Une batterie d''ordinateur portable comme la batterie de son smartphone ou n''importe quelle batterie est un composant qui a (malheureusement) une durée de vie limitée. La durée de vie d''une batterie d''ordinateur portable peut varier selon la capacité et la qualité de la batterie, son utilisation, la manière de la recharger La durée de vie moyenne d''une batterie est de 2 à 3
Lithium-ion batteries are extensively used in electric vehicles, aerospace, communications, healthcare, and other sectors due to their high energy density, long lifespan, low self
The utility model discloses a pure electric vehicle power battery insulating resistance on-line detection circuit. The pure electric vehicles power battery insulating resistance on-line detection circuit is mainly composed of a voltage division unit and a voltage acquisition unit, the above voltage division unit comprises a resistor R1 and the switches K1 and K2, and the above
In this research, a new framework for fault detection, fault classification, and localization is proposed, as depicted in Fig. 3 ing MATLAB Simulink, voltage–current data representing 11 distinct fault types were created in a simulation of a
3 天之前· A low self-discharge rate, memoryless effect, and high energy density are the key features that make lithium batteries sustainable for unmanned aerial vehicle (UAV)
To reduce the excessive power consumption and eliminate the battery voltage imbalance caused in conventional method, a novel broken line detection scheme for Li-ion battery protection integrated circuits (ICs) is presented in this study.
ject detection-based solutions, corner detectors and cout-ing methods with our segmentation-based MDCNet. We directly visualize the predicted results (MDCNet: Segmen-tation map, Others: Bounding box, Corner map, Density
Line-fault detection for PoC. Line-Fault Internal Circuitry. Inside the chip, the line-fault detector has a multilevel comparator to detect the possible line conditions: short to ground, short to the battery, a disconnected cable, and normal operation. This is shown in Figure 3. Table 1 lists the MAX96706 line-fault detection input comparator
We conduct a comprehensive study on a new task named power battery detection (PBD), which aims to localize the dense cathode and anode plates endpoints from X
Lithium-ion batteries are extensively used in electric vehicles, aerospace, communications, healthcare, and other sectors due to their high energy density, long lifespan, low self-discharge rate, and environmentally friendly characteristics (Xu et al., 2024a).However, complex operating conditions and improper handling can lead to various issues, including accelerated aging,
This work proposes a novel data-driven method to detect long-term latent fault and abnormality for electric vehicles (EVs) based on real-world operation data. Specifically, the battery fault features are extracted from the incremental capacity (IC) curves, which are smoothed by advanced filter algorithms. Second, principal component analysis
The accuracy and effectiveness of the proposed model are verified by the image dataset collected from the actual power battery production lines. Finally, edge detection and circular Hough transform are used to post-process the segmented weld image to obtain the weld information. In summary, the main contributions of the paper are outlined as follows: (1) A two
本文提出一个动力电池检测(pbd)视觉新任务！并创建了x 射线 pbd 数据集,包含从 5 个制造商的数千张动力电池中选取的 1,500 张不同的 x 射线图像,具有 7 种不同的视觉干扰,还提出一种新颖的基于分割的 pbd 解决方案,称为多维协作网络 (mdcnet),代码和数据集刚刚开源！
We conduct a comprehensive study on a new task named power battery detection (PBD), which aims to localize the dense cathode and anode plates endpoints from X
We conduct a comprehensive study on a new task named power battery detection (PBD), which aims to localize the dense cathode and anode plates endpoints from X-ray images to evaluate the quality of power batteries. Existing manufacturers usually rely on human eye observation to complete PBD, which makes it difficult to balance the
We conduct a comprehensive study on a new task named power battery detection (PBD), which aims to localize the dense cathode and anode plates endpoints from X-r
A novel network structure for power battery anomaly detection based on an improved TimesNet is proposed, achieving an improvement of 1%–19% in the F1 value and 1%–3% in the ACC compared to the other models. Health monitoring and abnormality detection of power batteries for new energy vehicles has been one of the hot topics in recent years.
