Artificial intelligence inferred microstructural properties from voltage–capacity curves
Abstract
Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn $$$$_2$$$$ O $$$$_4$$$$ chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 $$R^2$$ score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.
- Authors:
- Publication Date:
- Research Org.:
- Purdue Univ., West Lafayette, IN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Science Foundation (NSF)
- OSTI Identifier:
- 1879796
- Alternate Identifier(s):
- OSTI ID: 1904267
- Grant/Contract Number:
- SC0021142; DMS-1555072; DMS-2053746; DMS-2134209
- Resource Type:
- Published Article
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Name: Scientific Reports Journal Volume: 12 Journal Issue: 1; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 25 ENERGY STORAGE; batteries; computational methods
Citation Formats
Sun, Yixuan, Ayalasomayajula, Surya Mitra, Deva, Abhas, Lin, Guang, and García, R. Edwin. Artificial intelligence inferred microstructural properties from voltage–capacity curves. United Kingdom: N. p., 2022.
Web. doi:10.1038/s41598-022-16942-5.
Sun, Yixuan, Ayalasomayajula, Surya Mitra, Deva, Abhas, Lin, Guang, & García, R. Edwin. Artificial intelligence inferred microstructural properties from voltage–capacity curves. United Kingdom. https://doi.org/10.1038/s41598-022-16942-5
Sun, Yixuan, Ayalasomayajula, Surya Mitra, Deva, Abhas, Lin, Guang, and García, R. Edwin. Thu .
"Artificial intelligence inferred microstructural properties from voltage–capacity curves". United Kingdom. https://doi.org/10.1038/s41598-022-16942-5.
@article{osti_1879796,
title = {Artificial intelligence inferred microstructural properties from voltage–capacity curves},
author = {Sun, Yixuan and Ayalasomayajula, Surya Mitra and Deva, Abhas and Lin, Guang and García, R. Edwin},
abstractNote = {Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn $$_2$$ 2 O $$_4$$ 4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 $$R^2$$ R 2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.},
doi = {10.1038/s41598-022-16942-5},
journal = {Scientific Reports},
number = 1,
volume = 12,
place = {United Kingdom},
year = {Thu Aug 04 00:00:00 EDT 2022},
month = {Thu Aug 04 00:00:00 EDT 2022}
}
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