Abstract
The electrochemical performance of lithium-ion batteries strongly relies upon the composition and microstructure of the porous electrodes. An electrochemically-coupled set of transport processes underlie the composite electrodes depending on the microstructural arrangement of its constituent material phases. However, the intrinsically stochastic, anisotropic and heterogeneous nature of these porous electrodes makes the characterization of their effective transport properties quite complex. In this work, we examine the use of machine learning as a viable route for fast and accurate abstraction of effective electrode properties, e.g. the tortuosity and effective electronic conductivity of exemplar electrode microstructures based on three-dimensional X-ray tomography images. The proposed computational framework can be generally extended to characterize various modes of transport resistances in porous media.
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