Stochastic model for the 3D microstructure of pristine and cyclically aged cathodes in Li-ion batteries

TL;DR

This paper introduces a stochastic 3D microstructure model for lithium-ion battery cathodes, calibrated with tomographic data, to better understand aging effects and generate realistic virtual structures.

Contribution

It presents a novel stochastic modeling approach for cathode microstructures, extending previous anode models, and validates it against real 3D tomographic data for pristine and aged cathodes.

Findings

Model accurately describes microstructures of pristine and aged cathodes

Model can generate realistic virtual cathode microstructures

Model enhances understanding of aging effects in cathodes

Abstract

It is well-known that the microstructure of electrodes in lithium-ion batteries strongly affects their performance. Vice versa, the microstructure can exhibit strong changes during the usage of the battery due to aging effects. For a better understanding of these effects, mathematical analysis and modeling has turned out to be of great help. In particular, stochastic 3D microstructure models have proven to be a powerful and very flexible tool to generate various kinds of particle-based structures. Recently, such models have been proposed for the microstructure of anodes in lithium-ion energy and power cells. In the present paper, we describe a stochastic modeling approach for the 3D microstructure of cathodes in a lithium-ion energy cell, which differs significantly from the one observed in anodes. The model for the cathode data enhances the ideas of the anode models, which have been developed so far. It is calibrated using 3D tomographic image data from pristine as well as two aged cathodes. A validation based on morphological image characteristics shows that the model is able to realistically describe both, the microstructure of pristine and aged cathodes. Thus, we conclude that the model is suitable to generate virtual, but realistic microstructures of lithium-ion cathodes.