a well managed network topology, the radio link’s energy usage is tightly optimized for power usage and is not typically the main contributor to power consumption. Due to the detection range and latency requirements placed on detection of intrusions, the environmental transducer and its associated signal analysis in the MCU constitute the main power draw. Sensors in today’s most advanced Unattended Ground Sensor systems for personnel and vehicle detection, even those designed for optimized power usage have an average power draw sufficient to require an energy harvester that is not covert. To practically enable energy harvesting even to supplement batteries, sensor power budgets will need to be on the order of 5 to 15 mW or less average power, without sacrificing performance. Ongoing work shows this is achievable in the very near future. Unique sensing techniques and algorithms are taking advantage of the latest improvements in low power MCU architectures. Through more tightly coupled timing of processing requirements to the hardware, the MCU can be utilized in its lowest power mode during a majority of the sensing time, thus eliminating the need for transducer duty cycling or “sleeping” the sensor inputs, which is unacceptable in a border protection scenario. 4. ENERGY HARVESTING FOR SENSOR NODES The issue of powering the sensors in a UGS network becomes critical when one considers the prohibitive cost of wiring power to them or replacing their batteries. Obviously, such devices have to be small in size so that they can be conveniently placed in remote locations and enable covert emplacement. This places a severe restriction on their life if alkaline or similar batteries are used to power them. To make matters worse, battery technology has not sufficiently improved in terms of energy density and size over the last decade, especially for low power mobile applications such as sensor networks. While an effort is being made to improve the energy density of batteries, additional energy resources need to be investigated to increase the life of these devices. There are several sources of energy that can be used to power remote sensors. Table 1 compares the power generation potential of some of the typical EH modalities which include ambient radiation1, temperature gradients2, light3 and vibrations4,5,6. Among these sources of energy, solar EH through photo-voltaic conversion and vibration energy through piezoelectric elements provide relatively higher power densities. The energy harvested from any one source is of the order of a few hundred microwatts using a practical transducer. Therefore, technologists seek to combine multiple sources in order to boost the harvesting capability. However, this requires efficient power management (PM) circuit design and possibly a single PM solution to minimize size and cost. Consequently, this paper explores the feasibility of using piezoelectric vibration and solar EH for powering UGS network sensors for border security applications. Table 1. Power densities of energy harvesting technologies.   Energy Scavenging Power Density Information Source Source (µW/cm3) Solar (Outdoors) 15,000 – Direct Sun Commonly Available 150 – Cloudy Day Solar (Indoors) 6 – Office Desk Experiments Vibrations 100 - 200 Roundy et. al. Acoustic Noise 0.003 @ 75 dB Theory 0.96 @ 100 dB Daily Temp. Variation 10 Theory Temp. Gradient 15 @ 10o Celsius Stordeur & Stark 1997 Piezo Shoe Inserts 330 Starner 1996 There are three key components for successful development and commercialization of a practical EH sensor. The first component involves design of high efficiency transducers. The second involves design of high efficiency power management, and the third component is the energy storage device that must have low leakage, cost, and form factor and high cycle life. Texas Micropower, Inc. (TMP) is developing thin-film MEMS piezoelectric cantilevers based on a new composition that has much higher energy density as compared to contemporary compositions. The design for integrating multiple EH transducers involves complex tradeoffs due to the interaction of several factors such as the characteristics of the energy sources, power supply requirements and power management features of the embedded system, and application behavior. It is, therefore, essential to thoroughly understand and judiciously exploit these factors in order to maximize energy efficiency of the harvesting modules. However, as will be explained later, there is a method to design smart power management circuits that can adjust their operation to maximize power harvesting and consequently, system efficiency.
