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

doi:10.1002/adfm.202203070

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

Journal Article · Sun Jun 19 00:00:00 EDT 2022 · Advanced Functional Materials

DOI:https://doi.org/10.1002/adfm.202203070· OSTI ID:1890162

Fu, Tianyu ^[1]; Monaco, Federico ^[2]; Li, Jizhou ^[3]; Zhang, Kai ^[4]; Yuan, Qingxi ^[4]; Cloetens, Peter ^[2]; Pianetta, Piero ^[3]; ^[3]

Chinese Academy of Sciences (CAS), Beijing (China); University of Chinese Academy of Sciences, Beijing (China); SLAC
European Synchrotron Radiation Facility (ESRF), Grenoble (France)
SLAC National Accelerator Lab., Menlo Park, CA (United States). Stanford Synchrotron Radiation Lightsource (SSRL)
Chinese Academy of Sciences (CAS), Beijing (China)

We report 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.

View Accepted Manuscript (DOE)

Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: National Key Research and Development Program of China; National Natural Science Foundation of China (NSFC); USDOE Office of Science (SC), Engineering & Technology

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 1890162

Journal Information:: Advanced Functional Materials, Journal Name: Advanced Functional Materials Journal Issue: 39 Vol. 32; ISSN 1616-301X

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

References (27)

