Virtual GregAI

The work in brief

Greg's work addresses how lithium-ion and related batteries perform, heat up, degrade and fail, and how cells, packs and models can be designed to manage this. The papers span degradation mechanisms (SEI growth, lithium plating, particle cracking, loss of active silicon, high-nickel cathode phase change), thermal behaviour and its coupling to ageing, fast charging and low-temperature operation, and safety from thermal runaway to self-heating ignition of degraded cells. Methods include physics-based electrochemical–thermal–mechanical models, often implemented in PyBaMM, and equivalent circuit network models such as PyECN. These are paired with experimental techniques such as differential thermal voltammetry, differential voltage analysis, degradation mode analysis, isothermal test rigs, the cell cooling coefficient metric and in-operando X-ray tomography.

The earliest papers (2009–2013) compare battery electric, fuel cell and hybrid vehicles and examine thermal gradients and fault diagnosis in packs. From about 2015 the work moves through lithium-sulfur modelling and low-cost diagnostics to standardised thermal metrics for cell design (2019–2021). Since then the emphasis has been on coupled degradation modelling validated with degradation mode analysis, composite silicon–graphite anodes, and high-throughput and neural-operator simulation. Alongside this run life-cycle emissions, recycling economics and multi-criteria comparisons of battery chemistries, with recent titles turning to onboard silicon diagnostics and zero-degradation cell design.

AI-written from the paper titles and abstracts; Greg hasn't reviewed it.