Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks

Journal Article · Fri Feb 09 00:00:00 EST 2024 · Energy Storage Materials

DOI:https://doi.org/10.1016/j.ensm.2024.103251· OSTI ID:2320335

Norris, Chance Adison ^[1]; Ayyaswamy, Abhinand ^[1]; Vishnugopi, Bairav S. ^[1]; Martinez, Carianne ^[2]; Roberts, Scott A. ^[2]; Mukherjee, Partha P. ^[1]

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1. Purdue University, West Lafayette, IN (United States)
2. Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: NA0003525
• OSTI ID: 2320335
• Alternate ID(s): OSTI ID: 2369871
• Report Number(s): SAND--2024-02784J
• Journal Information: Energy Storage Materials, Journal Name: Energy Storage Materials Journal Issue: 1 Vol. 67; ISSN 2405-8297
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

25 ENERGY STORAGE
deep learning
effective electrode property
graphite electrode
lithium-ion battery
uncertainty quantification