We are developing battery management algorithms for lightweight lithium-sulfur batteries in this collaboration. Our work allows end users to utilise the full physical battery capacity.
The three and a half-year REVB (Revolutionary Electric Vehicle Battery) project, which finishes in April 2017, aims to develop an energy storage system made up of a revolutionary lithium-sulfur vehicle battery and energy system controller.
We are developing the embedded software algorithms for cell modelling and state estimation, using cutting-edge techniques from control engineering and computer science.
- Funded by Innovate UK.
Impact of our research
Electric vehicles will be key to meeting the mobility needs of a growing global population while mitigating the economic and environmental problems of traditional petrol and diesel vehicles. Unfortunately, electric vehicles are expensive due to their batteries which cost roughly the same as a traditional car’s engine. Also, even the best of today’s electric vehicles have a very short range when compared to a petrol or diesel vehicle.
Our research is developing lithium-sulfur battery technology so that it can be used in electric vehicles. If successful, it will help make electric vehicles cheaper, increase their range and improve their safety.
Why the research was commissioned
Lithium-sulfur batteries behave differently to incumbent lithium-ion technologies, and the battery management techniques that work well for lithium-ion do not work effectively with lithium-sulfur. To get the full physical battery capacity, battery management relies on good scientific models but it is difficult or impossible to use these directly as part of an on-board computer.
Our work was commissioned because there was a need to capture the insights from the detailed electrochemical work and develop it into a set of algorithms that could be used in a practical in-vehicle battery management system suitable for real-world vehicles.
We have a proven track record of taking advanced theoretical techniques from academia and deploying them in real-world applications. We have expert knowledge of control engineering and system integration, and we are experienced in applying these in an automotive context.
This project built on previous collaborative work and has made use of our expertise in computational modelling and system identification, estimation and control theory, and artificial intelligence, combined with our understanding of rapid control prototyping and software engineering.