The idea of using sensors to measure everything enables us to seamlessly interact with our surroundings. Ubiquitously distributed sensor nodes [e.g. SmartDust, SWARM] is becoming a significant part of our digital life in this post personal computing era. However, unlike many of the human centric portable devices (smart phone/pads), most of these sensor nodes cannot be powered directly by batteries. And the reason is quite straightforward: the desired service time for a typical wireless sensor is long and the cost associated with changing depleted batteries for a wireless sensor network is staggering. Facing with this challenge, engineers came up with an alternative solution: to harvest energy from the ambient environment.
There are several energy sources present in our environment, of which the most common ones may include: vibration, thermal gradient, radiation and power (solar and EM fields) The power output of an energy harvester depends on two factors: the power density of ambient energy sources and the effectiveness of conversion mechanisms. While the former is mostly determined by the nature of the applications, we are offered with more options in choosing the best conversion mechanism or sometimes even a hybrid mechanism for a particular energy sources. Some of the major conversion mechanisms are summarized in the figure on the upper left. With today’s technologies, for a large number of applications, such as, monitoring of the operation of machine tools, body sensor networks, scientists and engineers are able to design and fabricate battery-less sensing modules to perform tasks at a fairly high duty cycles and of course, for a very long service time.
In this particular project, we’ve designed and fabricated a self-powered wireless sensor node for the monitoring of overhead power lines. The node is powered by an electromechanical AC energy harvester. The harvester is constructed by attaching a pair of oppositely poled Neodymium-Iron-Boron permanent magnets to the end of a piezoelectric cantilever. Strong magnetic coupling between the permanent magnets and the AC magnetic field bends the piezoelectric cantilever, producing a significant voltage potential across its electrodes. A mechanical stopper is designed to prevent the energy harvester from mechanical failures. Since the harvester is an electromechanical resonator, its output of the device is an alternating current. We use LTC3588-1 energy harvesting rectification and conversion circuit from Linear Technology. This integrated circuit has a bridge rectifier and a DC-DC converter, and is capable of providing 1.8V, 2.5V, 3.3V and 3.6V outputs. The radio mote is the EZ430-RF2500 from Texas Instruments. This platform consists of an MSP430 microcontroller an a single chip CC2500 wireless transceiver, integrated on a printed circuit board which measures approximately 35mm times 20mm. It has ten accessible I/O pins and A/D channels to which peripherals, e.g. current, voltage, temperature.. sensors may be connected.
We show in the experiment the mote is able to continuously measure temperature and transmit the readings to a nearby receiver connected to a laptop. The reporting frequency is a function of the average power output of the energy harvester, both of which depend on the current carried by the conductor as shown in the figure on the left. As you can see in the “more technical details” section, we’ve modeled the electro-mechanical resonator as an LRC circuit, as essentially the resonating mass-spring-damper system can be characterized as a second-order differential equation, from which analogy can be drawn between the mechanical and electrical domain. This lumped parameter modeling technique allows us to simulate the entire system, from the coupling between the magnets and the current in the conductor, to the power conditioning circuit, and finally to the sensors and radio motes, in a single SPICE model. It greatly helps us understand the relationship among different factors in this multi-physics systems. We hope this battery-less platform will some day become an essential part of our electrical power infrastructure, reliably providing real-time monitoring data to utilities and help improve the overall energy efficiency and the stability of our electric power systems.