Surface defects of new energy batteries
Lithium battery surface defect detection based on the YOLOv3
To detect the defects of lithium batteries, a detection algorithm based on convolutional neural networks is proposed in this paper. Firstly, image preprocessing is
北理工课题组在高比能全固态锂离子电池研究中取得重要进展
Defective oxygen inert phase stabilized high-voltage nickel-rich cathode for high-energy lithium-ion batteries. Nat. Commun. 14, 8087 (2023). (第一作者：代中盛博士生)
Defect engineering for surface reconstruction of metal oxide
Researchers have gradually established theoretical relationships between defects and the thermodynamic properties of catalysts and OERs because of the advances in density functional theory (DFT) calculations and characterization techniques. 22 For instance, defect structures can lower the band gap by introducing new defect energy levels at the
Defects in Lithium-Ion Batteries: From Origins to Safety Risks
Lithium-ion batteries are currently the most widely used energy storage devices due to their superior energy density, long lifespan, and high efficiency. However, the manufacturing defects, caused by production flaws and raw material impurities can accelerate battery degradation. In extreme cases, these defects may result in severe safety
Research on the application of nanomaterials in new energy batteries
Nowadays, new energy batteries and nanomaterials are one of the main areas of future development worldwide. This paper introduces nanomaterials and new energy batteries and talks about the
Deep-Learning-Based Lithium Battery Defect Detection via Cross
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
Sim-YOLOv5s: A method for detecting defects on the end face of
In this study, we propose an effective defect-detection model, called Sim-YOLOv5s, for lithium battery steel shells. In this model, we propose a fast spatial pooling pyramid structure, SimSPPF, to speed up the model and embed the attention mechanism convolutional block attention module in the backbone.
Surface Anion Effects in Aqueous Hydrogen Ion Batteries
Aqueous hydrogen ion batteries possess the advantages of sustainability, low cost, and high safety, which makes them an ideal choice for grid-level energy storage. Although some anions show strong interaction with the surface of some metal oxides, the effect of anions on the cation intercalation behavior and electrochemical activity is rarely reported. Herein, we
Lithium battery surface defect detection based on the YOLOv3
With the continuous development of science and technology, cylindrical lithium batteries, as new energy batteries, are widely used in many fields. In the production process of lithium batteries, various defects may occur. To detect the defects of lithium batteries, a detection algorithm based on convolutional neural networks is proposed in this paper. Firstly, image
A novel approach for surface defect detection of lithium battery
Lithium-ion batteries are currently the most widely used energy storage devices due to their superior energy density, long lifespan, and high efficiency. However, the
北理工课题组在高比能全固态锂离子电池研究中取得重要进展
Defective oxygen inert phase stabilized high-voltage nickel-rich cathode for high-energy lithium-ion batteries. Nat. Commun. 14, 8087 (2023). (第一作者：代中盛博士生) 2. Chemical competing diffusion for practical all-solid-state batteries. J. Am. Chem. Soc. Doi: 10.1021/jacs.4c11645 (第一作者：代中盛博士生) 3. Regulating Sulfur Redox Kinetics by
3D Point Cloud-Based Lithium Battery Surface Defects
Detecting the lithium battery surface defects is a difficult task due to the illumination reflection from the surface. To overcome the issue related to labeling and training big data by using 2D techniques, a 3D point cloud-based technique has been proposed in this... Skip to main content. Advertisement. Account. Menu. Find a journal Publish with us Track your
A novel approach for surface defect detection of lithium battery
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 speed up the effect for point filtering. Then, the improved clustering
Deep-Learning-Based Lithium Battery Defect Detection via Cross
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 Learning.
Deep-Learning-Based Lithium Battery Defect Detection via Cross
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
DCS-YOLO: Defect detection model for new energy vehicle battery
In the manufacturing process of all-solid-state batteries, surface defects in the current collector can affect the cell''s quality and functionality. These issues can be mitigated by inspecting the current collector for defects during the manufacturing process.
