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New energy battery detection function

Towards Automatic Power Battery Detection: New Challenge

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A method for battery fault diagnosis and early warning

Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the sliding window (SW)

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Towards Automatic Power Battery Detection: New Challenge,

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 accuracy and

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Autoencoder-Enhanced Regularized Prototypical Network for New

This paper leverages Baidu''s New Energy Vehicle (NEV) live operation data as the foundation for experimentation. Multiple sensors are implemented to monitor the new energy battery, taking measurements of the battery pack''s voltage, current, and temperature, and

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Autoencoder-Enhanced Regularized Prototypical Network for New Energy

DOI: 10.1016/j nengprac.2023.105738 Corpus ID: 264328871; Autoencoder-Enhanced Regularized Prototypical Network for New Energy Vehicle battery fault detection @article{Sun2023AutoencoderEnhancedRP, title={Autoencoder-Enhanced Regularized Prototypical Network for New Energy Vehicle battery fault detection}, author={Gangfeng Sun

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Safety management system of new energy vehicle power battery

Therefore, the fault diagnosis model based on WOA-LSTM algorithm proposed in the study can improve the safety of the power battery of new energy battery vehicles and

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DGNet：新能源汽车电池集电器的自适应轻量级缺陷检测模型,IEEE

为了降低应用成本并利用有限的计算资源进行实时检测,我们提出了一种用于电池集流器（BCC）的端到端自适应轻量级缺陷检测模型DGNet。首先,我们设计了一个自适应轻量级主干网络（DOConv 和 Shufflenet V2 (DOS) 模块）,以沿着内核空间的所有四个维度自适应地提取有用的特征,同时保持较低的计算复杂度。其次,我们设计了一种轻量级的特征融合网

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Time Series Prediction of New Energy Battery SOC Based on

4.1 Data Preparation and Processing. The dataset used in the experiment is mainly divided into two parts, the dataset as a whole has a total of 5112 rows with a small base, the first part is mainly the original data of the new energy battery samples containing Time, Vehiclestatus, Chargestatus, Summileage, Sumvoltage, Sumcurrent, Soc, Gearnum,

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SmartSafe P01 Electric Vehicles Battery Detector

P01, a "special inspection level" in-depth inspection equipment launched by SmartSafe for electric vehicle battery inspection. It not only integrates battery pack detection, detailed status information and fault information of the battery pack, but also has the detection function of the whole vehicle system, and supports diagnostic functions such as code reading, code clearing, reading data

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Autoencoder-Enhanced Regularized Prototypical Network for New Energy

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

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Safety management system of new energy vehicle power battery

Therefore, the fault diagnosis model based on WOA-LSTM algorithm proposed in the study can improve the safety of the power battery of new energy battery vehicles and reduce the probability of safety accidents during the driving process of new energy vehicles.

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DCS-YOLO: Defect detection model for new energy vehicle battery

To enhance the performance of deep learning-based defect detection models for new energy vehicle battery current collectors, this paper designs inspiration from existing literature and designs a defect detection model based on deformable convolution and attention mechanisms: DCS-YOLO.

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Detection and Fault Diagnosis of High-Voltage System

Taking the leakage detection of byd-qin hybrid high-voltage system as an example, this paper analyzes the fault generation mechanism and puts forward the detection technology of new energy

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DGNet: An Adaptive Lightweight Defect Detection Model for New

In order to reduce application costs and conduct real-time detection with limited computing resources, we propose an end-to-end adaptive and lightweight defect detection

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New energy vehicle battery recycling strategy considering carbon

The negative impact of used batteries of new energy vehicles on the environment has attracted global attention, and how to effectively deal with used batteries of new energy vehicles has become a

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Advancing fault diagnosis in next-generation smart battery with

Uncovering subtle battery behavior changes for improved fault detection. Specific focus on multidimensional signals to enhance safety strategies. Future trends in battery fault diagnosis driven by AI and multidimensional data.

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Towards Automatic Power Battery Detection: New Challenge

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

Get Price

Towards Automatic Power Battery Detection: New

Get Price

Advancing fault diagnosis in next-generation smart battery with

Uncovering subtle battery behavior changes for improved fault detection. Specific focus on multidimensional signals to enhance safety strategies. Future trends in

Get Price

Comprehensive testing technology for new energy vehicle power batteries

Unscented particle filtering is used to improve particle swarm optimization and battery detection model. The study tested four various models of lithium-ion batteries. The model predicted a mean square error of 0.0011 for battery 5, 0.0007 for battery 6, 0.0022 for battery 7, and 0.0013 for battery 18.

