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

Schoo, Alexander; Moschner, Robin; Hülsmann, Jens; Kwade, Arno

doi:10.3390/batteries9020111

Cited by 13 publications

(5 citation statements)

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“…Other terms and types of elongated defects found in the literature are scratches, streaks, stripes, kinks and tears [20,[42][43][44][45]. Even though kinks mainly occur in the centre of jelly rolls in winded cells due to volumetric changes in operation, their positioning is a result of inhomogeneous core geometry [20,46].…”

Section: Intrinsic Inhomogeneitiesmentioning

confidence: 99%

Detection of Manufacturing Defects in Lithium-Ion Batteries-Analysis of the Potential of Computed Tomography Imaging

Evans,

Luc,

Tebruegge

et al. 2023

Energies

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Realising an ideal lithium-ion battery (LIB) cell characterised by entirely homogeneous physical properties poses a significant, if not an impossible, challenge in LIB production. Even the slightest deviation in a process parameter in its production leads to inhomogeneities and causes a deviation in performance parameters of LIBs within the same batch. The greater the number and/or intensity of inhomogeneities, the more they need to be avoided. Severe inhomogeneities (defects), such as metal particle contamination, significantly impact the cell’s performance. Besides electrical measurements, image-based measurement methods can be used to identify defects and, thus, ensure the production quality and safety of LIBs. While the applicability of computed tomography (CT) as an image-based measurement method for detecting defects has been proven, the limitations of this method still need to be determined. In this study, a systematic analysis of the capabilities of CT imaging was conducted. A multilayer pouch cell without an electrolyte was reassembled with several defects on one of the middle anodes. To investigate the boundaries of CT, defects such as a partial and complete removal of the coating, a cut, or a kink, as well as particle contaminations of various sizes and materials (aluminium, copper, iron) were chosen. By comparing the CT images of the cell using laser scanning microscope images of the defective anode, it could be proven that all selected defects except the kink were detectable.

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Section: Intrinsic Inhomogeneitiesmentioning

confidence: 99%

Detection of Manufacturing Defects in Lithium-Ion Batteries-Analysis of the Potential of Computed Tomography Imaging

Evans,

Luc,

Tebruegge

et al. 2023

Energies

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“…The heat of the battery is due to the phenomenon of thermal escape, where the excess heat promotes the chemical reaction that makes the battery work and thus generates more heat and enters a spiral of disasters [10]. These defects prompted scientists and researchers worldwide to develop new types of batteries to avoid these defects and thus increase the battery's efficiency [11]. Perhaps one of the most prominent of these solutions is the use of a new group of materials in the manufacture of these batteries.…”

Section: ■ Introductionmentioning

confidence: 99%

Surface Properties of Graphene and Graphene Oxide Aerogels for Energy Storage Applications

Mahmood,

Hussian

2024

Indones. J. Chem.

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This review is mainly on the relevance of graphene aerogels for energy storage systems highlighting their distinct properties and applications. Today, electronic devices such as smartphones, laptops, and other electrical appliances have become the axe of our daily lives. As a result, electrical energy is required for these devices. Despite the discovery of renewable energy sources as an alternative to fossil fuels, the construction of energy storage systems is still necessary to store energy. Lithium-ion batteries and supercapacitors are considered essential systems for this purpose and have witnessed tremendous development in recent years. The efficiency of these systems depends on the structure of the materials used in their formation. Graphene oxide and graphene aerogel materials improve the properties of energy storage systems in terms of stability of charging and discharging cycles, longevity, and reduction of combustion incidents resulting from ordinary compounds. However, the development of graphene aerogels faces challenges in improving their mechanical properties, the cost of their preparation, and their high agglomeration ability in solvents. Therefore, intensive efforts are needed to develop these materials for a new revolution in energy storage.

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“…During the forging process, defects such as cracks or scratches on the surface cannot be Systems 2024, 12, 24 2 of 16 avoided. So, in order to prevent these defects from affecting the final product, researchers have studied different approaches to identify defects in the parts of the battery [9][10][11] or to improve battery manufacturing processing [12].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on Microfasteners

Vu,

Chang,

Kim

2024

Systems

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The management of product quality is a crucial process in factory manufacturing. However, this approach still has some limitations, e.g., depending on the expertise of the engineer in evaluating products and being time consuming. Various approaches using deep learning in automatic defect detection and classification during production have been introduced to overcome these limitations. In this paper, we study applying different deep learning approaches and computer vision methods to detect scratches on the surface of microfasteners used in rechargeable batteries. Furthermore, we introduce an architecture with statistical quality control (SQC) to continuously improve the efficiency and accuracy of the product quality. The proposed architecture takes advantage of the capability of deep learning approaches, computer vision techniques, and SQC to automate the defect detection process and quality improvement. The proposed approach was evaluated using a real dataset comprising 1150 microfastener surface images obtained from a factory in Korea. In the study, we compared the direct and indirect prediction methods for predicting the scratches on the surface of the microfasteners and achieved the best accuracy of 0.91 with the indirect prediction approach. Notably, the indirect prediction method was more efficient than the traditional one. Furthermore, using control charts in SQC to analyze predicted defects in the production process helped operators understand the efficiency of the production line and make appropriate decisions in the manufacturing process, hence improving product quality management.

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

Cited by 13 publications

References 50 publications

Detection of Manufacturing Defects in Lithium-Ion Batteries-Analysis of the Potential of Computed Tomography Imaging

Detection of Manufacturing Defects in Lithium-Ion Batteries-Analysis of the Potential of Computed Tomography Imaging

Surface Properties of Graphene and Graphene Oxide Aerogels for Energy Storage Applications

Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on Microfasteners

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