Deep-Learning-Enabled Crack Detection and Analysis in Commercial Lithium-Ion Battery Cathodes

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

  • Tianyu Fu
  • Federico Monaco
  • Kai Zhang
  • Qingxi Yuan
  • Peter Cloetens
  • Piero Pianetta
  • Yijin Liu

Detail(s)

Original languageEnglish
Article number2203070
Journal / PublicationAdvanced Functional Materials
Volume32
Issue number39
Online published19 Jun 2022
Publication statusPublished - 26 Sep 2022
Externally publishedYes

Abstract

In Li-ion batteries, the mechanical degradation initiated by micro cracks is one of the bottlenecks for enhancing the performance. Quantifying the crack formation and evolution in complex composite electrodes can provide important insights into electrochemical behaviors under prolonged and/or aggressive cycling. However, observation and interpretation of the complicated crack patterns in battery electrodes through imaging experiments are often time-consuming, labor intensive, and subjective. Herein, a deep learning-based approach is developed to extract the crack patterns from nanoscale hard X-ray holo-tomography data of a commercial 18650-type battery cathode. Efficient and effective quantification of the damage heterogeneity with automation and statistical significance is demonstrated. The crack characteristics are further associated with the active particles’ packing densities and a potentially viable architectural design is discussed for suppressing the structural degradation in an industry-relevant battery configuration.

Research Area(s)

  • crack detection, deep learning, Li-ion batteries, phase contrast, X-ray holo-tomography, Data Science

Citation Format(s)

Deep-Learning-Enabled Crack Detection and Analysis in Commercial Lithium-Ion Battery Cathodes. / Fu, Tianyu; Monaco, Federico; Li, Jizhou; Zhang, Kai; Yuan, Qingxi; Cloetens, Peter; Pianetta, Piero; Liu, Yijin.

In: Advanced Functional Materials, Vol. 32, No. 39, 2203070, 26.09.2022.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review