Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Donal Finegan, Hongyi Xu, Juner Zhu, Hongbo Zhao, Xuekun Lu, Wei Li, Nathaniel Hoffman, Antonio Bertei, Paul Shearing, Martin Bazant

Research output: Contribution to journalArticlepeer-review

73 Scopus Citations

Abstract

Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium-ion cells. To facilitate the establishment of microstructure-resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium-ion cells are presented.

Original languageAmerican English
Article number2003908
Number of pages34
JournalAdvanced Energy Materials
Volume11
Issue number19
DOIs
StatePublished - 2021

Bibliographical note

Publisher Copyright:
© 2021 Wiley-VCH GmbH

NREL Publication Number

  • NREL/JA-5700-79891

Keywords

  • computational design
  • electrochemical properties
  • lithium-ion batteries
  • machine learning
  • mechanical properties
  • microscopic imaging
  • multiphysics modeling

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