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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.
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https://hal-u-picardie.archives-ouvertes.fr/hal-03610980
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Soumis le : jeudi 17 mars 2022 - 06:47:23
Dernière modification le : mercredi 28 septembre 2022 - 05:49:23
Archivage à long terme le : : samedi 18 juin 2022 - 18:13:28

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Abbos Shodiev, Marc Duquesnoy, Oier Arcelus, Mehdi Chouchane, Jianlin Li, et al.. Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes. Journal of Power Sources, Elsevier, 2021, 511, ⟨10.1016/j.jpowsour.2021.230384⟩. ⟨hal-03610980⟩

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