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Developing tools to catalogue emissions from thermal events in Li-ion batteries

Project ID: 2228cc1451 (You will need this ID for your application)

Research Theme: Energy and Decarbonisation

UCL Lead department: Chemical Engineering

Department Website

Lead Supervisor: Alex Rettie

Industry partner: HORIBA MIRA

Stipend enhancement: £1,000 pa

Project Summary:

Degradation and failure are unavoidable and critical characteristics governing the manufacture, use and regulation of energy storage devices. As the integration of Net Zero power sources into society expands, it is vital that we fully understand such adverse behaviours to inform policy, engineer solutions and mitigate real-world harm. Over the coming years, regulatory frameworks (e.g., ISO, IEC, DOT, ADR, IATA etc.), will require full life cycle safety for technologies such as Li-ion, solid-state, Li-sulphur and hydrogen fuel cells. This project will contribute to the methods and knowledge available for the development of such safety standards.

You will join a world-leading team based at the Advanced Propulsion Lab: a £50M UCL investment into clean propulsion technologies at the new UCL East campus, and at Horiba-MIRA: an automotive company specialising in vehicle engineering, research and product testing. We are looking for scientists and engineers who want to be on the forefront of clean economy.

The aim is to thoroughly classify emissions from Li-ion cells. Practically, this will involve experimental gas and particulate measurement techniques during abusive characterisation of batteries, e.g., mass spectrometry, gas chromatography, FTIR spectroscopy, particle size distribution via laser scattering. Cell testing might include stresses such as over/under-voltage/current, high/low temperature, mechanical/pressure/impact, penetration and short-circuit. There will be a focus on high-throughput experiments to build up robust datasets for analyses.

In addition, a broader range of sensors may be employed, including electrochemical/voltage, temperature, acoustic and pressure. These may be used to train machine-learning models via sensor fusion, i.e., combining numerous signals to look for novel signatures that conventional analyses might not have captured. We will determine metrics to disseminate as key factors in categorising the behaviour and acceptability of a given device or technology for consumer uptake in the transition to Net Zero.