We conduct a comprehensive study on a new task named power battery detection (PBD), which aims to localize the dense cathode and anode plates endpoints from X-ray images to evaluate the quality of power batteries.
With the development of power battery technology, new energy vehicles are receiving more and more attention. The power battery is the only source of driving energy for battery electric vehicle (BEV), which directly affects the power performance, endurance and safety of BEV [44].To ensure the safety of power battery, the functional evaluation has to be done through power battery
本文提出一个动力电池检测(pbd)视觉新任务！并创建了x 射线 pbd 数据集,包含从 5 个制造商的数千张动力电池中选取的 1,500 张不同的 x 射线图像,具有 7 种不同的视觉干扰,还提出一种
We conduct a comprehensive study on a new task named power battery detection (PBD) which aims to localize the dense cathode and anode plates endpoints from X-ray images to evaluate the quality of power batteries.
This paper provides a comprehensive review of transmission line fault detection methods. Keywords: transmission lines, power system, fault detection, power losses, reliability, fault analysis I. INTRODUCTION The topic for the project has been selected as the "Electrical Fault Detection In Transmission Line". It is a totally a
We conduct a comprehensive study on a new task named power battery detection (PBD) which aims to localize the dense cathode and anode plates endpoints from X-ray images to evaluate
ject detection-based solutions, corner detectors and cout-ing methods with our segmentation-based MDCNet. We directly visualize the predicted results (MDCNet: Segmen-tation map,
We conduct a comprehensive study on a new task named power battery detection (PBD), which aims to localize the dense cathode and anode plates endpoints from X-ray images to evaluate the quality of power batteries.
Moreover, the limitations in battery technology result in restrict flight times, High speed automatic power line detection and tracking for a UAV-based inspection. 2012 International Conference on Industrial Control and Electronics Engineering (2012), pp. 266-269, 10.1109/ICICEE.2012.77. Google Scholar [12] Sa I., Corke P. Close-quarters quadrotor flying
3 天之前· A low self-discharge rate, memoryless effect, and high energy density are the key features that make lithium batteries sustainable for unmanned aerial vehicle (UAV) applications which motivated recent works related to batteries, where UAV is important tool in navigation, exploration, firefighting, and other applications. This study focuses on detecting battery failure
This work proposes a novel data-driven method to detect long-term latent fault and abnormality for electric vehicles (EVs) based on real-world operation data. Specifically,
The signal from the detection device is able to locate underground power lines by penetrating the plastic conduit and detecting the metallic wire present inside the power line. In some cases, the conduit is made from metal, if that’s the case, then it becomes all the more easier to locate the power line.
In addition, a battery system failure index is proposed to evaluate battery fault conditions. The results indicate that the proposed long-term feature analysis method can effectively detect and diagnose faults. Accurate detection and diagnosis battery faults are increasingly important to guarantee safety and reliability of battery systems.
Based on the features, a cluster algorithm is employed to capture the battery potential failure information. Moreover, the cumulative root-mean-square deviation is introduced to quantificationally analyze the degree of the battery failures using large-scale battery data to avoid the missing fault reports using short-term data.
Specifically, the battery fault features are extracted from the incremental capacity (IC) curves, which are smoothed by advanced filter algorithms. Second, principal component analysis (PCA) algorithm is utilized to reduce dimensionality, and the cumulative percent variance (CPV) is to determine the number of significant features.
Moreover, the cumulative root-mean-square deviation is introduced to quantificationally analyze the degree of the battery failures using large-scale battery data to avoid the missing fault reports using short-term data. In addition, a battery system failure index is proposed to evaluate battery fault conditions.
Abstract: Accurate detection and diagnosis battery faults are increasingly important to guarantee safety and reliability of battery systems. Developed methods for battery early fault diagnosis concentrate on short-term data to analyze the deviation of external features without considering the long-term latent period of faults.
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