Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes

Shodiev, Abbos; Duquesnoy, Marc; Arcelus, Oier; Chouchane, Mehdi; Li, Jianlin; Franco, Alejandro A.

doi:10.1016/j.jpowsour.2021.230384

Title: Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes

Abstract

Electrolyte infiltration is one of the critical steps of the manufacturing process of lithium ion batteries (LIB). We present here an innovative machine learning (ML) model, based on the multi-layers perceptron (MLP) approach, to fast and accurately predict electrolyte flow in three dimensions, as well as wetting degree and time for LIB electrodes. The ML model is trained on a database generated using a 3D-resolved physical model based on the Lattice Boltzmann Method (LBM) and a NMC electrode mesostructure obtained by X-ray micro-computer tomography. The trained ML model is able to predict the electrode filling process, with ultralow computational cost and with high accuracy. Also, systematic sensitivity analysis was carried out to unravel the spatial relationship between electrode mesostructure parameters and predicted infiltration process characteristics. This paves the way towards massive computational screening of electrode mesostructures/electrolyte pairs to unravel their impact on the cell wetting and optimize the infiltration conditions.

Authors:

Shodiev, Abbos; Duquesnoy, Marc; Arcelus, Oier; Chouchane, Mehdi; Li, Jianlin;

Publication Date:: Mon Nov 01 00:00:00 EDT 2021

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office; European Research Council (ERC)

OSTI Identifier:: 1818194

Alternate Identifier(s):: OSTI ID: 1820761

Grant/Contract Number:: 957189; 772873; AC05-00OR22725

Resource Type:: Published Article

Journal Name:: Journal of Power Sources

Additional Journal Information:: Journal Name: Journal of Power Sources Journal Volume: 511 Journal Issue: C; Journal ID: ISSN 0378-7753

Publisher:: Elsevier

Country of Publication:: Netherlands

Language:: English

Subject:: 25 ENERGY STORAGE; Lithium ion batteries; Electrolyte infiltration; Cell wetting; Machine learning; Lattice Boltzmann method

Citation Formats


                    Shodiev, Abbos, Duquesnoy, Marc, Arcelus, Oier, Chouchane, Mehdi, Li, Jianlin, and Franco, Alejandro A. Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes.  Netherlands: N. p., 2021. 
Web.  doi:10.1016/j.jpowsour.2021.230384.

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                    Shodiev, Abbos, Duquesnoy, Marc, Arcelus, Oier, Chouchane, Mehdi, Li, Jianlin, & Franco, Alejandro A. Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes.  Netherlands.  https://doi.org/10.1016/j.jpowsour.2021.230384

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                    Shodiev, Abbos, Duquesnoy, Marc, Arcelus, Oier, Chouchane, Mehdi, Li, Jianlin, and Franco, Alejandro A. Mon .  
"Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes".  Netherlands.  https://doi.org/10.1016/j.jpowsour.2021.230384.

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@article{osti_1818194,

  title        = {Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes},

  author       = {Shodiev, Abbos and Duquesnoy, Marc and Arcelus, Oier and Chouchane, Mehdi and Li, Jianlin and Franco, Alejandro A.},

  abstractNote = {Electrolyte infiltration is one of the critical steps of the manufacturing process of lithium ion batteries (LIB). We present here an innovative machine learning (ML) model, based on the multi-layers perceptron (MLP) approach, to fast and accurately predict electrolyte flow in three dimensions, as well as wetting degree and time for LIB electrodes. The ML model is trained on a database generated using a 3D-resolved physical model based on the Lattice Boltzmann Method (LBM) and a NMC electrode mesostructure obtained by X-ray micro-computer tomography. The trained ML model is able to predict the electrode filling process, with ultralow computational cost and with high accuracy. Also, systematic sensitivity analysis was carried out to unravel the spatial relationship between electrode mesostructure parameters and predicted infiltration process characteristics. This paves the way towards massive computational screening of electrode mesostructures/electrolyte pairs to unravel their impact on the cell wetting and optimize the infiltration conditions.},

  doi          = {10.1016/j.jpowsour.2021.230384},

  journal      = {Journal of Power Sources},

  number       = C,

  volume       = 511,

  place        = {Netherlands},

  year         = {Mon Nov 01 00:00:00 EDT 2021},

  month        = {Mon Nov 01 00:00:00 EDT 2021}

}

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Title: Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes

Abstract

Citation Formats

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