Digital Twin Modelling and Embedded Sensing of Batteries

Project Description

Batteries are one of the most ubiquitous man made items. They are used to power all sorts of devices from electric cars, to heart pace makers, to cell phones. They employ a wide range of materials, technologies and packaging but all use electrochemical principles to convert chemically stored energy into an electric current. As the demands on battery technology grow, industry faces many challenges related to power density, efficiency, safety and ecological sustainability. The well-publicised Samsung Note 7 recall is a good example of the competing challenges battery technologists face and the risks of pushing a technology to the very limits. This project proposes to develop a modelling and sensing strategy that is individualized to the battery technology which will allow it to be managed for maximum efficiency. This will enable greater energy density and extend operating life, which in turn will have significant implications on raw materials use and sustainability. The strategy is to develop a complete 'digital twin' of the battery which accurately estimates all of the important state parameters. An augmented sensing platform with many different sensing capabilities will be designed as part of the project to accurately inform the model. Accurate and up to date models will allow for optimization of the performance at all stages of the batteries life and under all operating conditions which would be a huge evolutionary step for battery monitoring systems.