  • 5. The key technical challenges of such a system requires development of a single power management solution that can adapt to different transducer characteristics for maximum energy transfer (e.g. different piezoelectric transducers will have different source capacitance, and therefore, the power management must tune itself to match the source impedance). The multi-source EH solution will require integration of power management circuits designed for EH from each natural source (e.g. integration of solar maximum power tracking circuits with piezoelectric impedance matching circuits). This power module must be capable of adapting to the characteristics of the EH source characteristics (e.g. light intensity, frequency of vibration, etc.). 4.1 Energy harvesting from vibrations Mechanical energy can be converted to electrical energy using piezoelectric, electromagnetic, or electrostatic mechanisms. However, the mechanical-to-electrical energy conversion using the piezoelectric effect provides a smaller, lighter, and more efficient method to harvest vibration energy. Piezoelectric converters have been shown to possess three times higher energy density as compared to the other converter types7. The generated power is proportional to the square of the voltage, (P ∝ V2), thus further improvements in the energy density can be obtained by enhancing the output voltage (Voc) magnitude. The primary factors for choosing a piezoelectric material for EH are the piezoelectric strain constant (d) and the piezoelectric voltage constant (g). At sufficiently low frequencies, a piezoelectric sensor can be modeled as a parallel plate capacitor. The electric energy available under ac stress excitation from a parallel plate capacitor is: 1 U = CV 2 (1) 2 or u = 1 2 (d .g ) ⋅ ( X ) 2 (2) where: U = energy, C = capacitance, V = voltage, X = stress, and u = energy density8. This equation illustrates the importance of d and g constants in fabricating piezoelectric harvesting devices. A piezoelectric material with high energy density is characterized by a large product of piezoelectric voltage constant (g) and piezoelectric stress constant (d), given as (d.g), and a high magnitude of g coefficient. The ideal material should therefore combine the highest possible product of the (d.g) and the highest possible g coefficient for EH applications. TMP has exclusive license to a composition that has the highest reported (d.g product) as shown in Figure 3 that compares the composition to all the other commercially available PZT-based sensor/actuator ceramic materials. The composition, developed at the University of Texas at Arlington (UTA), combines the giant magnitude of the piezoelectric voltage coefficient that is comparable to that of piezoelectric polymer, and a giant magnitude of (d.g) product that is comparable to that of relaxor-based single crystals. In the end, this composition has the highest figure of merit compared to many commercial PZT compositions. This material is currently being developed in thin-film form that is suitable for EH device and system integration9. 18000 TMP/UTA Morgan DongIl 15000 Ferroperm APC 12000 APC Fuji EDO Channel DongIl Ferroperm EDO 9000 Morgan Channel 6000 10 20 30 40 50 60 Voltage Sensitivity g33 (x 10-3 Vm/N or m2/C) Figure 3. The newly developed composition (UTA) has significantly higher (d.g) product than many commercial compositions.
  • 6. A typical system for implementing vibration energy harvesting from a piezoelectric element is shown in Figure 4. In order to obtain maximum power transfer, the input impedance of the converter (Zin) should match that of the source (Zo, reflected at the output of the rectifier). The transducer impedance (Zo) is given by 1 Z = (3) 0 4 f SourceC Source where fSource is the frequency of a piezoelectric cantilever based vibration transducer, and C is the transducer series capacitance. Therefore, as the frequency decreases, the impedance increases and vice versa. The rectifier output voltage, (Vrect) across Cs determines the amount of power delivered to the converter, and consequently, the load. The power delivered to the converter is, V2 Z P = rect ≅V 2 O (4) 0 Z in Speak (Z O + Z in )2 From Figure 4 and equation 3, it can be seen that Zin must equal to Zo to obtain maximum Vrect which is approximately half of the peak transducer voltage (VSpeak). Switching converter (buck or buck-boost) or Switched capacitor converter Piezoelectric generator C Rs DC-DC Converter Cs Secondary Vs battery (Thin film, lithium, NiMH, etc.) Schottky diodes or Synchronous rectifier Figure 4. EH circuit for piezoelectric converters. Figure 5 shows how a switch mode buck-boost converter implements the required impedance matching. Any non- isolated switch mode DC-DC converter such as a buck, boost, buck-boost, or boost-buck topology may be employed as the converter. It is, however, important to fix the converter’s operation in the discontinuous conduction mode (DCM) for maximum power harvesting. DCM mode is preferred over continuous conduction mode (CCM), because the former avoids the reverse recovery problem of the diode10. Also, for certain topologies such as buck-boost operating in DCM, the average input impedance Zin does not depend upon the output energy storage device voltage VB1 and this simplifies controller design. Buck converters, however, require a high input to output voltage differential to operate. The optimal converter duty cycle depends on output filter inductance L, and the switching frequency fsw. Figure 5. DC-DC buck-boost converter with battery load.
