Defect detection within LIBs requires advanced methodologies for three-dimensional defect localization, enabling the differentiation of electrodes, separators, and aluminum-plastic films within the battery layers.
In this paper, AIA DETR model is proposed by adding AIA (attention in attention) module into transformer encoder part, which makes the model pay more attention to correct defect information. Rather than the noise information on the image, so as to improve the detection ability of lithium battery surface defects.
Ultrasonic detection offers several distinct advantages over the aforementioned characterization methods for detecting gas defects in LIBs. Firstly, ultrasonic detection can penetrate the aluminum plastic film of batteries, allowing it to monitor tiny bubbles and defects deep inside the battery in real-time.
This capability is of critical importance for the identification of defects that could lead to battery failure or safety issues, and guide the optimization of LIBs with better safety and performance. This perspective review briefly summarize the comprehensive application of industrial CT in LIBs including battery materials, cells and modules.
In case of FMD detection, High Potential (HiPot) test, a test method used in the electrical safety assessment, is widely used in the battery manufacturing line [ 46 ]. HiPot test realizes the detection of FMD by examining the insulation performance of the battery.
The current state, main technical approaches, and challenges of ultrasonic technology in battery defect and fault diagnosis are summarized. The prospect of ultrasound application in the field of batteries in the future is anticipated.
Detecting the foreign matter defect in lithium-ion batteries …
In this paper, we propose a data-driven detection method for foreign matter defect in lithium-ion batteries. In contrast to the existing battery diagnosis and fault detection methods that use battery operating data as input, we conducted the experiments and implanted foreign matter defects into batteries on a real battery pilot manufacturing ...
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An end-to-end Lithium Battery Defect Detection Method Based …
In this paper, AIA DETR model is proposed by adding AIA (attention in attention) module into transformer encoder part, which makes the model pay more attention to correct defect information. Rather than the noise information on the image, so as to improve the detection ability of lithium battery surface defects. Experiments show that AIA DETR ...
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Few-shot learning approach for 3D defect detection in lithium battery
The height-gray transformed image For the target lithium battery as shown in Figure 1. Defining the 3D reconstruction result generated by multiple exposure fusion methods as a 3D cloud point P .
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Computed Tomography detects defects in modern lithium-ion batteries
rechargeable lithium-ion batteries are subject to strict quality monitoring. Industrial computed tomography (CT) is increasingly being used to detect defects and internal changes throughout a battery''s lifecycle. CT-data analysis and visualisation software provides functions that allow a deep look into the inner workings of energy storage ...
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Deep-Learning-Based Lithium Battery Defect Detection via …
This research addresses the critical challenge of classifying surface defects in lithium electronic components, crucial for ensuring the reliability and safety of lithium batteries. With a scarcity of specific defect data, we introduce an innovative Cross-Domain Generalization (CDG) approach, incorporating Cross-domain Augmentation, Multi-task Learning, and Iteration …
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Defects Detection of Lithium-Ion Battery Electrode Coatings
Aiming to address the problems of uneven brightness and small defects of low contrast on the surface of lithium-ion battery electrode (LIBE) coatings, this study proposes a defect detection method that combines background reconstruction with an enhanced Canny algorithm. Firstly, we acquire and pre-process the electrode coating image, considering the …
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Deep-Learning-Based Lithium Battery Defect Detection via …
This research addresses the critical challenge of classifying surface defects in lithium electronic components, crucial for ensuring the reliability and safety of lithium batteries. With a scarcity of specific defect data, we introduce an innovative Cross-Domain Generalization (CDG) approach, incorporating Cross-domain Augmentation, Multi-task ...
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Multi-task Deep Learning Based Defect Detection For Lithium Battery ...
This paper proposes a lithium battery tab gap defect technology based on multi-task deep learning model. The model takes U-Net as the architecture, ResNet as the encoder backbone network, and the decoding end connects up to three task-related networks, including defect area detection task network, defect contour detection task network, and ...
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Progress and challenges in ultrasonic technology for state …
Currently, applications of ultrasonic technology in battery defect detection primarily include foreign object defect detection, lithium plating detection, gas defect detection, wetting degree analysis, thermal runaway detection, electrode defects and dry state identification, and Solid Electrolyte Interphase (SEI) film growth recognition, among others. The following …
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HE-Yolov8n: an innovative and efficient method for detecting defects …
Experimental results demonstrate that HE-Yolov8n significantly outperforms mainstream models in detecting surface defects. Specifically, in lithium battery shell defect detection, it achieves an mAP50 of 97.0%, representing a 4.6% improvement over Yolov8n. Its parameters and FLOPs are reduced by 18.75% and 8.05%, respectively, while maintaining ...
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Detecting the foreign matter defect in lithium-ion batteries based …
In this paper, we propose a data-driven detection method for foreign matter defect in lithium-ion batteries. In contrast to the existing battery diagnosis and fault detection methods …
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Progress and challenges in ultrasonic technology for state …
Defect detection within LIBs requires advanced methodologies for three-dimensional defect localization, enabling the differentiation of electrodes, separators, and …
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Computed Tomography detects defects in modern lithium-ion …
the general applicability of CT imaging to detect manufacturing defects such as foreign matter contamination (FMD) or anode–cathode misalignments, further investigations are needed to determine...
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HE-Yolov8n: an innovative and efficient method for detecting …
Experimental results demonstrate that HE-Yolov8n significantly outperforms mainstream models in detecting surface defects. Specifically, in lithium battery shell defect …
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X-Ray Computed Tomography (CT) Technology for Detecting Battery Defects ...
The 3D nano-CT imaging reveals significant recombination of CuO particles and precipitation of Li + conductive films suitable for battery applications. 7 The CT detection technique enables the identification of material type and composition, observation of the impact of charge and discharge variations on materials, and exploration into the ...
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Lithium battery surface defect detection based on the YOLOv3 detection …
To detect the defects of lithium batteries, a detection algorithm based on convolutional neural networks is proposed in this paper. Firstly, image preprocessing is introduced on the collected lithium battery dataset. Secondly, the K-means clustering algorithm is used on the processed dataset to generate anchor boxes for lithium battery defect detection. Then the …
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Coating Defects of Lithium-Ion Battery Electrodes …
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 …
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A YOLOv8-Based Approach for Real-Time Lithium-Ion Battery
Targeting the issue that the traditional target detection method has a high missing rate of minor target defects in the lithium battery electrode defect detection, this paper proposes an improved and optimized battery electrode defect detection model based on YOLOv8. Firstly, the lightweight GhostCony is used to replace the standard convolution, and the …
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Multi-task Deep Learning Based Defect Detection For Lithium …
This paper proposes a lithium battery tab gap defect technology based on multi-task deep learning model. The model takes U-Net as the architecture, ResNet as the encoder backbone …
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(PDF) A Systematic Review of Lithium Battery Defect Detection ...
The review covers various defect types, including manufacturing, operational, and environmental defects, and discusses the methodologies used for defect detection, including their...
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