Download Citation | On Dec 1, 2023, Gangfeng Sun and others published Autoencoder-Enhanced Regularized Prototypical Network for New Energy Vehicle battery fault detection | Find, read and cite all
Electronics 2024, 13, 173 3 of 16 Initially introduced by Joseph et al. in 2016, the YOLO (You Only Look Once) algo-rithm marked a significant advancement in object detection.
The experimental results show that the proposed method can effectively detect and identify five common types of defects in the coating of LBEs, including scratches, bubbles,
Coaxial line scan light: It is mainly aimed at surface feature detection, such as surface scratch, bump, character and other features detection. The lithium battery is used coaxial line scan light for uniform surface, scratches, foreign bodies,
To achieve significant fuel consumption and carbon emission reductions, new energy vehicles have become a transport development trend throughout the world.
proposed a local optimized random sample consensus algorithm (LO-RANSAC), and Ref. proposed an improved Sim-YOLOv5s model for the problems of low trace
The weight loss in a battery cell caused by a 6 cm-diameter hole in an electrode sheet is 0.4% of the total weight of the battery cell, assuming it consists of 25 sheets with a total area of 288 cm 2. Even smaller is the weight loss caused by a scratch. These defects are within the statistical variation of a battery cell batch.
In the process of scratch defect detection in textured materials, there are often problems of low efficiency in traditional manual detection, large errors in machine vision, and difficulty in distinguishing defective scratches from the background texture. In order to solve these problems, we developed an enhanced scratch defect detection system for textured materials
The invention relates to a new energy automobile battery pack bottom scratch test device, which comprises a test platform and a simulated scratch mechanism arranged in the middle of the test platform; the test platform is a cuboid-shaped bench mechanism, four feet at the lower part are respectively provided with foundation bolt holes, the left side and the right side of the middle
The experimental results show that the mean average precision (mAP) value of the detection algorithm on the lithium battery validation dataset reaches 94% and the detection
The FPS reaches 147.1, and the detection accuracy of various defect categories is improved, especially Severely bad and No cover, and the detection recall rate reaches 100%. This method has high target detection model efficiency and meets the requirements of real-time detection of battery collector defects.
Health monitoring and abnormality detection of power batteries for new energy vehicles has been one of the hot topics in recent years. Accurate and efficient power battery anomaly detection is
Welding defect detection plays an important role in the quality control of new energy batteries. Since the traditional manual detection methods are not intelligent enough and cost a lot, many deep learning algorithms have been proposed. With the development of detection technology, the Yolo series of algorithms have been applied to various detection tasks. Focus on our welding
Article "Research on Surface Scratch Detection Method of Button Battery Based on Machine Vision" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as "JST"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology,
The proposed algorithm can effectively locate and classify the bottom defects of the lithium battery and can effectively locate and classify the bottom defects of the lithium battery. 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,
and regions.1 New electric energy vehicles are playing an increasingly important role in decarbonization in the trans-portation industry. They constitute a promising solution to a set of global challenges such as climate change and air pollution.2 Developing new energy vehicles has been a global consensus, and developing new energy vehicles
This paper presents a study on the problem of burrs on the electrodes of new energy batteries, which are a major factor contributing to battery short-circuits and explosions. During the process of electrode cutting, the use of cutting tools with a notch is likely to cause burrs on the electrode. Therefore, it is essential to accurately detect the notch of the cutting tool.
In general, energy density is a key component in battery development, and scientists are constantly developing new methods and technologies to make existing batteries more energy proficient and safe. This will make it possible to design energy storage devices that are more powerful and lighter for a range of applications.
This study compares two nondestructive testing methods for the 3D visualization of defects at different depths inside a pouch battery cell: scanning acoustic microscopy (SAM) and X-ray computed tomography (CT).
For example, literature first masked the fonts on the button battery through template matching and then used the gradient feature method to locate the scratch location. Although there is an excellent recognition effect on a specific sample set, the change in light of the light source will seriously influence the visual system''s classification effect.
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
This paper introduces a new energy battery active-passive hybrid binocular intelligent inspection system, using structured light and laser line-scan instruments to acquire battery surface image information. Based on the existing 3D reconstruction technology, the active-passive hybrid binocular system is designed. In order to reduce the interference of multiple factors, the 3D
The method comprises the following steps: carrying out nonlinear mapping on the gray level image on the surface of the lithium battery; transforming the decoupled illumination and
Sensor fault detection and diagnosis (SFDD) methods can be broadly divided into data-driven and model-based methods (Reppa et al., 2015; Lee et al., 2021).The model-based methods are usually easy
As the ownership of new energy vehicles (NEVs) is experiencing a sustained growth, the safety of NEVs has become increasingly prominent, with power battery faults emerging as the primary cause of fire accidents in NEVs. Successful detection of incipient faults can not only improve the safety and reliability but also provide optimal maintenance
Request PDF | Machine Learning-Enhanced Vision Systems for Cutting Tool Notch Detection in New Energy Battery Manufacturing | This paper presents a study on the problem of burrs on the electrodes
With the development of new energy technology, the battery management system (BMS) can collect more and more monitoring data, such as voltage, temperature, and so on. Kim et al. proposed an outlier mining-based fault detection method that uses a hybrid model-based battery condition monitoring algorithm to estimate the physical model
Research can achieve real-time monitoring and timely reminders of potential faults. By early detection of issues such as battery overheating and voltage imbalance, this
Download Citation | A Scratch Detection Method Based on Deep Learning and Image Segmentation | With the improvement of product surface quality requirements in industrial production, machine vision
Due to the growing pressure of environmental pollution and energy crisis, electric vehicles (EVs) have become the future development trend. At the same time, due to the increasing proportion of new energy in power generation , the energy storage system is also developing rapidly nefited from high power density and long service life, Lithium-ion
Download Citation | On Nov 17, 2023, Lei Yuan and others published SGNet:A Lightweight Defect Detection Model for New Energy Vehicle Battery Current Collectors | Find, read and cite all the
Download Citation | On Jan 1, 2024, Sara Sepasiahooyi and others published Fault Detection of New and Aged Lithium-ion Battery Cells in Electric Vehicles | Find, read and cite all the research you
The model-based methods mainly include the parameter estimation method, state estimation method, parity space method, and structural analysis method .The method is mainly based on establishing a clear physical model of the battery system, comparing the measurable signals with the model-generated signals to obtain the residual signals, and comparing the residual signals
This paper introduces an autoencoder-enhanced regularized prototypical network for New Energy Vehicle (NEV) battery fault detection. An autoencoder is first deployed
a new battery anomaly detection method based on time series clustering. Lee et. al. present an efficient single-model strategy for offline detection of faulty batteries in UPS systems utilizing isolation forest and hyper-parameter adjustment. A multi-model solution also covers online anomaly detection,
We identified a gap in the existing BESS defense research and formulated new types of attacks against a BESS and their detection methods. The attack detection is divided into a forecast-based approach and long-term pattern analysis. T1 - Cyberattack detection methods for battery energy storage systems. AU - Kharlamova, Nina. AU - Træhold
2.1 Experimental setup for data collection. To develop a reliable CNN-based detection model, a large-scale surface scratch database is essential. To extend the database scale, cylinder-on-flat sliding tests (see Fig. 1) were conducted under a wide range of operation conditions listed in Table 1.The cylinder-on-flat sliding setup has been used to mimic the
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.
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.
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.
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.
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.
Comparison of the detection models for defects on the side and bottom of the battery case. The performances of DSSD, Faster R-CNN, YOLOX, and YOLOv5 are poor in the detection of defects on cylindrical battery cases.
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