Christina Sauter

Accurate 3D representations of lithium-ion battery electrodes, in which the active particles, binder and pore phases are distinguished and labeled, can assist in understanding and ultimately improving battery performance. Here, we demonstrate a methodology for using deep-learning tools to achieve reliable segmentations of volumetric images of electrodes on which standard segmentation approaches fail due to insufficient contrast. We implement the 3D U-Net architecture for segmentation, and, to overcome the limitations of training data obtained experimentally through imaging, we show how synthetic learning data, consisting of realistic artificial electrode structures and their tomographic reconstructions, can be generated and used to enhance network performance. We apply our method to segment x-ray tomographic microscopy images of graphite-silicon composite electrodes and show it is accurate across standard metrics. We then apply it to obtain a statistically meaningful analysis of the microstructural evolution of the carbon-black and binder domain during battery operation.

Filling of the electrode and the separator with an electrolyte is a crucial step in the lithium ion battery manufacturing process. Incomplete filling negatively impacts electrochemical performance, cycle life, and safety of cells. Here, we apply concepts from the theory of partial wetting to explain the amount of gas entrapment that occurs during electrolyte infilling and show that this can explain the lower than expected effective transport coefficients that are measured experimentally. We consider a polyethylene separator as a model system. Quasi-static infilling simulations on 3D reconstructions of the separator structure indicate that there can be up to 30% gas entrapment upon infilling due to the geometry of the separator, which results in a reduction of effective transport by >40%. Considering the dynamics of the electrolyte (e.g., viscosity) and the infilling process explains why the residual gas phase is typically less (15%–20%) and why, for electrolytes that wet well, increasing viscosity leads to higher values of gas entrapment, which is observed experimentally as decreased effective electrolyte conductivity. This work highlights the importance of optimizing not only the physiochemical properties of the electrolyte and pore surfaces, but also the 3D structure of the pore space, providing insights how to do so.