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

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.

The PhD candidate will be a member of the Advanced Vehicle Engineering Centre (AVEC) at Cranfield University. AVEC focuses on the development, application and evaluation of advanced vehicle technologies to help make vehicles more capable, lighter, greener and more efficient. Working across multiple sectors, such as automotive, aerospace, agriculture, defence and motorsport, our engineering capabilities deliver solutions to make vehicles greener, through advanced and alternative powertrain solutions to the development and application of lightweight materials. 

We have provided research, development, education and training for the automotive engineering sector for more than 65 years. Our close contacts with industry means we equip students with the knowledge and opportunities to develop a successful future career in engineering whether this be in main stream automotive sector or other relevant sectors. Our courses are accredited by the Institution of Mechanical Engineers (IMechE), the Royal Aeronautical Society (RAeS) and the Institution of Engineering and Technology (IET) for fulfilling further learning requirements for CEng.

Although Cranfield University’s Battery Lab operates within AVEC, our battery research is not just limited to vehicles. Established in 2014, our battery lab provides test facilities and equipment for battery testing under a wide range of applications. Our experimental data is usable for developing battery electrical and thermal models as well as designing and validating BMS algorithms. 

On the other hand, outside Cranfield University, the PhD candidate will also receive support from the Faraday Institution, the funder of this study. Further information about them is available on the Faraday Institution website.

Regarding the expected impacts of this PhD study, it targets several key challenges currently limiting the advancement and commercialisation of next-generation batteries in the following ways: 

(1) Tackling the performance and degradation uncertainties in Li-S Batteries. (2) Bridging the gap between physics models and real-world operation. (3) Enabling robust, data-efficient battery management. (4) Improving the safety and reliability of batteries.

Sponsored by the Faraday Institution, the PhD researcher receives a UKRI stipend for 4 years. All researchers will also receive a stipend per year to support training and consumables, alongside access to a bespoke Faraday Institution PhD Training Programme valued at approximately £5,000 per year. Recipients benefit from a wide range of development opportunities, including networking events, industry visits, mentorship, and internships, as well as high-quality training experiences designed to further develop their knowledge, skills, and career aspirations. Details of previous training programmes can be found on the Faraday Institution website.

In addition, the PhD researcher will have access to Cranfield University’s test facilities, as required. Full access to the Battery Lab on the Cranfield Campus will be provided for any experimental tests. Training will be provided to the candidate before using the battery lab. 

This PhD study provides a fantastic opportunity to obtain transferable skills in the fields of Battery Energy Storage Systems, as well as Applied Artificial Intelligence. Those are both hot topics in the industry, which help candidate’s employability after graduation.