Physics-informed Deep Learning for Multi-scale Design and Optimisation of Advanced Sodium-ion Battery

This project provides an opportunity to work on a truly innovative project in the renewable energy storage area. The electricity generated by wind farms and solar panels has provided 10% of the world’s power consumption and is still increasing now.

However, the stability of wind and solar energy is a challenging issue which needs to be addressed by deploying high-efficiency energy storage systems nearby them. As a promising electrochemical energy storage technology, Sodium-ion (Na-ion) batteries have attracted rapidly increasing interest because of their higher safety than Li-ion batteries and the high Na volume present in the earth.

The project focuses on the nano/microstructures in the porous battery electrodes, where the interplay between complex morphology and electrochemical reactions predominantly determines the battery performance. Understanding how the Na-ion transport mechanism is challenging but relevant to optimise the electrode microstructures and eventually provides the structural feedback to the electrode manufacturing. Thus, both numerical modelling and experimental investigation are necessary to tackle the issue.

The project aims to advance the performance of Sodium-ion batteries by optimising their electrode nano/micro microstructures, underpinned by advanced modelling and imaging techniques.

During the project, you will perform cutting-edge research in multi-scale modelling (e.g., molecular dynamics and Lattice Boltzmann Method (LBM)) and physics-informed deep learning to reveal the structure-function relationship for Na-ion battery electrodes. You will learn how to characterise batteries by the combination of advanced imaging facilities in Loughborough Materials Characterisation Centre and diverse testing facilities in AAE.

You also will have opportunities to attend international conferences to disseminate your research and communicate with researchers worldwide.

Supervisors

Primary supervisor: Dr Zhiqiang Niu

Secondary supervisors: Dr Ashley Fly

Entry requirements for United Kingdom

Students should have a degree in Chemical Engineering, Physics or any other relevant areas with a 2:1 or higher.

English language requirements

Applicants must meet the minimum English language requirements. Further details are available on the International website.

Find out more about research degree funding

How to apply

All applications should be made online and must include a research proposal. Under the programme name, select 'Chemical Engineering'. Please quote the advertised reference number AACME-23-021 in your application.

To avoid delays in processing your application, please ensure that you submit the minimum supporting documents.

Apply now