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Title: MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

Conference ·
ORCiD logo [1];  [2]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
  2. Virginia Tech, Blacksburg, VA
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Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Electricity (OE); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
DOE Contract Number:
AC05-00OR22725
OSTI ID:
1965258
Resource Relation:
Conference: IEEE International Conference on Big Data - Osaka, , Japan - 12/17/2022 10:00:00 AM-12/20/2022 10:00:00 AM
Country of Publication:
United States
Language:
English

References (13)

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book November 2015
Morphology of nanoporous carbon-binder domains in Li-ion batteries—A FIB-SEM study journal November 2015
MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation journal January 2020
Visualizing the Carbon Binder Phase of Battery Electrodes in Three Dimensions journal July 2018
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Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes journal May 2020
Fully convolutional networks for semantic segmentation conference June 2015

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