journal article·2023-02-03Open access

Coating Defects of Lithium-Ion Battery Electrodes and Their Inline Detection and Tracking

Alexander Schoo, Robin Moschner, Jens Hülsmann, Arno Kwade

Batteries· Volume 9· Issue 2

Abstract

In order to reduce the cost of lithium-ion batteries, production scrap has to be minimized. The reliable detection of electrode defects allows for a quality control and fast operator reaction in ideal closed control loops and a well-founded decision regarding whether a piece of electrode is scrap. A widely used inline system for defect detection is an optical detection system based on line scan cameras and specialized lighting. The cameras scan the electrode, and brightness differences on the surface are detected and processed inline. The characteristics of the defect image are used for automated classification of the defects based on image features. Furthermore, the detailed detection of defects allows for the identification of causes. This paper describes the working principle of such an inline detection system, the catalog of typical defects, and the image features used to classify them automatically. Furthermore, we propose and discuss causes and effects of the different defect types on the basis of the literature and expert experience. In combination with tracking and tracing, this enables the manufacturer to reduce scrap by detecting defects early in the production chain.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Industrial Vision Systems and Defect Detection · Image and Object Detection Techniques · Image Processing Techniques and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute for Particle Technology, Technische Universität Braunschweig, Volkmaroder Str. 5, 38104 Braunschweig, Germany

Battery LabFactory Braunschweig, Technische Universität Braunschweig, Langer Kamp 19, 38106 Braunschweig, Germany

ISRA Surface Vision GmbH, Albert-Einstein-Allee 36-40, 45699 Herten, Germany

Funding

German - Federal Ministry of Economic Affairs and Climate Action (BMWK) · Project Management Agency Jülich (PTJ) · “EVOLi2S—Evaluation of the technical economic advantages of the open cell module for lithium ion and lithium sulphur batteries with regard to stationary and mobile application · BMBF—Federal Ministry of Education and Research

References (54)

