The next generation of batteries, which includes developments to Li-ion technologies, must be lighter, have higher energy densities, last longer and use less of the critical raw materials found in the technologies of today. This will require rapid innovations in materials design and electrode engineering to ensure this next generation of battery materials are well-suited to the ever increasing technological demands. Battery intelligence is a key pathway to achieving these urgently-needed innovations.

A critical bottleneck in the acceleration of all battery chemistries is the lengthy cycle of ideation, manufacture and testing of novel electrode architectures. This is currently mostly done experimentally, which is a time- and resource-intensive process and requires significant resources in the research laboratory, continually iterating electrode design parameters. Furthermore, parameterisation of electrode materials often require costly, intricate techniques like FIB-SEM, X-ray CT or synchrotron beamline access, which themselves have issues in terms of the complexity of datasets and the human-intensive analysis required.

This means that the parameters extracted require substantial human intervention and there are questions surrounding the repeatability and standardisation of extracted parameters. Finally, because of the large amounts of data that are collected during complex in-situ experiments, particularly at beamlines, the lack of suitable, reliable processing methods could mean that key information about battery electrode degradation mechanisms, failure modes or morphology and structural changes can be lost from these multidimensional datasets during processing.

By developing novel algorithms, accelerating physics-based model creation and robustly and repeatably linking these to tomography datasets, electrochemical performance data can be accurately correlated to tomographic measurement. This will enable a significant boost in optimising electrode material for specific electrochemical performance requirements, such as energy density, power, and lifetime. By using in-situ environmental testing facilities within the Sheffield Tomography Centre, coupled with the advanced algorithms for data processing, rapid analysis on battery materials can be done.