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| Fig. 1: Free space path loss plotted verse distance at two carrier frequencies. |
Harvesting and converting electromagnetic radiation into electricity is well established for frequencies in the electromagnetic spectrum around those of visible light. However, when our dominate source of visible light dips below the horizon, stored battery power must be used to meet electrical demands for systems dependent on photovoltaics alone. However, many new applications are forcing a reconsideration of the requirement for stored power in batteries or similar charge storage devices. [1] Some examples of such systems include devices that are:
too small to fit a battery, [2]
predominately ink-jet printed, rendering battery placement impractical, or [3]
placed in very hard to reach places, where battery replacement is challenging. [4]
Fortunately, if any of these devices required energy on a gloomy day or overnight, there is often abundant, although not- visible, electromagnetic radiation available. This electromagnetic radiation is not from an extraterrestrial source like the sun, but instead a very much terrestrial source: the television, radio and cellular towers that dot our planet. Like sunlight, the power emitted from these towers that is not captured by an energy collector, will end up (just slightly) heating the pavement. However, unlike photovoltaics, the energy collectors at these lower radio frequencies (RF) are antennas, and the circuits attached to them. For all the antennas that are collecting this energy for purposes other than what the transmitters intended (i.e. for television broadcast or cellular communications), this electromagnetic energy can be considered part of the environmental ambience, available to be repurposed.
For reasons that will soon be made more obvious, the distance between the RF collector (or harvester) and the RF tower is critical to the performance of the collector. However, the density of the towers, as well as transmission power level at each tower, varies from region to region. In particular, urban and semi-urban regions with higher population densities (and thus often a higher density of towers) generally have the highest levels of ambient RF energy in the frequencies of interest. A survey conducted in London, found that over 50% of locations just outside of London's 270 underground stations, has sufficient ambient RF energy to power RF harvester circuits tuned for digital television or various cellular transmission technologies (ranging in frequency from 300 MHz - 3 GHz). [5]
In Japan, where television broadcasts are limited to a power level of 100 kilowatts, signals from seven television broadcast were detected in downtown Tokyo (at a distance of about 6.5 km from the mountain top broadcasting site), with measured power levels that could be exploited by an RF harvesting circuit. [6] Nonetheless, referring to their measured results in table 1, we can see that the power level has decreased by more than 9 orders of magnitude (over 90dB): from a hundred kilowatts to tens of microwatts.
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| Table 1: Signal strengths measured from seven television broadcast channels, detected in downtown Tokyo about 6.5 km from the mountain top broadcasting site. [6] |
Although some of the power losses seen in the table above can be attributed to inefficiencies in the power harvesting circuitry, the overwhelming cause of power loss is due to a phenomenon referred to as free space power loss. Referring to the equation for path loss shown below, we can see that power of a signal scales with the square of distance, r, due to the effect of path loss over that distance.
| free space path loss | = | ( | 4 π r λ |
)2 |
where λ is the wavelength (speed of light divided by the frequency). It is also noteworthy that the distance is linearly proportional to the frequency. Thus decreasing the carrier frequency of the signal by x, increases the distance the signal reaches at a given power level also by x. The relationship between distance, path loss and carrier frequency can be seen in Fig. 1.
Finally, we must also address for the energy efficiency of the harvesting units themselves. It is well known that the computational efficiency of chips is increasing at an exponential rate. Following the process detailed in Smith, we can see if the increases in computational efficiency can also be extend to energy efficiency. [7] We start with a metric of "instructions per second" to capture computational efficiency and we divide this by the energy used per second (aka the "Watt", which is equivalent to the unit "Joule per second) to come up with a figure of merit with units of "instructions per Joule". This is represented as the equation [7]
| instructions per second Watt |
= | instructions/second Joule/second |
= | instructions Joule |
With the informed assumption that the microcontroller will be the most energy hungry component, Smith surveyed almost four decades of microcontroller's data sheets, and selected the most efficient microcontroller (given the above figure of merit) for each year. [7] He found that the relation between time and the number of "instructions per Joule" was indeed exponential (proportional to 2(t/τ), where t = time and τ = 2.17, both with units of seconds).
We can thus expect to see more and more ambient RF energy harvesting devices in our future, even beyond our ambient energy rich cities.
© Emily McMilin. The author grants permission to copy, distribute and display this work in unaltered form, with attribution to the author, for noncommercial purposes only. All other rights, including commercial rights, are reserved to the author.
[1] A. Eisenberg, "Bye-Bye Batteries: Radio Waves as a Low-Power Source," New York Times, 17 Jul 10.
[2] M. Tabesh, et al., "A power-harvesting pad-less mm-sized 24/60GHz passive radio with on- chip antennas," IEEE 6858380, 10-13 June 2014.
[3] K. Sangki, et al., "Inkjet-printed RF energy harvesting and wireless power trasmission devices on paper substrate," IEEE 6686824, 6-10 Oct. 2013.
[4] T. Galchev, et al.,"Energy Harvesting of Radio Frequency and Vibration Energy to Enable Wireless Sensor Monitoring of Civil Infrastructure" Proc. SPIE 7983, 798314, 18 Apr 11.
[5] M. Pinuela, et al., "Ambient RF Energy Harvesting in Urban and Semi-Urban Environments," IEEE Trans. Microw. Theory 61, 2715 (2013).
[6] R. Vyas, et al., "A Battery-Less, Energy Harvesting Device For Long Range Scavenging of Wireless Power From Terrestrial TV Broadcasts," IEEE 6259708, 17 Jun 12.
[7] J. R. Smith, Wirelessly Powered Sensor Networks and Computational RFID (Springer, 2013).