  • 7. Z0 is required for designing the DC-DC converter with average input impedance (Zin) equal to Z0 for maximum power transfer. It can be clearly observed that different transducers will have different capacitances which will result in different transducer impedances. Similar observations can be made for transducers operating at different source frequencies. Therefore, it is important that the converter can predict the Z0 so that it automatically adjusts its Zin. 4.2 EH from solar energy Figure 6 shows the typical EH solution for harvesting solar energy where a DC-DC converter maximizes the energy transfer. Switching converter (buck or buck-boost) or Switched capacitor converter DC-DC Converter Secondary battery Load (Thin film, lithium, S1 Cs NiMH, etc.) or Ultracapacitor Figure 6. EH circuit for solar cells. Figure 7 shows the V-I characteristics of a single 25-mm x 60-mm silicon photovoltaic (PV) panel under different lighting conditions. These curves show the relationship between two key parameters, the open circuit voltage (VOC) and the short circuit current (ISC), that influence energy conversion. Each parameter form the x- and y- intercepts of the V-I curve, respectively as shown in Figure 7. The curves demonstrate that a solar panel behaves as a voltage limited current source and that the short circuit current ISC is proportional to the light intensity. 100000 Full Sun (95000  lx) 10000 3300 lx 1000 Current (uA) 300 lx 100 10 1 0 1 2 3 4 5 6 Voltage (V) Figure 7. Solar panel V-I characteristics A power management scheme is required to regulate the power from what is essentially a current source transducer, and deliver it to storage devices such as rechargeable batteries or ultracapacitors. Efficient power management circuits utilize maximum power point tracking (MPPT) techniques to harvest the most energy possible from a given light intensity. For example, Figure 7 indicates that the selected panel produces a maximum power of about 200 micro-Watts at two Volts when the light intensity is about 300 lux. Analog circuits, such as the DC-DC buck-boost converter shown in Figure 8, track the optimum voltage VMPPT for different light intensities more efficiently as compared to circuits using higher
  • 8. power consumption microcontrollers11. The DC-DC buck-boost converter monitors the voltage across the input capacitor until it increases above VMPPT. At this point the MOSFET switch is turned on to route energy to the storage device. The converter turns off when the input capacitor voltage decreases below VMPPT thus maintaining the average voltage at VMPPT, which is the optimum point for maximum power transfer. Since VOC scales linearly with light intensity and VMPPT scales linearly with VOC, then measuring VOC leads to a simple estimation of VMPPT. The designers must complete this characterization for each solar cell prior to its implementation in a control circuit. It is, however, possible to use a fixed reference voltage given known lighting conditions, such as indoor applications. Q1 Ibat VPV D1 D2 Irect Vctrl To + + Sensor Vrect Cs L VC2 C2 B1 VB1 PV - - Vrect MPP Estimation Q2 Figure 8. DC-DC buck-boost converter for solar energy harvesting. 4.3 Multi-source energy harvesting with intelligent power management The core of any EH module is the harvesting circuit, which draws power from the transducer (e.g. solar panels, piezoelectric bimorphs, temperature differential, etc.), and manages energy storage. Figure 9 shows the simplified diagram of the proposed EH power management (PM) system architecture. The most important consideration in the design of this circuit is to maximize energy efficiency, enhance device reliability, and consequently, lengthen the life of the sensor. It is important to note that no matter what the source energy is, the PM circuit needs to be designed so that its input impedance matches that of the source. As noted earlier, solar cells and PZT bimorphs have high output impedance. This results in considerable loading and therefore, minimal output power. If this impedance is matched adaptively, there will always be maximum power routed and stored in the energy storage device. The PM circuit is responsible for adaptively calculating the output impedance of the transducer and consequently, adjusting key parameters to maximize charging of the energy storage device.   Solar/Vibrations/ AC/DC Rechargeable battery,   Thermal, etc. Conversion Ultracapacitor, etc. Energy Optional DC/DC Tranducer Storage Rectification Converter Device PWM Controller Vrect, Irect Vbat, Ibat Figure 9. Adaptive multi-source power management architecture. As noted earlier, it is feasible to use switch mode DC-DC buck, boost and buck-boost converters to maximize harvested power. The optimal duty cycle of the converter is dependent on output filter inductance (L) and switching frequency (fs).
