Fault detecting is crucial for the safety of the lithium-ion battery. This is because thermal fault and sensor fault are the most common fault in battery, and it may be catastrophic. This study explores a novel fault detection scheme for the cylindrical lithium-ion battery. In this scheme, for the modeling simplicity and physical realism, an electrothermal model is adopted
In this article, an online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current,
In particular, we offer (1) a thorough elucidation of a general state–space representation for a faulty battery model, involving the detailed formulation of the battery system state vector and the identification of system parameters; (2) an elaborate exposition of design principles underlying various model-based state observers and their
Lithium deposition on anode surfaces can lead to fast capacity degradation and decreased safety properties of Li-ion cells. To avoid the critical aging mechanism of lithium
By analyzing the data of three actual electric vehicles in operation, it is shown that the method proposed in this paper can effectively and accurately detect an abnormal battery cell in a lithium-ion battery pack. Compared with other methods, the proposed method has more advantages, and the results show that this method exhibits strong
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
Lithium-ion battery Lithium plating Electrochemical model Impedance On-line detection. 1. Introduction. The rapid development of electric vehicles (EVs) has promoted an electrification revolution in the transportation sector [1, 2]. As the core power source, the energy density, power capability, durability and safety of power batteries determine the performance
Dans le dernier article, nous avons présenté le connaissances techniques approfondies sur la cellule lithium-ion, nous commençons ici à introduire davantage la carte de protection de la batterie au lithium et les connaissances
realizes the rapid and accurate identification of lithium battery defects in the rapid production process of auto-matic production line. Keywords: lithium battery, defect detection, artificial intelligence, whale algorithm 1 Introduction The preparation of manuscripts which are to be reproduced by photo-offset requires special care. Papers submit-
Experiments show that AIA DETR model can well detect the defect target of lithium battery, effectively reduce the missed detection problem, and reach 81.9% AP in the lithium battery
Fault injection is achieved by manipulating line connections and operations in the experimental setup. For instance, at 736 s, the connection between batteries is intentionally disconnected to simulate an open circuit fault, with the fault duration set to 30 s, causing the current to return to
Surface Defect Detection System for Lithium Battery. Wintriss surface inspection system can implement online detection of defects on the surface of battery separator films, battery electrodes and aluminum laminated films through the principle of machine vision inspection, while providing exact product quality information. The artificial intelligence-based defect detection system
Experiments show that AIA DETR model can well detect the defect target of lithium battery, effectively reduce the missed detection problem, and reach 81.9% AP in the lithium battery defect data set. Conferences > 2023 5th International Confer...
Fault injection is achieved by manipulating line connections and operations in the experimental setup. For instance, at 736 s, the connection between batteries is intentionally disconnected to simulate an open circuit fault, with the fault duration set to 30 s, causing the current to return to zero. At 2947 s, a circuit breaker is connected in parallel with the battery to simulate a short
Lithium deposition on anode surfaces can lead to fast capacity degradation and decreased safety properties of Li-ion cells. To avoid the critical aging mechanism of lithium deposition, its detection is essential. We present workflows for the efficient detection of Li deposition on electrode and cell level. The workflows are based on a variety
The DDCNet-YOLO algorithm model was proposed based on the deformable convolution and YOLOv5, aiming at the complex lithium battery electrode surface with multiple small object defects and large aspect ratio object defects at the same time. The deformable downsampling convolution network (DDCNet) was constructed in the backbone. The context
In this article, an online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current, voltage, and temperature sensor faults, short-circuit faults, and connection faults.
This paper provides a comprehensive review of various fault diagnostic algorithms, including model-based and non-model-based methods. The advantages and disadvantages of the reviewed algorithms, as well as
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
The DDCNet-YOLO algorithm model was proposed based on the deformable convolution and YOLOv5, aiming at the complex lithium battery electrode surface with multiple
As a result, the worldwide usage of lithium will rise as the use of lithium batteries rises. Therefore, a quick and precise technique for identifying lithium is critical in exploration to fulfill
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 detection approach is proposed based on improved K-nearest neighbor (KNN) and Euclidean clustering segmentation. Firstly, an improved voxel density strategy for KNN is
By analyzing the data of three actual electric vehicles in operation, it is shown that the method proposed in this paper can effectively and accurately detect an abnormal battery cell in a lithium-ion battery pack.
Abusive lithium-ion battery operations can induce micro-short circuits, which can develop into severe short circuits and eventually thermal runaway events, a significant safety concern in
This paper provides a comprehensive review of various fault diagnostic algorithms, including model-based and non-model-based methods. The advantages and disadvantages of the reviewed algorithms, as well as some future challenges for Li-ion battery fault diagnosis, are also discussed in this paper.
Rather than the noise information on the image, so as to improve the detection ability of lithium battery surface defects. Experiments show that AIA DETR model can well detect the defect target of lithium battery, effectively reduce the missed detection problem, and reach 81.9% AP in the lithium battery defect data set Published in: 2023 5th International Conference on Intelligent
• Peak to background for trace detection •Windowless • Eliminate absorption of Li x-rays in the window . 7 . Proprietary & Confidential . Building an EDS that detects Li x-rays . Low energy x-rays have a difficult time getting to the SDD module . Low energy x-ray SDD module. Light element window. Collimator . On-chip FET location . 8 . Proprietary & Confidential . The
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
In particular, we offer (1) a thorough elucidation of a general state–space representation for a faulty battery model, involving the detailed formulation of the battery system state vector and
Abusive lithium-ion battery operations can induce micro-short circuits, which can develop into severe short circuits and eventually thermal runaway events, a significant safety concern in lithium-ion battery packs. This paper aims to detect and quantify micro-short circuits before they become a safety issue. We develop offline batch least
Experiments show that AIA DETR model can well detect the defect target of lithium battery, effectively reduce the missed detection problem, and reach 81.9% AP in the lithium battery defect data set Conferences > 2023 5th International Confer...
The algorithm uses an electrochemical model, with the first step being estimating the insertion and extraction rates of Li ions using particle filtering, and the second step being the comparison of the estimated data with the boundary condition to generate alarms at faulty state.
Abstract: Various faults in the lithium-ion battery system pose a threat to the performance and safety of the battery. However, early faults are difficult to detect, and false alarms occasionally occur due to similar features of the faults.
Non-model-based methods, particularly data-driven methods, can have a crucial role in predicting battery behavior as it degrades and aiding the model development process. Therefore, the most effective approach for Li-ion battery fault diagnosis should be a combination of both model-based and non-model-based methods. Table 1.
Therefore, the most effective approach for Li-ion battery fault diagnosis should be a combination of both model-based and non-model-based methods. Table 1. Summary of Lithium-ion (Li-ion) fault diagnostic algorithms.
There has not been an effective and practical solution to detect and isolate all potential faults in the Li-ion battery system. There are several challenges in Li-ion battery fault diagnosis, including assumption-free fault isolation, fault threshold selection, fault simulation tools development, and BMS hardware limitations.
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