Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning
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.
- Authors:
-
- Univ. of Connecticut, Storrs, CT (United States)
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Univ. College London (United Kingdom); The Faraday Institution, Didcot, OX (United Kingdom); National Physical Lab., Teddington (United Kingdom)
- Univ. of Pisa (Italy)
- Univ. College London (United Kingdom); The Faraday Institution, Didcot, OX (United Kingdom)
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States); Univ. of Connecticut, Storrs, CT (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO); USDOE
- OSTI Identifier:
- 1781629
- Alternate Identifier(s):
- OSTI ID: 1786227; OSTI ID: 2205157
- Report Number(s):
- NREL/JA-5700-79891
Journal ID: ISSN 1614-6832; MainId:39109;UUID:02eb1560-26ce-4ecd-bdd7-c5f8bad8beda;MainAdminID:22366
- Grant/Contract Number:
- AC36-08GO28308; DE‐AC36‐08GO28308; EE0008302
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Advanced Energy Materials
- Additional Journal Information:
- Journal Volume: 11; Journal Issue: 19; Journal ID: ISSN 1614-6832
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 25 ENERGY STORAGE; computational design; electrochemical properties; lithium-ion batteries; machine learning; mechanical properties; microscopic imaging; multiphysics modeling
Citation Formats
Xu, Hongyi, Zhu, Juner, Finegan, Donal P., Zhao, Hongbo, Lu, Xuekun, Li, Wei, Hoffman, Nathaniel, Bertei, Antonio, Shearing, Paul, and Bazant, Martin Z. Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning. United States: N. p., 2021.
Web. doi:10.1002/aenm.202003908.
Xu, Hongyi, Zhu, Juner, Finegan, Donal P., Zhao, Hongbo, Lu, Xuekun, Li, Wei, Hoffman, Nathaniel, Bertei, Antonio, Shearing, Paul, & Bazant, Martin Z. Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning. United States. https://doi.org/10.1002/aenm.202003908
Xu, Hongyi, Zhu, Juner, Finegan, Donal P., Zhao, Hongbo, Lu, Xuekun, Li, Wei, Hoffman, Nathaniel, Bertei, Antonio, Shearing, Paul, and Bazant, Martin Z. Thu .
"Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning". United States. https://doi.org/10.1002/aenm.202003908. https://www.osti.gov/servlets/purl/1781629.
@article{osti_1781629,
title = {Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning},
author = {Xu, Hongyi and Zhu, Juner and Finegan, Donal P. and Zhao, Hongbo and Lu, Xuekun and Li, Wei and Hoffman, Nathaniel and Bertei, Antonio and Shearing, Paul and Bazant, Martin Z.},
abstractNote = {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.},
doi = {10.1002/aenm.202003908},
journal = {Advanced Energy Materials},
number = 19,
volume = 11,
place = {United States},
year = {Thu Apr 08 00:00:00 EDT 2021},
month = {Thu Apr 08 00:00:00 EDT 2021}
}
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