Despite numerous studies presenting advances in tomographic imaging and analysis of lithium ion batteries, graphite-based anodes have received little attention. Weak X-ray attenuation of graphite and, as a result, poor contrast between graphite and the other carbon-based components in an electrode pore space renders data analysis challenging. Here we demonstrate operando tomography of weakly attenuating electrodes during electrochemical (de)lithiation. We use propagation-based phase contrast tomography to facilitate the differentiation between weakly attenuating materials and apply digital volume correlation to capture the dynamics of the electrodes during operation. After validating that we can quantify the local electrochemical activity and microstructural changes throughout graphite electrodes, we apply our technique to graphite-silicon composite electrodes. We show that microstructural changes that occur during (de)lithiation of a pure graphite electrode are of the same order of magnitude as spatial inhomogeneities within it, while strain in composite electrodes is locally pronounced and introduces significant microstructural changes.

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It is an open question how the particle microstructure of a lithium-ion electrode influences a potential thermal runaway. In order to investigate this, information on the structural changes, in particular cracked particles, caused by the failure are desirable. For a reliable analysis of these changes a reasonably large amount of data is necessary, which necessitates automatic extraction of particle cracks from tomographic 3D image data. In this paper, a classification model is proposed which is able to decide whether a pair of particles is the result of breakage, of the image segmentation, or neither. The classifier is developed using simulated data based on a 3D stochastic particle model. Its validity is tested by applying the methodology to hand-labelled data from a real electrode. For this dataset, an overall accuracy of 73% is achieved.

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Thermodynamically consistent transport theory is used to compare 3D images of real anode microstructures from lithium-ion batteries to virtual ones created by a parametric stochastic 3D microstructure model. Half-cell simulations in 3D with spatially resolved microstructures at different applied currents show that for low currents the deviations between various electrochemical quantities like current density or overpotential are negligibly small. For larger currents small differences become more pronounced. Qualitative and quantitative differences of these features are discussed with respect to the microstructure and it is shown that the real and virtual structures behave similar during electrochemical simulations. Extensions of the stochastic microstructure model, which overcome small differences in electrochemical behavior, are proposed.