Attention-Enhanced Damage Prediction in Electrode Materials From Simulation Video Sequences

IEEE Transactions on Automation Science and Engineering, vol. 23, pp. 2734–2746

Abstract

Accurate prediction of damage in electrode materials is crucial to automating reliability management for lithium-ion batteries. Phase field-based simulations offer valuable insight into the chemomechanical processes and are nowadays often used as substitutes in lieu of physical microscopy data for developing fracture detection and tracking algorithms. Even for simulated phase-field videos, the data amount is still limited, making a machine learning task challenging and the traditional approaches less effective. This study proposes an attention-enhanced approach that applies physics-informed attention operations under deep learning-based image prediction models. Using synchronized stress and damage field data, the proposed method is able to focus effectively on critical fracture regions to improve spatio-temporal prediction accuracy despite the limited amount of training data. Experimental results demonstrate the robustness and effectiveness of the proposed attention-enhanced approach, which could offer a scalable solution for real-time fracture analysis and for vision systems for in-line metrology in the long run. Note to Practitioners—Machine learning approaches are finding increasing application in material science and engineering problems nowadays. One bottleneck encountered in battery reliability management is the limitation of data amount, not only from physical experiments but also from large-scale physics-based simulations. Researchers and practitioners argue for the use of attention mechanisms to alleviate data scarcity challenges but still face the conundrum that whilst data scarcity strongly requires focus, a lot of training data is needed to know where to focus. Our study resolves this conundrum by combining domain information with machine learning and identifying the critical regions for focus without requiring a large amount of training data. This approach mirrors how a skilled engineer would prioritize areas of concern in material degradation analysis. This study will help practitioners develop scalable and data-efficient solutions that can eventually lead to better battery management towards reliability and longevity.

Authors 5

1. Quan Zeng

   Georgia Institute of Technology

   Affiliation as printed

   Stewart School of Industrial and Systems Engineering, Georgia Tech, Atlanta, GA, USA

2. Shahed Rezaei Aachen Access e.V

   Access

   Affiliation as printed

   Access e.V., Aachen, Germany

3. Bai‐Xiang Xu

   Technische Universität Darmstadt

   Affiliation as printed

   Institute of Materials Science, Division Mechanics of Functional Materials, Technische Universität Darmstadt, Darmstadt, Germany

4. Sarbajit Banerjee

   ETH Zurich · Laboratory of Inorganic Chemistry

   Affiliation as printed

   Department of Chemistry and Applied Biosciences, Laboratory for Inorganic Chemistry, ETH Zürich, Zürich, Switzerland

5. Yu Ding

   Georgia Institute of Technology

   Affiliation as printed

   Stewart School of Industrial and Systems Engineering, Georgia Tech, Atlanta, GA, USA