Mapping the Architecture of Single Lithium Ion Electrode Particles in 3D, Using Electron Backscatter Diffraction and Machine Learning Segmentation

Orkun Furat, Donal Finegan, David Diercks, Francois Usseglio-Viretta, Kandler Smith, Volker Schmidt

Research output: Contribution to journalArticlepeer-review

37 Scopus Citations

Abstract

Accurately quantifying the architecture of lithium ion electrode particles in 3D is critical to understanding sub-particle lithium transport, rate limitations, and degradation mechanisms within lithium ion batteries. Most commercial positive electrode materials consist of polycrystalline particles, where intra-particle grains have a range of morphologies and orientations. Here, focused ion beam slicing in sequence with electron backscatter diffraction is used to accurately quantify intra-particle grain morphologies in 3D. The intra-particle grains are identified using convolution neural network segmentation and distinctly labeled. Efficient morphological characterization of the grain architectures is achieved. Bivariate probability density maps are developed to show correlative relationships between morphological grain descriptors. The implication of morphological features on cell performance, as well as the extension of this dataset to guide artificial generation of realistic particle architectures for 3D multi-physics models, is discussed.

Original languageAmerican English
Article number229148
Number of pages12
JournalJournal of Power Sources
Volume483
DOIs
StatePublished - 31 Jan 2021

Bibliographical note

Publisher Copyright:
© 2020 The Authors

NREL Publication Number

  • NREL/JA-5700-77489

Keywords

  • Convolutional neural network
  • Copula
  • Electron backscatter diffraction
  • Lithium ion battery
  • Model fitting
  • Statistical image analysis

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