Quantification of Heterogeneous Degradation in Li‐Ion Batteries Yang, Yang; Xu, Rong; Zhang, Kai Advanced Energy Materials, Vol. 9, Issue 25 https://doi.org/10.1002/aenm.201900674	journal	May 2019
3D printing‐enabled advanced electrode architecture design Chu, Tiankuo; Park, Soyeon; Fu, Kun (Kelvin) Carbon Energy, Vol. 3, Issue 3 https://doi.org/10.1002/cey2.114	journal	May 2021
Heterogeneous damage in Li-ion batteries: Experimental analysis and theoretical modeling Xu, Rong; Yang, Yang; Yin, Fei Journal of the Mechanics and Physics of Solids, Vol. 129 https://doi.org/10.1016/j.jmps.2019.05.003	journal	August 2019
Chemomechanical interplay of layered cathode materials undergoing fast charging in lithium batteries Xia, Sihao; Mu, Linqin; Xu, Zhengrui Nano Energy, Vol. 53 https://doi.org/10.1016/j.nanoen.2018.09.051	journal	November 2018
The PyHST2 hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities Mirone, Alessandro; Brun, Emmanuel; Gouillart, Emmanuelle Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, Vol. 324 https://doi.org/10.1016/j.nimb.2013.09.030	journal	April 2014
Capacity Fading of Ni-Rich Li[Ni _x Co _y Mn _{1– x – y} ]O ₂ (0.6 ≤ x ≤ 0.95) Cathodes for High-Energy-Density Lithium-Ion Batteries: Bulk or Surface Degradation? Ryu, Hoon-Hee; Park, Kang-Joon; Yoon, Chong S. Chemistry of Materials, Vol. 30, Issue 3 https://doi.org/10.1021/acs.chemmater.7b05269	journal	January 2018
Three-Dimensional Analysis of Particle Distribution on Filter Layers inside N95 Respirators by Deep Learning Lee, Hye Ryoung; Liao, Lei; Xiao, Wang Nano Letters, Vol. 21, Issue 1 https://doi.org/10.1021/acs.nanolett.0c04230	journal	December 2020
A New Coating Method for Alleviating Surface Degradation of LiNi _0.6 Co _0.2 Mn _0.2 O ₂ Cathode Material: Nanoscale Surface Treatment of Primary Particles Kim, Hyejung; Kim, Min Gyu; Jeong, Hu Young Nano Letters, Vol. 15, Issue 3 https://doi.org/10.1021/acs.nanolett.5b00045	journal	February 2015
Oxygen Release Induced Chemomechanical Breakdown of Layered Cathode Materials Mu, Linqin; Lin, Ruoqian; Xu, Rong Nano Letters, Vol. 18, Issue 5 https://doi.org/10.1021/acs.nanolett.8b01036	journal	April 2018
Heterogeneous Reaction Activities and Statistical Characteristics of Particle Cracking in Battery Electrodes Lin, Feng; Zhao, Kejie; Liu, Yijin ACS Energy Letters, Vol. 6, Issue 11 https://doi.org/10.1021/acsenergylett.1c02135	journal	October 2021
In situ Visualization of State-of-Charge Heterogeneity within a LiCoO ₂ Particle that Evolves upon Cycling at Different Rates Xu, Yahong; Hu, Enyuan; Zhang, Kai ACS Energy Letters, Vol. 2, Issue 5 https://doi.org/10.1021/acsenergylett.7b00263	journal	April 2017
In Situ Visualization of Li-Whisker with Grating-Interferometry-Based Tricontrast X-ray Microtomography Zan, Guibin; Qian, Guannan; Gul, Sheraz ACS Materials Letters, Vol. 3, Issue 12 https://doi.org/10.1021/acsmaterialslett.1c00600	journal	November 2021
Nanoscale Morphological and Chemical Changes of High Voltage Lithium–Manganese Rich NMC Composite Cathodes with Cycling Yang, Feifei; Liu, Yijin; Martha, Surendra K. Nano Letters, Vol. 14, Issue 8 https://doi.org/10.1021/nl502090z	journal	July 2014
Intragranular cracking as a critical barrier for high-voltage usage of layer-structured cathode for lithium-ion batteries Yan, Pengfei; Zheng, Jianming; Gu, Meng Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms14101	journal	January 2017
Magnetically aligned graphite electrodes for high-rate performance Li-ion batteries Billaud, Juliette; Bouville, Florian; Magrini, Tommaso Nature Energy, Vol. 1, Issue 8 https://doi.org/10.1038/nenergy.2016.97	journal	July 2016
High-performance battery electrodes via magnetic templating Sander, J. S.; Erb, R. M.; Li, L. Nature Energy, Vol. 1, Issue 8 https://doi.org/10.1038/nenergy.2016.99	journal	July 2016
Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes Jiang, Zhisen; Li, Jizhou; Yang, Yang Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-16233-5	journal	May 2020
Highly selective synthesis of all-carbon tetrasubstituted alkenes by deoxygenative alkenylation of carboxylic acids Li, Yantao; Shao, Qianzhen; He, Hengchi Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-021-27507-x	journal	February 2022
Additive engineering for robust interphases to stabilize high-Ni layered structures at ultra-high voltage of 4.8 V Tan, Sha; Shadike, Zulipiya; Li, Jizhou Nature Energy, Vol. 7, Issue 6 https://doi.org/10.1038/s41560-022-01020-x	journal	May 2022
Deep learning for cellular image analysis Moen, Erick; Bannon, Dylan; Kudo, Takamasa Nature Methods, Vol. 16, Issue 12 https://doi.org/10.1038/s41592-019-0403-1	journal	May 2019
Understanding multi-scale battery degradation with a macro-to-nano zoom through its hierarchy Zan, Guibin; Zhang, Jin; Monaco, Federico Journal of Materials Chemistry A, Vol. 9, Issue 35 https://doi.org/10.1039/D1TA02262H	journal	January 2021
Holotomography: Quantitative phase tomography with micrometer resolution using hard synchrotron radiation x rays Cloetens, P.; Ludwig, W.; Baruchel, J. Applied Physics Letters, Vol. 75, Issue 19 https://doi.org/10.1063/1.125225	journal	November 1999
Recent advances in synchrotron-based hard x-ray phase contrast imaging Liu, Y.; Nelson, J.; Holzner, C. Journal of Physics D: Applied Physics, Vol. 46, Issue 49 https://doi.org/10.1088/0022-3727/46/49/494001	journal	November 2013
VoxelMorph: A Learning Framework for Deformable Medical Image Registration Balakrishnan, Guha; Zhao, Amy; Sabuncu, Mert R. IEEE Transactions on Medical Imaging, Vol. 38, Issue 8 https://doi.org/10.1109/TMI.2019.2897538	journal	August 2019
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Badrinarayanan, Vijay; Kendall, Alex; Cipolla, Roberto IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, Issue 12 https://doi.org/10.1109/TPAMI.2016.2644615	journal	December 2017
Dynamics of particle network in composite battery cathodes Li, Jizhou; Sharma, Nikhil; Jiang, Zhisen Science, Vol. 376, Issue 6592 https://doi.org/10.1126/science.abm8962	journal	April 2022
ImageNet classification with deep convolutional neural networks Krizhevsky, Alex; Sutskever, Ilya; Hinton, Geoffrey E. Communications of the ACM, Vol. 60, Issue 6 https://doi.org/10.1145/3065386	journal	May 2017

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25 ENERGY STORAGE
crack detection
deep learning
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x-ray holo-tomography

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

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