Microstructure Characterization of Battery Materials Based on Voxelated Image Data: Computation of Active Surface Area and Tortuosity

Abstract

The reliable computation of microstructure metrics such as specific surface area and tortuosity factors is key to bridge the gap between the battery microscale and fast, homogenized cell models. In this work, we present an approach to compute the surface area of phases based on pixelated image data which is both easy-to-implement and computationally efficient. The concept is inspired from the diffuse surface representation in phase-field methods. Subsequently, the approach is validated and compared with common python libraries on two benchmark cases and actual battery microstructure data. The results underline the reliability and fast computational performance of the approach. Furthermore, the concept of through-feature connectivity in pixelated image data is introduced and explored to quantify the reliability of tortuosity factor computations. Overall, this work enhances the computational tools to bridge the scale from battery microstructures to cell models and gives an overview of state-of-the-art methodology. The developed code is published to further accelerate the scientific progress in this field.