Grant: Engage Plus Grants for Universities

The importance of energy storage in the sustainable energy chain has resulted in an increasing number of_x000D_ battery technologies being introduced into the market. Utilizing each type of the battery to its full potential_x000D_ requires charging and discharging methods that are specific to the battery chemistry and the battery's state of_x000D_ the health. However, the technology behind the battery is not always known to the user. A method that enables_x000D_ the fast identification of the battery chemistry can be achieved through measuring its response to electrical or_x000D_ other perturbations. This approach could enable identification of the number of cells in a packaged battery and_x000D_ its appropriate and safe handling. Many major telecommunication device manufacturers have shown interest in_x000D_ this area by filing patents for such a device. However, these patented systems are directed towards use within_x000D_ the company and mainly distinguishes between the various battery models specific to the manufacturer. They_x000D_ have yet to show a working prototype of a universal battery identifier. During the first Engage grant supported_x000D_ research, the specific characteristics of the various battery technologies were studied to determine the_x000D_ distinguishing features of each battery type. Battery classification was handled by fitting to physical models and_x000D_ then through data cleansing and machine learning techniques. The results showed that the proof of concept was_x000D_ successful. This initial success will be further developed with the company to create a universal battery_x000D_ monitoring and charging system.