Under pressure: Predictive modelling of battery ageing across the scales

The main goal of the project is to develop a novel, physics-informed surrogate model using Bayesian machine learning to predict gas generation and pressure evolution. It will build on the following specific goals:

• Construct a continuum mechanical model to simulate battery expansion and contraction during cycling.
• Incorporate gas-generating reactions to link electrochemical activity with mechanical stress and long-term pressure evolution.
• Train a surrogate model on outputs from high-fidelity physics-based models, and validate it against experimental data.

• The project will deliver a novel, physics-informed Bayesian modelling framework for gas generation in lithium-ion batteries. The developed code will be made publicly available to support open science, and the results will be published in leading peer-reviewed journals, contributing to the advancement of battery modelling research.
• We will create new collaborations, particularly with experimental partners involved in Faraday Institution projects, who will provide access to high-quality data and serve as a foundation for future interdisciplinary research projects. The student will lead these collaborations, positioning as an expert in battery modelling.
• The student will develop their skills in a broad range of areas. This will prepare them for competitive careers in both academia and the battery industry, where such integrated expertise is in high demand.

The student will acquire the following skills:

Battery science: The student will gain deep understanding into lithium-ion battery behaviour and degradation mechanisms, with a particular focus on gas generation and mechanical response.

Interdisciplinary collaboration: Through close interaction with our industrial partner, Jaguar Land Rover, the student will develop strong collaboration skills across physics, engineering, and data science.

Physics-based and data-driven modelling: The project combines continuum mechanics with machine learning, equipping the student to model complex, multiscale systems using both physics-based and data-driven approaches.

Scientific programming: The student will gain proficiency in simulation and model development using widely adopted tools such as PyBaMM and TensorFlow.

Communication: Clear and effective communication of scientific ideas will be developed through writing articles and preparing presentations for a broad range of audiences.

These skills position you for careers in AI research, computational materials science, national laboratories, tech industry or academic research. The HetSys training provides a foundation for these skills through dedicated courses and cohort activities.

We require at least a II(i) honours degree at BSc or an integrated masters degree (e.g. MPhys, MChem, MSci, MEng etc.) in a physical sciences, mathematics or engineering discipline. We do not accept applications from existing PhD holders.

If you are an overseas candidate please check here that you hold the equivalent grades before applying.

For postgraduate study in HetSys, the term “overseas” or “international” student refers to anyone who does not qualify for UK home fee status. This includes applicants from the European Union (EU), European Economic Area (EEA), and Switzerland, unless they hold settled or pre-settled status under the UK’s EU Settlement Scheme.