Stochastic 3D microstructure modeling of anodes in lithium-ion batteries with a particular focus on local heterogeneity

Lithium-ion batteries can be considered as one of the most important energy storing devices. To satisfy the rapidly growing demand for higher energy densities, as for example required by automotive applications, the optimization of the electrode morphology is an important goal in battery research since it is well known that the 3D microstructure of anodes and cathodes has a significant impact on the resulting performance of the battery. A promising approach is called virtual materials testing, where stochastic 3D microstructure models are used to generate a wide range of virtual but realistic electrode morphologies as structural input for spatially-resolved numerical simulations of effective electrochemical properties. This beneficial combination allows to derive microstructure?property relationships just at the cost of computer simulations. The present paper introduces a novel parametric stochastic 3D microstructure model based on random fields that is calibrated to tomographic image data of six graphite anodes. The model is validated by comparing geometrical characteristics and effective tortuosity, which significantly influences the electrochemical behaviour of battery electrodes, computed for tomographic and simulated image data, respectively. A particular focus is put on local heterogeneity, which is quantitatively accessed by computing local distributions of certain microstructure-dependent descriptors.

Automated summary

The paper introduces a novel parametric stochastic 3D microstructure model based on random fields for optimizing electrode morphology and studying electrochemical properties. The microstructure-property relationships are derived and validated by combining virtual materials testing and computer simulations, with a specific focus on local heterogeneity of battery electrodes.