Show source-provided references
1. From Materials to Cell: State-of-the-Art and Prospective Technologies for Lithium-Ion Battery Electrode Processing10.1021/acs.chemrev.1c00565 ↗
2. Electrode manufacturing for lithium-ion batteries—Analysis of current and next generation processing10.1016/j.est.2019.100862 ↗
3. γ-Valerolactone: An Alternative Solvent for Manufacturing of Lithium-Ion Battery Electrodes10.1021/acsaem.0c02575 ↗
4. A Numerical Study of the Effects of Cell Formats on the Cycle Life of Lithium Ion Batteries10.1149/2.0261910jes ↗
5. Energy Density of Cylindrical Li-Ion Cells: A Comparison of Commercial 18650 to the 21700 Cells10.1149/2.0281814jes ↗
6. Current status and challenges for automotive battery production technologies10.1038/s41560-018-0130-3 ↗
7. Kornas, T., Daub, R., Karamat, M.Z., Thiede, S., and Herrmann, C. (2019, January 22–26). Data-and Expert-Driven Analysis of Cause-Effect Relationships in the Production of Lithium-Ion Batteries. Proceedings of the 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), Vancouver, BC, Canada.10.1109/COASE.2019.8843185 ↗
8. A review of metrology in lithium-ion electrode coating processes10.1016/j.matdes.2021.109971 ↗
9. Identifying degradation mechanisms in lithium-ion batteries with coating defects at the cathode10.1016/j.apenergy.2018.09.073 ↗
10. Femtosecond laser structuring of graphite anodes for improved lithium-ion batteries: Ablation characteristics and process design10.2351/1.5040611 ↗
11. Enhanced Fast Charging and Reduced Lithium-Plating by Laser-Structured Anodes for Lithium-Ion Batteries10.1149/2.1241915jes ↗
12. Increasing the Discharge Rate Capability of Lithium-Ion Cells with Laser-Structured Graphite Anodes: Modeling and Simulation10.1149/2.1181807jes ↗
13. Effect of electrode manufacturing defects on electrochemical performance of lithium-ion batteries: Cognizance of the battery failure sources10.1016/j.jpowsour.2016.02.007 ↗
14. Classification of Calendering-Induced Electrode Defects and Their Influence on Subsequent Processes of Lithium-Ion Battery Production10.1002/ente.201900026 ↗
15. Kai Peter, B., Max, W., and Michael, O. (2022). Handbook on Smart Battery Cell Manufacturing: The Power of Digitalization, World Scientific Pub Co. Inc.
16. Effect of Defects on Diffusion Behaviors of Lithium-Ion Battery Electrodes: In Situ Optical Observation and Simulation10.1021/acsami.8b15260 ↗
17. Glass Surface Defects Detection with Wavelet Transforms10.7763/IJMMM.2015.V3.189 ↗
18. Automated fabric defect detection—A review10.1016/j.imavis.2011.02.002 ↗
19. Method for Classification of Battery Separator Defects Using Optical Inspection10.1016/j.procir.2016.11.101 ↗
20. Traceability in Battery Cell Production
21. Integration of Traceability Systems in Battery Production10.1016/j.procir.2020.04.002 ↗
22. Critical electrode properties and drying conditions causing component segregation in graphitic anodes for lithium-ion batteries10.1016/j.est.2018.06.009 ↗
23. Defect detection of uneven brightness in low-contrast images using basis image representation10.1016/j.patcog.2009.09.006 ↗
24. Surface Quality Assurance Method for Lithium-Ion Battery Electrode Using Concentration Compensation and Partiality Decision Rules10.1109/TIM.2019.2929670 ↗
25. Conveying Advanced Li-ion Battery Materials into Practice the Impact of Electrode Slurry Preparation Skills10.1002/aenm.201600655 ↗
26. Hoffmann, L., Kasper, M., Kahn, M., Gramse, G., Ventura Silva, G., Herrmann, C., Kurrat, M., and Kienberger, F. (2021). High-Potential Test for Quality Control of Separator Defects in Battery Cell Production. Batteries, 7.10.3390/batteries7040064 ↗
27. Stress Development of Li-Ion Battery Anode Slurries during the Drying Process10.1021/acs.iecr.5b00878 ↗
28. Influence of Convective Drying Parameters on Electrode Performance and Physical Electrode Properties10.1149/06422.0057ecst ↗
29. A Raman spectroscopic method to find binder distribution in electrodes during drying10.1007/s11998-013-9509-z ↗
30. Delamination behavior of lithium-ion battery anodes: Influence of drying temperature during electrode processing10.1080/07373937.2015.1060497 ↗
31. Drying of Lithium-Ion Battery Anodes for Use in High-Energy Cells: Influence of Electrode Thickness on Drying Time, Adhesion, and Crack Formation10.1002/ente.201900722 ↗
32. Towards Understanding of Cracking during Drying of Thick Aqueous-Processed LiNi 0.8 Mn 0.1 Co 0.1 O 2 Cathodes10.1021/acssuschemeng.9b06363 ↗
33. Influence of metallic contaminants on the electrochemical and thermal behavior of Li-ion electrodes10.1016/j.jpowsour.2021.230760 ↗
34. The progress on aluminum-based anode materials for lithium-ion batteries10.1039/D0TA09762D ↗
35. Production of large-area lithium-ion cells—Preconditioning, cell stacking and quality assurance10.1016/j.cirp.2012.03.101 ↗
36. Enabling aqueous processing for crack-free thick electrodes10.1016/j.jpowsour.2017.04.030 ↗
37. Experimental investigation into battery electrode surfaces: The distribution of liquid at the surface and the emptying of pores during drying10.1016/j.jcis.2017.01.063 ↗
38. Enhanced performance and lifetime of lithium-ion batteries by laser structuring of graphite anodes10.1016/j.apenergy.2021.117693 ↗
39. Three-dimensionalization via control of laser-structuring parameters for high energy and high power lithium-ion battery under various operating conditions10.1016/j.jechem.2021.04.011 ↗
40. Pfleging, W., and Gotcu, P. (2019). Femtosecond Laser Processing of Thick Film Cathodes and Its Impact on Lithium-Ion Diffusion Kinetics. Appl. Sci., 9.10.3390/app9173588 ↗
41. Laser structuring of graphite anodes and NMC cathode—Proportionate influence on electrode characteristics and cell performance10.1016/j.electacta.2021.139002 ↗
42. Gutoff, E.B. (2006). Coating and Drying Defects: Troubleshooting Operating Problems, Wiley-Interscience. [2nd ed.].10.1002/0470044136 ↗
43. Investigation of edge formation during the coating process of Li-ion battery electrodes10.1007/s11998-021-00521-w ↗
44. Recent advances in surface defect inspection of industrial products using deep learning techniques10.1007/s00170-021-06592-8 ↗
45. Automatic Surface Defect Inspection System Using Convolutional Neural Networks10.1088/1757-899X/999/1/012012 ↗
46. Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C.M., and Dario, P. (2020). Visual-Based Defect Detection and Classification Approaches for Industrial Applications-A SURVEY. Sensors, 20.10.3390/s20051459 ↗
47. Arai, K., and Kapoor, S. (2020). Advances in Computer Vision, Springer International Publishing.10.1007/978-3-030-17795-9 ↗
48. A Generic Deep-Learning-Based Approach for Automated Surface Inspection10.1109/TCYB.2017.2668395 ↗
49. Image-based defect detection in lithium-ion battery electrode using convolutional neural networks10.1007/s10845-019-01484-x ↗
50. Image-Based Surface Defect Detection Using Deep Learning: A Review10.1115/1.4049535 ↗
51. He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. arXiv.10.1109/CVPR.2016.90 ↗
52. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. arXiv.
53. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2023, January 26). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. Available online: http://arxiv.org/pdf/1603.04467v2.
54. Estimating Mass of Harvested Asian Seabass Lates calcarifer from Images10.4236/wjet.2018.63B003 ↗