In this paper, the fault diagnosis of battery systems in new energy vehicles is reviewed in detail. Firstly, the common failures of lithium-ion batteries are classified, and the triggering mechanism of battery cell failure is briefly analyzed. Next, the existing fault diagnosis methods are described and classified in detail.
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.
Among these, deformations, scratches, dents, and notches are considered serious defects that significantly impact the quality of the battery case, potentially affecting its performance and, in severe cases, leading to unforeseen accidents.
It will accelerate the degradation process, which will eventually lead to premature battery failure, and the whole battery system will also show the progressive fault of capacity fade.
Fan et al. constructed a generalized dimensionless indicator with a tolerance factor and mapped it to 2-dimensional space to represent voltage evolution patterns. Then the local outlier factor algorithm was used to find the anomalies pattern and identify faulty batteries .
Distribution of defects in the cylindrical battery case. To analyse the surface defect characteristics of a cylindrical battery case, most of the defects exist mainly on its cylindrical surface (side) and are affected by the material reflection problem, resulting in complex image acquisition and detection.
YOLOv5
Experimental results show that the proposed YOLOv5s-4Scale-DCN algorithm can effectively detect the defects of new-energy vehicle battery collection panel, with mAP up to 91%, 2. 5% higher than that of the original algorithm, and the FPS reaches 113. 6. There are two types of defects, severe defects and uncovered defects.
CN115266585A
In order to solve the technical problems, the invention provides a system and a method for detecting defects of a new energy battery box, which can automatically detect the defects of...
Can A Brand New Car Battery Be Bad? Signs Of Failure And …
How Can I Test A New Car Battery for Defects? To test a new car battery for defects, utilize a multimeter and a load test, observing voltage readings and performance under load. Multimeter Test: – Measure Voltage: Use a multimeter to test the battery voltage. A healthy, fully charged car battery should read between 12.6 to 12.8 volts when the ...
SGNet:A Lightweight Defect Detection Model for New Energy …
The quality of the current collector, an essential component in new energy vehicle batteries, is crucial for battery performance and significantly impacts the safety of vehicle occupants. However, detecting defects in battery current collector in real-time industrial applications with limited computational resources poses a major challenge. To address this, our paper proposes …
Welding defects on new energy batteries based on 2D pre …
The assessment of welding quality in battery shell production is a crucial aspect of battery production. Battery surface reconstruction can inspect the quality of the weld instead of relying on human inspection. This paper proposes a defect detection method in the small field of view based on 2D pre-processing and an improved-region-growth method. A …
New Energy Lithium Battery Defect Detection | Do3think
New Energy Lithium Battery Defect Detection 2024 5 20 Solar PV and Lithium-Ion Battery 336 0 The traditional method of battery defect detection is manual measurement and judgment, and the detection system of machine vision …
DCS-YOLO: Defect detection model for new energy vehicle battery …
Due to the similarity between defects like the Weld through of battery current collector in new energy vehicles and central hole features, the central holes were labeled as positioning holes to prevent model misidentification. To balance the data for five types of defects and avoid adverse effects, a small number of unclear images were excluded during the labeling process.
Semantic segmentation supervised deep-learning algorithm for …
The experiment results indicate that the welding-defect detection method based on semantic segmentation algorithm achieves 86.704% and the applicability of the proposed framework in industrial applications, which supports the effectiveness of the deep learning model in segmenting defects. As the main component of the new energy battery, the safety vent …
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 …
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 …
Named Entity Recognition of Lithium-ion Battery Defects Based …
The global market for new energy vehicles has been rapidly expanding, driving the development of the lithium-ion battery industry. According to customs statistics, China''s exports of Li-battery increased by 86.6% from the same period in 2021 during the period of January to November 2022. The safe export of Li-battery is highly valued by Chinese customs. Lithium-ion batteries …
Machine vision-based detection of surface defects in cylindrical ...
Cylindrical battery cases are generally produced by stamping equipment, for the defect detection of stamped parts, a lot of research has been carried out at home and abroad, the detection means from the traditional contact measurement to optical measurement technology to the application of machine vision technology, the development is rapid, but for the new …
Autoencoder-Enhanced Regularized Prototypical Network for New Energy ...
As NEV (New Energy Vehicle) battery failures occur only over a small period of time, the collected battery data exhibits a severe class imbalance phenomenon, meaning that the number of normal samples is significantly greater than the number of failure samples (Japkowicz & Stephen, 2002). In fact, Class imbalance problems are a prevalent and challenging issue …
A Review on the Fault and Defect Diagnosis …
In this paper, the fault diagnosis of battery systems in new energy vehicles is reviewed in detail. Firstly, the common failures of lithium-ion batteries are classified, and the …
Research on Improving YOLOv5s Algorithm for Defect Detection …
Abstract—The advancement of new energy vehicles has led to more demanding standards for detecting defects in cylindrical coated lithium batteries. The current research lacks robustness and has low performance. This paper seeks to provide real-time defect identification in cylindrical coated lithium batteries and
Safety management system of new energy vehicle power battery …
The continuous progress of society has deepened people''s emphasis on the new energy economy, and the importance of safety management for New Energy Vehicle Power Batteries (NEVPB) is also increasing (He et al. 2021).Among them, fault diagnosis of power batteries is a key focus of battery safety management, and many scholars have conducted …
DCS-YOLO: Defect detection model for new energy vehicle battery …
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 …
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 …
[PDF] DCS-YOLO: Defect detection model for new energy vehicle …
A cross-domain few-shot learning (FSL) approach for lithium-ion battery defect classification using an improved siamese network (BSR-SNet) is proposed and can be used to classify the surface defects of lithium batteries well.
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 …