Lithium-ion batteries are a key technology in the energy and mobility transition. Therefore, recycling processes for these batteries are becoming increasingly important from both ecological and economic perspectives. However, current processes only recover the solid components of batteries, while the organic electrolyte solvents (OES) are sent for thermal recovery. Recovering the OES components is challenging because ageing and decomposition processes during the battery’s lifetime give rise to a large number of secondary components, some of which are unknown. Developing sustainable OES recycling processes therefore requires tools for efficiently characterising and thermodynamically modelling complex mixtures.

My project investigates how nuclear magnetic resonance (NMR) experiments and modern machine learning methods can reliably and automatically determine the composition of OES at the end of a battery’s lifetime. The methodology is being developed so that the resulting characterisation of the mixtures enables direct thermodynamic modelling of the OES, which in turn serves as the basis for designing suitable OES recycling processes.

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