Nigel Groome Studentship: Battery degradation modelling and SOX estimation for EV applications

Accurate battery degradation modelling and estimation of electrochemical states are essential for improving electric vehicle performance, safety, and lifetime. This PhD, in collaboration with Jaguar Land Rover, will develop physics-informed, state-based estimatiion algorithms for Lithium-ion battery systems.

The project will integrate electrochemical degradation models with advanced estimation methods, inc. Kalman filtering and observer-based techniques, to enable real-time prediction of internal states and ageing mechanisms. Emphasis will be placed on balancing model fidelity, computationals efficiency, and robustness under varying operating conditions.

The research will involve developing reduced-order electrochemical models that capture ageing effects such as capacity fade and resistance growth, and embedding them within state observers and filtering algorithms. Data-driven and hybrid modelling approaches will be explored to enhance adaptability to real-world operating conditions.

The candidate will validate algorithms using experimental or industrial datasets and implement them in MATLAB/Simulink for integration within automotive BMS architectures. Close collaboration with Jaguar Land Rover will ensure industrial relevance with a focus on scalable, implementable solutions for production vehicle platforms.

The studentship requires you to undertake the equivalent of up to 6 hrs of teaching per week on average, during semester time, and to include preparation and marking (no more than 20 hrs / week), and to participate in a teaching skills course. Teaching, preparation and skills courses would all be without further remuneration.