Seeing the unseen: real-time tracking of battery cycling-to-failure via surface strain
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Passive failures in lithium-ion batteries, driven by cumulative degradation, present serious safety risks due to their spontaneity and asymptomatic progression. Conventional electrochemical diagnostics cannot differentiate passive failures from routine degradation, while temperature responds too late for early detection. Here, we introduce surface strain as a non-invasive indicator to track degradation-to-failure transitions in commercial 21700 batteries. Under accelerated degradation by depth-of-discharge stress, strain signals detect failure onset > 50% earlier than temperature. We develop two strain-derived metrics, the slope-based threshold and failure-proximity index and achieve 99.7% F1-score (100% true positive rate) and 3.82% normalized mean absolute error for failure detection and proximity estimation using machine learning on partial charging data with strain. Strain monitoring remains effective under fast-charge cycling. Using attachable sensors and lightweight computations, our approach allows easy integration with current battery systems, enabling real-time, onboard passive failure monitoring to enhance battery safety, reliability, and lifespan for future energy storage.
Date Issued
2026-03-18
Date Acceptance
2025-12-03
Citation
Joule, 2026, 10 (3)
ISSN
2542-4351
Publisher
Elsevier BV
Journal / Book Title
Joule
Volume
10
Issue
3
Copyright Statement
© 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
License URL
Identifier
10.1016/j.joule.2025.102272
Publication Status
Published
Article Number
102272
Date Publish Online
2026-01-02