How many times have you gone somewhere, seen that your phone's battery was low, and then realized that you forgot to bring your charger with? Although it may take a decade or two, this pesky issue may become a thing of the past.
These days, it's likely you've heard terms like "4G", "5G", and so forth mentioned in the context of smartphones. Informally speaking, each additional "G" represents a new "generation" of wireless communication standard. These generations are rolled out in roughly 10-year intervals, bringing various improvements and innovations. For example, past innovations have increased the data rate of wireless technologies, making it possible to wirelessly send videos and images, rather than just plain text. Wireless charging may eventually make its way into a future generation, allowing you to charge your phone without actually needing a physical charging cord.
Professor Mai Vu, an associate professor in the school of electrical and computer engineering, leads the LiNKS lab at Tufts. Among other things, one project of this lab is the development of the aforementioned wireless charging technologies.
So how does wireless charging work? It may seem surreal, but the central principle is the same one behind many other wireless technologies. Wireless communication essentially operates through the transmission and reception of various electromagnetic signals, such as RF (radio frequencies). Radio frequencies are a form of energy, and can thus be converted to the electrical energy needed to charge a phone. Because RF is so common in our modern, technology-saturated world, a device capable of wirelessly charging could, in an ideal situation, harvest an appreciable amount of energy from its surrounding environment.
Despite the ubiquity of RF signals, complex issues remain. Mobile devices are, as the name suggests, highly mobile—and as a result, it's difficult to ensure that optimal charging can take place in a constantly changing environment. One moment, a device may be inside a bedroom, and in the next, it might be outside. Luckily, tools such as machine learning are useful for attempting to solve this problem.
While much of the original research into RF energy harvesting was done at the University of Washington, the LiNKS lab continues exploring possibilities of RF and refining it in hopes that it may one day become practical for everyday use.
To students interested in this area of study, Prof. Vu recommends building up a strong foundation in mathematics. Such complex engineering problems involve many types of mathematical modeling, including probability theory, linear algebra, and optimization; machine learning is also becoming an increasingly relevant skill in this area.