A novel approach for surface defect detection of lithium battery
A novel approach for surface defect detection of lithium battery based on improved K‑nearest neighbor and Euclidean clustering segmentation and high energy density [1–3]. However, many new energy vehicle and electric tools with lithium battery are usually damaged because of the integrity of the battery system in the process of complex industrial production []. Moreo4 - ver,
Surface defect detection of cylindrical lithium-ion battery by
In the proposed Lithium-ion battery Surface Defect Detection (LSDD) system, an augmented dataset of multi-scale patch samples generated from a small number of lithium-ion battery images is used in the learning process of a two-stage classification scheme that aims to differentiate defect image patches of lithium-ion batteries in the first stage
Sim-YOLOv5s: A method for detecting defects on the end face of
In this study, we propose an effective defect-detection model, called Sim-YOLOv5s, for lithium battery steel shells. In this model, we propose a fast spatial pooling
DCS-YOLO: Defect detection model for new energy vehicle battery
In the manufacturing process of all-solid-state batteries, surface defects in the current collector can affect the cell''s quality and functionality. These issues can be mitigated
(PDF) A novel approach for surface defect detection of
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...
Surface defect detection of cylindrical lithium-ion battery by
In the proposed Lithium-ion battery Surface Defect Detection (LSDD) system, an augmented dataset of multi-scale patch samples generated from a small number of lithium-ion battery
Lithium battery surface defect detection based on the YOLOv3
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
Machine vision-based detection of surface defects in cylindrical
Automotive 21700 series lithium batteries are prone to surface defects during production and transportation, thus affecting their performance, so we propose a full-surface defect detection method for battery cases based on the synthesis of traditional image processing and deep learning to address this problem. First, the mechanism of
(PDF) A novel approach for surface defect detection
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
Machine vision-based detection of surface defects in cylindrical
Automotive 21700 series lithium batteries are prone to surface defects during production and transportation, thus affecting their performance, so we propose a full-surface defect detection method for battery cases based on the synthesis of traditional image
DGNet: An Adaptive Lightweight Defect Detection Model for New Energy
As an essential component of the new energy vehicle battery, current collectors affect the performance of battery and are crucial to the safety of passengers. The significant differences in shape and scale among defect types make it challenging for the model detection of current collector defects. In order to reduce application costs and conduct real
(PDF) A novel approach for surface defect detection of lithium battery
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...
Surface Defects Detection and Identification of Lithium Battery
Abstract: In order to realize the automatic detection of surface defects of lithium battery pole piece, a method for detection and identification of surface defects of lithium battery pole piece based on multi-feature fusion and PSO-SVM was proposed in this paper. Firstly, image subtraction and contrast adjustment were used to preprocess the defect image to weaken the
6 FAQs about [Surface defects of new energy batteries]
How to identify surface defects of lithium battery?
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 speed up the effect for point filtering.
Can surface defect detection system improve the production quality of lithium battery?
The application results show that the surface defect detection system of lithium battery can accurately construct the three-dimensional model of lithium battery surface and identify the defects on the model, improving the production quality and efficiency of lithium battery.
Can computer terminals detect surface defects during lithium battery industrial production?
Shown in Fig. 14 is the use of computer terminals to control equipment and adjust parameters for defect detection during lithium battery industrial production. Based on the method presented in this paper, the system is used to detect the surface defects of lithium battery and display them in real time.
Do battery shells have defects?
In terms of defect detection in battery shells, the major relevant studies have mainly focused on photovoltaic cells or button cells, whereas there are few studies on cylindrical lithium batteries and defects of the end face of the battery shell.
Do lithium battery shells have defects?
The presence of pits, R-angle injuries, hard printing, and other defects on the end face of lithium battery shells severely affects the production safety and usage safety of lithium battery products. In this study, we propose an effective defect-detection model, called Sim-YOLOv5s, for lithium battery steel shells.
Is there a defect-detection model for lithium battery steel shells?
In this study, we propose an effective defect-detection model, called Sim-YOLOv5s, for lithium battery steel shells. In this model, we propose a fast spatial pooling pyramid structure, SimSPPF, to speed up the model and embed the attention mechanism convolutional block attention module in the backbone.
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