Improved Rate Capability for Dry Thick Electrodes through Finite Elements Method and Machine Learning Coupling

doi:10.1021/acsenergylett.4c00203

Improved Rate Capability for Dry Thick Electrodes through Finite Elements Method and Machine Learning Coupling

2024-03-12

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DOI：10.1021/acsenergylett.4c00203

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A coupled finite elements method (FEM) and machine learning (ML) workflow is presented to optimize the rate capability of thick positive electrodes (ca. 150 mu m and 8 mAh/cm(2)). An ML model is trained based on the geometrical observables of individual LiNi0.8Mn0.1Co0.1O2 particles and their average state of discharge (SOD) predicted from FEM modeling. This model not only bypasses lengthy FEM simulations but also provides deeper insights on the importance of pore tortuosity and the active particle size, identified as the limiting phenomenon during the discharge. Based on these findings, a bilayer configuration is proposed to tackle the identified limiting factors for the rate capability. The benefits of this structured electrode are validated through FEM by comparing its performance to a pristine monolayer electrode. Finally, experimental validation using dry processing demonstrates a 40% higher volumetric capacity of the bilayer electrode when compared to the previously reported thick NMC electrodes.

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Improved Rate Capability for Dry Thick Electrodes through Finite Elements Method and Machine Learning Coupling

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