Stochastic reconstruction and performance prediction of cathode microstructures based on deep learning

Xinwei Yang, Chunwang He*, Le Yang*, Wei Li Song, Hao Sen Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The effective properties of lithium-ion battery (LIB) cathode are determined by both the volume fractions of constituents and the morphological features of microstructure. However, it is difficult to establish an accurate quantitative relationship between the macroscopic effective properties and microstructural features. Deep learning techniques, due to their exceptional nonlinear fitting capabilities, have been widely applied in various complex fields. Our study presents a generation scheme of numerous three-dimensional (3D) digital microstructures of cathode, using a deep convolutional neural network (CNN)-based stochastic reconstruction algorithm combining with the scanning electron microscope (SEM) images. The reconstructed samples are substituted with the corresponding finite element (FE) models, and the effective mechanical and electrochemical properties are assessed through the FE-based homogenization theory. Finally, the generated cathode samples and their effective properties are used to train the 3D CNN for performance prediction. This study demonstrates that the deep learning approaches can accurately and rapidly reconstruct the microstructure of cathode and predict their effective properties. Furthermore, the established framework can be extended to other heterogeneous materials.

Original languageEnglish
Article number234410
JournalJournal of Power Sources
Volume603
DOIs
Publication statusPublished - 30 May 2024

Keywords

  • 3D CNN
  • Cathode microstructure
  • Effective properties
  • Stochastic reconstruction

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Yang, X., He, C., Yang, L., Song, W. L., & Chen, H. S. (2024). Stochastic reconstruction and performance prediction of cathode microstructures based on deep learning. Journal of Power Sources, 603, Article 234410. https://doi.org/10.1016/j.jpowsour.2024.234410