A data-driven modeling approach is presented to quantify the influence of morphology on effective properties in nanostructured sodium vanadium phosphate $\mathrm{Na}_3\mathrm{V}_2(\mathrm{PO}_4)_3$/ carbon composites (NVP/C), which are used as cathode material in sodium-ion batteries. This approach is based on the combination of advanced imaging techniques, experimental nanostructure characterization and stochastic modeling of the 3D nanostructure consisting of NVP, carbon and pores. By 3D imaging and subsequent post-processing involving image segmentation, the spatial distribution of NVP is resolved in 3D, and the spatial distribution of carbon and pores is resolved in 2D. Based on this information, a parametric stochastic model, specifically a Pluri-Gaussian model, is calibrated to the 3D morphology of the nanostructured NVP/C particles. Model validation is performed by comparing the nanostructure of simulated NVP/C composites with image data in terms of morphological descriptors which have not been used for model calibration. Finally, the stochastic model is used for predictive simulation to quantify the effect of varying the amount of carbon while keeping the amount of NVP constant. The presented methodology opens new possibilities for a ressource-efficient optimization of the morphology of NVP/C particles by modeling and simulation.

翻译：提出了一种数据驱动建模方法，用于量化形貌对钠离子电池正极材料——纳米结构磷酸钒钠（$\mathrm{Na}_3\mathrm{V}_2(\mathrm{PO}_4)_3$/碳复合材料，NVP/C）有效性质的影响。该方法结合了先进成像技术、实验纳米结构表征以及由NVP、碳和孔隙组成的三维纳米结构的随机建模。通过三维成像及后续的图像分割处理，在三维空间中解析了NVP的空间分布，并在二维空间中解析了碳和孔隙的空间分布。基于此信息，校准了一种参数化随机模型（具体为Pluri-Gaussian模型）以匹配纳米结构NVP/C颗粒的三维形貌。通过比较模拟NVP/C复合材料的纳米结构与图像数据中未用于模型校准的形貌描述符进行模型验证。最后，利用该随机模型进行预测性模拟，量化在保持NVP含量不变时改变碳含量产生的影响。所述方法为通过建模与仿真实现NVP/C颗粒形貌的资源高效优化开辟了新可能性。