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Fault diagnosis of new energy vehicles based on improved

The new energy vehicle system is in the initial stage of application, so the probability of fault is greater. Therefore, its reliability urgently needs to be improved. In order to improve the fault diagnosis effect of new energy vehicles, this paper proposes a fault diagnosis system of new energy vehicle electric drive system based on improved machine learning and

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Abnormal sensing feature detection of DC high voltage power battery

DOI: 10.2478/amns-2024-3205 Corpus ID: 273805239; Abnormal sensing feature detection of DC high voltage power battery for new energy vehicles @article{Chen2024AbnormalSF, title={Abnormal sensing feature detection of DC high voltage power battery for new energy vehicles}, author={Yuanhua Chen and Yanping Yang and Lifeng Wang}, journal={Applied

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DCS-YOLO: Defect detection model for new energy vehicle battery

To enhance the performance of deep learning-based defect detection models for new energy vehicle battery current collectors, this paper designs inspiration from existing

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DGNet：新能源汽车电池集电器的自适应轻量级缺陷检测模型,IEEE

为了降低应用成本并利用有限的计算资源进行实时检测,我们提出了一种用于电池集流器（BCC）的端到端自适应轻量级缺陷检测模型DGNet。首先,我们设计了一个自适

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Semantic segmentation supervised deep-learning algorithm for

Request PDF | Semantic segmentation supervised deep-learning algorithm for welding-defect detection of new energy batteries | As the main component of the new energy battery, the safety vent

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A method for battery fault diagnosis and early warning combining

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Comprehensive testing technology for new energy vehicle power batteries

As the new energy industry continues to progress, the health management of power batteries has become the key to ensuring the performance and safety of automobiles. Therefore, accurately predicting battery capacity decline is particularly important. A battery capacity degradation prediction model combining unscented particle filtering, particle swarm

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DGNet: An Adaptive Lightweight Defect Detection Model for New Energy

In order to reduce application costs and conduct real-time detection with limited computing resources, we propose an end-to-end adaptive and lightweight defect detection model for the battery current collector (BCC), DGNet. First, we designed an adaptive lightweight backbone network (DOConv and Shufflenet V2 (DOS) module) to adaptively extract

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Comprehensive testing technology for new energy vehicle power

Unscented particle filtering is used to improve particle swarm optimization and battery detection model. The study tested four various models of lithium-ion batteries. The

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Autoencoder-Enhanced Regularized Prototypical Network for New Energy

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New energy battery detection function [PDF]

6 FAQs about [New energy battery detection function]

Why is re important in battery research and development?

The presence of the RE serves as a valuable in-situ diagnostic tool in battery research and development, offering the following advantages: (1) Decoupling and distinguishing the potentials of the positive and negative electrodes, allowing for the assessment of each electrode's unique contribution to the overall battery capacity.

How can Advanced Battery Sensor technologies improve battery monitoring and fault diagnosis capabilities?

Herein, the development of advanced battery sensor technologies and the implementation of multidimensional measurements can strengthen battery monitoring and fault diagnosis capabilities.

What are battery sensors used for?

Sensors have been developed and designed for diverse scenarios, enabling real-time, in-situ monitoring of the internal and external states of batteries across electrical, thermal, mechanical, gas, acoustic, and optical dimensions. However, their applications in battery fault diagnosis still grapple with the following deficiencies and challenges:

Can external sensors detect a battery's internal reaction?

Currently, external sensors provide limited clarity in characterizing these internal reactions and exhibit slow response. Research has shown that under high-rate charge and discharge conditions, the temperature difference between the inside and outside of the battery can reach up to 15 °C .

How does a battery eddy current sensor work?

Utilizing alternating current (AC) excitation in the coil, it generates a reverse magnetic field on the aluminum casing of the battery, influencing the coil impedance. They further integrated the eddy current sensor with a platinum RTD to create a flexible thin-film sensor, enabling the combined measurement of battery temperature and expansion.

How can feature engineering improve battery safety?

Techniques such as principal component analysis and feature engineering enable the efficient selection of highly correlated feature signals for battery safety. It eliminates redundant signals and optimizes sensor placement, achieving an optimal fusion of multidimensional information.