  • 9. These converters can significantly reduce the matching impedance of the circuit so that typical loads such as batteries that have impedance of the order of few hundred ohms can be efficiently charged. However, there has been limited work towards development of adaptive converters that facilitate accurate impedance matching. Most of the converters used are meant for providing regulated output voltage to resistive loads with no real concern on loading effects of the source. This is because these power supplies operate using near ideal voltage sources and simply perform the required energy conversion. Existing system level solutions that incorporate custom DC-DC converters for impedance matching are tuned to a specific frequency and transducer for specific application environments. These power management strategies are not generic and therefore, cannot be used over a wide spectrum of frequencies and transducers. Moreover, some of these methods use power hungry digital signal processors (DSPs) for implementing PWM control algorithms12. This strategy results in excessive power consumption in the power management solution and therefore, does not allow feasibility in practical applications. Figure 9 shows the active PM architecture. A simple feedforward strategy is used wherein the average input voltage (Vrect) and current (Irect) of the adaptive PM circuit is measured to determine the average input resistance (Rin) given by: Vrect/Irect. Alternatively, input frequency can be measured (for vibration transducers) and converted to a voltage (frequency to voltage converter) to determine input impedance. The converter input impedance can then be adjusted to this value by varying D to obtain accurate impedance matching. Similarly, fs or a combination of fs and D can be varied to obtain required Rin. Such a method can easily be implemented on a DSP or FPGA but this is impractical due to lower power levels (mWs) being harvested as compared to power consumption of the control circuits. The controller uses a simple formula to determine required D based on the Rin measurement. The calculated D value is passed onto a standard pulse width modulator (PWM) and MOSFET driver circuit to turn on the MOSFET at the desired D. Although, the converter output voltage (Vbat) and current (Ibat) is shown to be fedback to the controller, this is not necessary if operation is restricted in DCM. This reduces overhead and simplifies controller design. 5. PRELIMINARY RESULTS 5.1 Energy harvesting from vibrations and solar cells In order to harvest energy from vibrations, the first step involves obtaining the vibration spectrum from the source or structure. It is important to determine the range of frequencies at which maximum force (acceleration) occurs. In this paper, data has been obtained from ground and bridge vibrations to observe feasibility of designing vibration EH systems for UGS sensors for border security and perimeter monitoring. The Texas Micropower Inc. TMP-DL-2 vibration data logger was used to measure the data. This device contains a 3-axis accelerometer and is well-suited to measure vibration data upto ±6 gpeak with ±0.01gpeak resolution and sampling rate upto 500 Hz or 1000 Hz13. Figure 10 shows one set of data obtained from bridge deck vibrations during low traffic situations. Acceleration of about 0.02 gpeak was obtained in the 5 - 20 Hz frequency range during relatively low traffic situations on bridges. Similar data has been obtained for staircase foot vibrations (with resonant frequency of 20 - 30 Hz at acceleration of about 0.05 g)14. These results give good insight of typical vibration profiles that can be expected at fences and posts. Figure 10. Frequency spectrum of a bridge deck under low traffic conditions.
