The Bruggeman model is routinely employed to determine transport parameters in macroscale electrochemical models. Yet, it relies on both a simplified representation of the pore-scale structure and specific hypotheses on the transport dynamics at the pore scale. Furthermore, its inherent scalar nature prevents it from capturing the impact that pore-structure anisotropy has on transport. As a result, the complex topology of electrochemical storage devices, combined with the broad range of conditions in which batteries operate, renders the Bruggeman relationship approximate, at best. We propose a self-consistent multiscale framework, based on homogenization theory, which a priori allows one to calculate effective parameters of battery electrodes for a range of transport regimes while accounting for full topological information at the pore scale. The method is based on the solution of a closure problem on a translationally periodic unit cell and generalized to handle locally non-periodic structures. We compare the Bruggeman and the closure-problem predictions of the effective diffusivity for a set of 18,000 synthetically generated images and propose a data-driven polynomial function correlating porosity and effective diffusivity, as calculated from a solution of the closure problem. We test its predictive capability against measured diffusivity values in a LiCoO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document} cathode and a Ni-YSZ anode.

中文翻译：

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一种数据驱动的多尺度框架，用于从微结构图像估计锂离子电池的有效性能

Bruggeman 模型通常用于确定宏观电化学模型中的传输参数。然而，它既依赖于孔隙尺度结构的简化表示，也依赖于孔隙尺度输运动力学的特定假设。此外，其固有的标量性质使其无法捕捉孔隙结构各向异性对传输的影响。因此，电化学存储设备的复杂拓扑结构，加上电池运行的广泛条件，充其量只能使布鲁格曼关系近似。我们提出了一种基于均质化理论的自洽多尺度框架，该框架先验地允许人们计算电池电极在一系列传输状态下的有效参数，同时考虑到孔隙尺度的完整拓扑信息。该方法基于平移周期晶胞上的闭合问题的解决方案，并推广到处理局部非周期结构。我们比较了一组 18,000 张合成生成的图像的有效扩散率的 Bruggeman 和闭合问题预测，并提出了一个数据驱动的多项式函数，该函数将孔隙率和有效扩散率相关联，根据闭合问题的解决方案计算。我们根据 LiCoO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} 中测量的扩散率值测试其预测能力\usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document} 阴极和 Ni-YSZ 阳极。