Circuit Design for Wireless Energy Harvesting

Abstract

Introduction To date, there have been a number of research proposals to explore the newly emerging wireless charging technologies based on radio-frequency (RF) signals, ambient or dedicated. In particular, research efforts towards achieving the goal of transmitting information and energy at the same time have been rapidly expanding, but the feasibility of this goal has not been fully addressed. Moreover, the respective coverage areas of transmitting information and energy are wildly different, the latter being considerably smaller than the former. This is because the receiver sensitivities are very different, namely -60 dBm for an information receiver and -10 dBm for an energy receiver [1, 2]. Owing to this limitation, recently a commercial implementation of RF energy transfer has been restricted to lower-power sensor nodes with dedicated RF energy transmitters, such as the Powercast wireless rechargeable sensor system [3] and the Cota system [4]. In this chapter, we discuss the implementation of long- and short-range RF energy harvesting systems, where the former is to provide far-field-based RF energy transfer over long distances with a 4 × 4 phased antenna array and the latter to provide biosensors with RF energy over short distances. An overall circuit design for these RF energy harvesting systems is described in detail, along with the measurement results to validate the feasibility of far-field-based RF energy transfer. We illustrate the designed test-beds which will be applied to develop sophisticated energy beamforming algorithms to increase the transmission range. Finally, a new research framework is developed through the cross-layer design of the RF energy harvesting system, which is intended to power a low-power sensor node, like the Internet-of-Things (IoT) sensor node. To this end, we present a circuit-layer stored energy evolution model based on the measurements which will be used in designing the upper-layer energy management algorithm for efficient control of the stored energy at the sensor node. The new framework will be useful because the existing works on RF energy harvesting do not explicitly take into account a realistic temporal evolution model of the stored energy in the energy storage device, like such as a supercapacitor.