  • 10. Adequate macro-harvesters were designed for these specifications. Initial experiments have been performed by designing a buck-boost converter for vibration EH using piezoelectric bimorphs. A buck-boost converter was chosen for the design because under DCM, at fixed values of fs and D, it is possible to have a constant Rin which can be fixed at the required matching impedance. The prototype was capable of delivering > 150 µW continuous power at low excitation levels of 0.07 gpeak at efficiencies of the order of 60-70%15,16. With a 5 - 15 mW sensor average power requirement budget, it can be observed that the size of these bulk piezoelectric harvesters will be fairly large. For example, for 0.02 g sustained vibrations, we anticipate the harvester size to be about 300 cm3 for a 5 mW average sensor load. Similarly, we anticipate the harvester size to be about 800 cm3 for a 15 mW average sensor load. 5.2 Multi-source energy harvesting In order to enhance the net energy harvested, it might be necessary to incorporate multiple energy harvesting sources. In a practical system, this needs to be implanted with a low power transfer overhead rather than using a custom power management system for each source. A single power management solution shown in Figure 11 that allows energy harvesting from multiple sources can provide this flexibility. Power Management DC/DC Vpzt converter AC/ Vrect DC Switch Impedance PZT Matching Circuit B Vctrl1 a VIBRATION SENSE t t e DC/DC B r VPV converter a y t MPPT Tracking t Solar Switch M e Algorithm Loop a r n y Vctrl2 a LIGHT SENSE g e m DC/DC e VTEG Charge converter n Pump t TEG Max Power TEG Vboost Switch Loop Vctrl3 TEMPERATURE SENSE Figure 11. Power management for multi-source energy harvesting. We have developed a multi-source energy harvesting power management solution for low power sensors and electronic systems which efficiently enables both solar energy harvesting from photo-voltaic cells & vibration energy harvesting from piezoelectric transducers. The harvested energy is stored in a rechargeable 3.6 VDC lithium-ion battery. It is designed with the intent of low power consumption from circuit components and high conversion efficiency delivering unmatched performance. The figure also includes thermoelectric generators (TEGs), however initial results have focused on only vibrations and solar. Note that TEGs have a low output voltage, and a relatively large current. This poses a challenge when charging Li-ion batteries. If the TEG is designed to deliver a higher voltage at the expense of lower current, the cost increases. Therefore, for most commercially available TEGs, the output voltage is small, and the current is high. To allow TEGs to be integrated to the power management, the figure shows a charge pump followed by a boost converter. The harvester’s vibration module consists of a full-wave rectifier circuit for converting AC to DC and a buck-boost converter to enable adequate impedance matching and maximum power transfer to the load. The circuit is optimized to work on typical low frequency vibrations that are obtained on automobiles, rail cars, industrial machines, bridges and other places which are subject to vibrations. The harvester’s solar module also uses a buck-boost converter that allows maximum power transfer to load based on a desired constant voltage maximum power point (MPP) value. The circuit is
  • 11. optimized to work on typical ambient light sources such as sun light, incandescent or fluorescent lights. A battery protection circuit helps in protecting the battery from over-charging and over-discharging. Figure 12 shows the prototype system with power management circuit and lithium polymer secondary battery as the energy storage device. The overall form factor of the complete prototype is estimated to be 40 x 40 x 85 mm3 and is expected to be much smaller once the electronics are integrated into a single chip. Power management board Vibration power management Vibration  Piezoelectric  transducer  bimorphs inputs VDD to Solar cell sensor inputs Amorphous solar cells Solar power management Figure 12. Solar and vibration multi-source energy harvesting system. For Solar EH, the MPP voltage VMPP can is set or modified by replacing external resistors based on a simple formula. Similarly for vibration EH, the matching impedance can be set by replacing an external inductor using on a simple formula. Initial results have been measured using solar cells under indoor lighting conditions with characteristics shown in Figure 7 and a bulk Smart Material based piezoelectric bimorph. An intensity of 300 lux was used similar to that obtained under indoor fluorescent lighting conditions. At this light intensity the power output to the battery from the solar cells equaled about 160 µW. The vibration intensity (excitations) for the piezoelectric transducer was varied upto 0.1 gpeak and the power output to the battery from each source was quantified. The x axis indicates the corresponding rectifier voltage (Vrect) at a given excitation (refer Figure 11). As seen from Figure 13, the power management strategy enhanced energy harvested with an efficiency of > 80%. For example, for a Vrect of 3VDC, the output power from vibrations equaled about 148 µW. The power management routed the output from both vibrations and solar so that the total output power equaled about 292 µW at Vrect = 3VDC. 500 combined 450 400 Output power (µW) 350 vibrations 300 250 200 solar (indoors) 150 100 50 0 1.5 2 2.5 3 3.5 4 Vrect (vibration) Figure 13. Multi-source energy harvesting results. Similar results were also obtained under outdoor lighting conditions. However, in this case, the solar cell output power (few mWs) dominates the total power routed to the storage device and therefore, these results are not included. 6. CONCLUSIONS
