Hundreds of electric vehicle (EV) battery thermal runaway accidents resulting from untreated defects restrict further development of EV industry. Battery defect detection based on the abnormality of external parameters is a promising way to reduce this kind of thermal runaway accidents and protect EV consumers from fire danger. However, the influence of …
Hundreds of electric vehicle (EV) battery thermal runaway accidents resulting from untreated defects restrict further development of EV industry. Battery defect detection based on the abnormality of external parameters is a promising way to reduce this kind of thermal runaway accidents and protect EV consumers from fire danger. However, the influence of …
Designing an EV battery fault detection algorithm that is implementable and ... Y., Liu, P., Wang, Z., Zhang, L. & Hong, J. Fault and defect diagnosis of battery for electric vehicles based on big ...
When manufacturing battery cells, various defects can occur that require detection so the product can be removed before shipping. Microscopic cracks can occur in the electrode materials or the separator, potentially leading to reduced performance and safety concerns. Inconsistent coating on electrodes can lead to short circuits or reduced capacity.
The welding defects of battery safety vents were proposed to be detected using a lightweight and effective deep learning algorithm. A huge number of images were collected …
Keywords: lithium-ion battery, ultrasonic, non-destructive testing, material property, battery defect, battery safety. Citation: Yi M, Jiang F, Lu L, Hou S, Ren J, Han X and Huang L (2021) Ultrasonic Tomography Study of Metal Defect Detection in Lithium-Ion Battery. Front. Energy Res. 9:806929. doi: 10.3389/fenrg.2021.806929
Experimental results showed that the detection accuracy of this method for 2000 samples reached 98.9%, providing an effective way for X-ray defect detection of thermal battery. 10 Xu W et al. introduced an attention mechanism into the residual neural network to obtain the I-ResNet50 network, which can automatically detect assembly defects in ...
A GDP-DLCSS is proposed for the extraction of fault signals from lithium batteries. The proposed method is applied to actual operating data of electric vehicles. …
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) …
For the traditional algorithm to detect lithium battery defects, the missing rate is high and the speed is slow, an improved YOLOv7 algorithm was proposed. Firstly, CBAM attention mechanism is added to feature extraction part, which can enhance network''s representation ability. Secondly, in the feature fusion part, ConvNeXt lightweight module was …
Unfortunately, traditional methods for defect detection in battery manufacturing have encountered several challenges. Conventional techniques, such as visual inspection or basic electronic testing, often lack the precision …
This paper proposes a new approach to defect detection system design focused on exact damaged areas demonstrated through visual data containing gear wheel images. The main advantage of the system is the capability to detect a wide range of patterns of defects occurring in datasets. The methodology is built on three processes that combine …
This study compared two nondestructive testing methods, SAM and CT, for the detection and 3D localization of defects in battery cells. It is important to detect such defects before performance degradation or safety issues arise.
DOI: 10.1117/12.2615289 Corpus ID: 244452083; Lithium battery surface defect detection based on the YOLOv3 detection algorithm @inproceedings{Lang2021LithiumBS, title={Lithium battery surface defect detection based on the YOLOv3 detection algorithm}, author={Xianli Lang and Yu Zhang and Shuangbao Shu …
Surface defects of lithium batteries seriously affect the product quality and may lead to safety risks. In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed based on improved K-nearest neighbor (KNN) and Euclidean clustering segmentation. Firstly, an improved voxel density strategy for KNN is proposed to …
The results show that the optimization algorithm can improve the accuracy and speed of the lithium battery and achieves a 92.7% detection accuracy, surpassing the original network by 2.1%. For the traditional algorithm to detect lithium battery defects, the missing rate is high and the speed is slow, an improved YOLOv7 algorithm was proposed. Firstly, CBAM …
Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies.
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 model can well detect the defect target of lithium battery, effectively reduce the missed detection problem, and reach 81.9% AP in the lithium battery defect data set
When and why does a rechargeable battery lose capacity or go bad? ... Mohammadi, M., Schauerman, C.M. et al. Rechargeable lithium-ion cell state of charge and defect detection by in-situ inside ...
DOI: 10.3390/electronics13010173 Corpus ID: 266721264; A YOLOv8-Based Approach for Real-Time Lithium-Ion Battery Electrode Defect Detection with High Accuracy @article{Zhou2023AYA, title={A YOLOv8-Based Approach for Real-Time Lithium-Ion Battery Electrode Defect Detection with High Accuracy}, author={Hongcheng Zhou and Yongxing Yu …
ISSN: 3006-2004 (Print), ISSN: 3006-0826 (Online) | Volume 2, Number 2, Year 2024
research on battery defect detection. Research shows that most of the current research are mainly aimed at lithium-ion batteries.4–6 Although some scholars have conducted research on defect detection of thermal batteries, the research on intelligent detection of different types of defects in thermal batteries is relatively weak.
To ensure a high-grade battery, defect-free separators and high-quality electrodes are required. SURFACE VISION. 2 Coating on an aluminum (cathode) or copper ... SmartView provides total vision integration for high-speed defect detection, monitoring, and reporting. It delivers robust features, flexible operation, and proven, high-quality ...
For surface defect detection in a cylindrical battery case, because annealed SPCE nickel-plated steel has a smooth surface with severe reflections, as well as small and complex surface defects, a random distribution, a small wall thickness at the end of the battery case, and noise, this paper uses traditional image processing combined with YOLOv7 to propose a prescription that is …
A battery defect detection method that integrates the traditional image processing and deep learning based on the image processing technique and employs a deep neural network for the training of battery defect detection is proposed. Battery defect detection is an important task in the battery production line. Realizing full automation for battery defect …
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 …
Surface defects of lithium batteries seriously affect the product quality and may lead to safety risks. In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed …
Laser welding defects detection in lithium-ion battery poles. Author links open overlay panel Nasir Ud Din, Li Zhang ... different instruments and methods are needed for laser welding defect detection. In most cases, one device or procedure will not provide adequate detection accuracy, although it may seem feasible to integrate all of the ...
Experiments show that AIA DETR model can well detect the defect target of lithium battery, effectively reduce the missed detection problem, and reach 81.9% AP in the lithium battery …
DOI: 10.1784/insi.2024.66.5.305 Corpus ID: 269679222; Resolving data imbalance in alkaline battery defect detection: a voting-based deep learning approach @article{Xu2024ResolvingDI, title={Resolving data imbalance in alkaline battery defect detection: a voting-based deep learning approach}, author={Zhenying Xu and Bangguo Han and Liling Han and Yucheng Tao …
Focus on the requirement for detecting laser welding defects of lithium battery pole, a new model based on the improved YOLOv5 algorithm was proposed in this paper.
Different types of defects can be introduced into batteries during the battery manufacturing process, such as pinholes, metal particles, non-uniform coating, burrs or rips on the tab, deflected electrode, etc. [22, 26].Among all kinds of detects, the foreign matter defect (FMD) is a severe problem which can be introduced in almost every process of battery …
The 3D point cloud-based defect detection of lithium batteries used feature-based techniques to downscale the point clouds to reduce the computational cost, extracting the normals of the points and calculating their differences to detect the defects of the battery which assure the quality of the product.
2 · Despite the growth in anomaly detection algorithms and datasets, resources for battery defect detection remain scarce. To address this gap, we have meticulously …