Exploring 4D XCT Battery Discharging through Immersive Analytics

Understanding time-varying processes, such as battery discharge mechanisms, is essential for advancing energy storage technologies. While in situ and in operando X-ray computed tomography are key to observing these dynamic processes, they result in large, complex 4D spatial and abstract datasets. Analyzing such complex data becomes cognitively demanding when using conventional visualization methods on traditional 2D desktop-based systems, severely limiting researchers’ abilities. This work leverages immersive analytics to enable the joint exploration of primary data from the imaging process and secondary data, i.e., additionally quantified or derived data, within an augmented reality environment. We introduce metaphors tailormade for interacting with 4D spatiotemporal data in augmented reality systems, in order to facilitate an intuitive inspection of dynamic processes in the spatial domain while simultaneously visualizing temporal changes in derived properties. We apply this new workflow for two challenging use cases drawn directly from current industrial inspection scenarios. Our approach thereby demonstrates how immersive visualization can transform battery research by making complex 4D processes more accessible and comprehensive to materials scientists and stakeholders.