Energy Harvesting across Temporal Temperature Gradients using Vaporization
Charles Xiao, Nicholas D. Naclerio, Elliot W. Hawkes
Abstract
Energy harvesting is an attractive alternative to carrying onboard power for mobile robots, especially for long duration missions. While solar is a powerful option, alternatives are needed for situations where direct sunlight is unavailable. One intriguing concept was proposed in the 17th century to power clocks: energy harvesting based on temporal, rather than spatial, temperature gradients, using a low boiling point fluid that vaporizes at ambient temperatures. This concept has many strengths: it offers all-in-one energy harvesting and storage; direct high-force and large displacement mechanical output, eliminating the need for a motor; and temporal gradients are ubiquitous, due to diurnal thermal fluctuations. The challenge for robotic applications, however, is to create large enough amounts of work in a small enough package to power a mobile device while using a non-toxic and readily available working fluid. Here we present a simple, low-cost energy harvesting actuator, powered by the vaporization of butane and isobutane, with an isobaric energy density of up to 38000 J/m3 (i.e. energy extracted per total volume expansion) each time the temperature fluctuates 13.1°C, enough to power a small car to drive 10m. Two principles enable this: i) precompression of the working fluid, allowing us to tune the boiling point and choose among many non-toxic fluids that do more work than non-compressed fluids; and ii) a constant force profile of the return springs, allowing more work than a linear spring. We present a simple model of the actuator and experimental results characterizing its behavior. Our work lays the foundation for energy harvesting across temporal temperature gradients using vaporization as a viable option for powering mobile robots.
BibTeX
@inproceedings{iros2019_energyharvesting,
title = {Energy Harvesting across Temporal Temperature Gradients using Vaporization},
author = {Charles Xiao and Nicholas D. Naclerio and Elliot W. Hawkes},
booktitle = {IROS 2019},
year = {2019}
}