Towards inline ultrasonic characterisation of battery slurry mixing: opportunities, challenges, and perspectives

Abstract

Inefficiencies in the slurry mixing stage are a major factor in high scrap rates in battery manufacturing, thus hindering sustainable production. Current offline characterisation techniques for slurry microstructure and rheology are slow and inadequate for closed-loop quality control or process optimisation. This review evaluates ultrasound as a promising inline, non-invasive characterisation tool to address this crucial need. We critically examine decades of developments and applications in ultrasonic evaluation techniques, assessing their relevance and identifying challenges specific to the high-concentration, non-Newtonian battery slurries. Key wave-slurry interaction mechanisms, including attenuation, wave speed, scattering, and guided wave propagation, are discussed in the context of characterising microstructural features (e.g. particle dispersion and agglomeration) and macroscopic rheological properties (e.g. viscosity and viscoelasticity). To make full use of the crucial yet limited information accessible via ultrasound, we propose a hybrid framework marrying ultrasonic and other offline data through physics-informed machine learning for accurate and comprehensive property estimation. With the analyses and framework, this review points to a clear path towards achieving robust inline monitoring and efficient optimisation of battery slurry mixing.