  • 12. The design of multi-source EH systems for remote wireless devices such as in border security is explored. These low- power wireless sensor applications are very important primarily because of the rapid development of the markets and secondly, due to the limited life of batteries. This, in particular, adds cost and limits deployment of these devices to high- value asset and limited life applications. Although the advantages of these devices are enormous in terms of communication range, throughput, and reliability, the battery cost and its replacement do hamper adoption rates. Harvesting energy from multiple sources such as vibrations and solar can extend battery life and at the bare minimum replenish “sensing” energy consumption which is a dominant factor in battery life reduction for these applications. This requires efficient power management to enhance energy harvested. Results show that a switching DC-DC converter is realistic solution that allows low cost power management and enhances efficiency. This allows miniature energy harvesting power supplies to maximize their power density within desired form factors and cost, and allows a realistic possibility of perpetual sensor node life especially at remote locations for a broad range of defense (including in border security) and industry applications. REFERENCES [1] E. Yeatman, "Advances in Power Sources for Wireless Sensor Nodes", International Workshop Wearable and Implantable Body Sensor Networks, pp. 20–21, Imperial College, 2004. [2] J. Stevens, "Optimized Thermal Design of Small ∆T Thermoelectric Generators", 34th Intersociety Energy Conversion Engineering Conference, paper 1999-01-2564, Society of Automotive Engineers, 1999. [3] H. Schmidhuber and C. Hebling, "First Experiences and Measurements with a Solar Powered Personal Digital Assistant (PDA)", 17th European Photovoltaic Solar Energy Conference, pp. 658–662, ETA-Florence and WIP- Munich, 2001. [4] C. Shearwood and R. Yates, "Development of an Electromagnetic Micro-generator”, Electronics Letters, Vol.33, No.22, pp.1883-1884, October 1997. [5] R. Amirtharajah and A. Chandrakasan, "Self-Powered Signal Processing using Vibration-Based Power Generation", IEEE Journal of Solid-State Circuits, Vol.33, No.5, pp.687-694, April 2004. [6] S. Roundy, P. Wright, and J. Rabaey, "A Study of Low Level Vibrations as a Power Source for Wireless Sensor Nodes", Computer Communications, Vol. 26, pp. 1131–1144, July 2003. [7] S. Priya, “Advances in Energy Harvesting Using Low Profile Piezoelectric Transducers”, J. Electroceram, 19, 165(2007). [8] R. Islam and S. Priya, “High-Energy Density Ceramic Composition in the System Pb(Zr,Ti)O3-Pb((Zn, Ni)1/3Nb2/3)O3”, J. Am. Ceram. Soc. 89, 3147(2006). [9] L. Baldenegro-Lopez, H. Alshareef, E. Fuentes, M. Quevedo-Lopez, B. Gnade, A. Hande, and P. Shah, “Deposition and Characterization of Piezoelectric Thin Films on Non-Conducting Surfaces for Energy Harvesting Applications”, Material Research Society (MRS) Fall Meeting, Boston, MA, December 2008. [10] E. Lefeuvre, D. Audigier, C. Richard, and D. Guyomar, “Buck-Boost Converter for Sensorless Power Optimization of Piezoelectric Energy Harvester”, IEEE Transactions on Power Electronics, Vol. 22, No. 5, September 2007. [11] D. Brunelli and L. Benini, “An Efficient Solar Energy Harvester for Wireless Sensor Nodes”, 11th Conference on Design, Automation and Test in Europe, March 2008. [12] G. Ottman, H. Hofmann, A. Bhatt, and G. Lesieutre, “Adaptive Piezoelectric Energy Harvesting Circuit for Wireless Remote Power Supply”, IEEE Transactions on Power Electronics, Volume 17, Issue 5, pp. 669-676, September 2002. [13] “Portable Vibration Data Logger”, Texas Micropower Inc., http://www.texasmicropower.com/products_tech/data_logging.htm, 2008. [14] E. S. Leland, E. M. Lai, and P. K. Wright, “A Self-Powered Wireless Sensor for Indoor Environmental Monitoring”, 2004 Wireless Networking Symposium, The University of Texas at Austin Department of Electrical & Computer Engineering, Wireless Networking & Communications Group, October 2004. [15] A. Hande, A. Rajasekaran, and D. Bhatia, “Buck-Boost Converter Based Power Conditioning Circuit for Low Excitation Vibrational Energy Harvesting”, Third Annual Austin Conference on Integrated Circuits and Systems, Austin, TX, May 2008. [16] A. Hande, P. Shah, E. Fernandez, L. Baldenegro, H. Alshareef, and B. Gnade, “Integrated Energy Harvesting with Multisource, Adaptive Interfaces”, 4th Annual Energy Harvesting Workshop, Blacksburg, VA, January 2009.