PhD Studentship: Self-learning battery management systems for lithium–sulfur batteries

This fully funded PhD by the Faraday Institution, facilitates the use of next-generation Lithium-Sulfur (Li-S) batteries for transport systems. You will combine hands-on experiments with physics-based data-driven modelling to understand how Li-S batteries perform in real applications and develop suitable battery management systems (BMS) for that technology, capable of coping with the unfamiliar, reducing time, self-calibration, and optimising performance throughout the battery’s life. The project contributes to the development of next-generation battery systems, aligned with the UK’s ambitions for advanced energy technologies.

Lithium–sulfur (Li-S) batteries are a promising alternative to today’s lithium-ion batteries because they offer much higher theoretical energy density and use low-cost, sustainable materials. However, Li-S batteries are difficult to manage in real applications. Their electrochemical behaviour is highly complex, and they suffer from fast capacity loss, unstable reactions, and challenges in estimating key internal states such as state-of-charge (SoC) and state-of-health (SoH). Existing battery management systems (BMS), designed mainly for lithium-ion chemistries, cannot accurately track or predict these behaviours, which limits the safe and efficient use of Li-S batteries. Today, we do have techniques for estimating SoC and SoH in Li-S batteries; these depend on data collected a posteriori before deployment, so it is necessary to completely age a set of batteries in a representative environment in order to design BMS algorithms for them.

This studentship will consider how a BMS for Li-S could learn ‘on the go’. This opens up a pathway to quickly deploy new build standards of Li-S, and to transfer into new applications with different duty cycles and conditions. This would smooth the pathway for the latest technologies, and maximise the potential for early deployment in applications. This project aims to develop a physics-based, self-learning BMS tailored specifically for Li-S batteries. The research will combine physics-driven models with machine-learning algorithms that can update themselves as the battery operates. By integrating real-time sensor data, the BMS will continuously refine its internal model, improving the accuracy and allowing the system to adapt to ageing, changing conditions, and different usage patterns. The final outcome will be an intelligent BMS capable of coping with the unfamiliar, reducing time, self-calibration, and optimising performance throughout the battery’s life. The project contributes to the development of next-generation battery systems, aligned with the UK’s ambitions for advanced energy technologies.