Physics-Based Modelling of Lithium-Ion Batteries for Electrochemical and Thermal Optimization

Proposed Faculty Supervisors: Dibakar Rakshit, M. Ali Haider

This PhD project proposes an integrated multiscale approach to enhance lithium-ion battery (LiB) performance by linking material-level modelling with system-level thermal management. Density Functional Theory (DFT) calculations will provide key atomic-scale insights, such as diffusion barriers, thermodynamic properties, and reaction energetics, which will be used to derive material parameters like diffusion coefficients, entropic heat contributions, and inform models of reaction kinetics across different states of charge and temperature. These parameters will be incorporated into physics-based electrochemical-thermal models to accurately simulate charge transport, reaction dynamics, and internal heat generation under realistic conditions. By grounding the modelling framework in material-specific properties, the project aims to predict and minimize the primary sources of heat generation within the battery, providing a deeper understanding of how material choices and operating protocols influence overall thermal behaviour.

Building on these insights, the project will focus on reducing the reliance on external thermal management systems by engineering batteries that inherently generate less heat during operation. Instead of reacting to temperature rise through heavy cooling, this approach proactively minimizes internal thermal stresses, improving energy efficiency, extending battery life, and enhancing safety. The combined methodology�starting from atomistic DFT modelling, transitioning to refined physics-based simulation, and culminating in optimized thermal strategies�will create a comprehensive pathway for designing thermally efficient lithium-ion batteries suitable for next-generation Battery Electric Vehicles (BEVs).

Background required: A Bachelor�s or Master�s degree in Energy Engineering, Mechanical Engineering, Chemical Engineering, Materials Science, or a closely related field is required. Candidates should possess strong analytical skills and foundational knowledge in thermodynamics, electrochemistry, and numerical modelling, with a keen interest in multiscale simulation and battery technologies.