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.
Direct use of parameters such as ultrasonic amplitude, frequency, and ToF for SOC estimation has accuracy issues, but ultrasonic detection methods have a wealth of data available for analyzing the internal state of the battery. These features make it possible to implement the ultrasonic method using data-driven approaches. Fig. 4.
Moreover, ultrasonic technology allows for real-time monitoring and imaging, facilitating prompt detection of internal defects in batteries. It also enables personalized detection solutions tailored to different battery types.
Investigations revealed that the failure was attributed to an internal defect specifically related to an unusual deposition of lithium metal and dendrite growth within the battery cells during operation. However, in this incident, it appears that the battery management system (BMS) did not detect the battery defect that led to the fire.
Defect detection within LIBs requires advanced methodologies for three-dimensional defect localization, enabling the differentiation of electrodes, separators, and aluminum-plastic films within the battery layers.
This method is particularly sensitive to local defects on the battery's anode and has the advantages of low inspection requirements and simple operation, with clear potential for in situ monitoring.
Smart battery equipped with potential sensor is proposed for real-time monitoring. The potential sensing separator enables long-term and non-invasive monitoring. It allows for early defect detection, unattainable through external measures alone. Multi-point sensing system provides potential distribution and defect location.
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.
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 study not only provides a new approach to assessing lithium-ion battery health but also demonstrates the significant capabilities of digital twin technology in processing and predicting real-time data, offering valuable insights …
Liu, H., Hao, S., Han, T., et al. (2023). Random forest-based online detection and location of internal short circuits in lithium battery energy storage systems with limited number of sensors. IEEE T. Instrum. Meas. 72: 1−11. DOI: 10.1109/TIM.2023.3304674. View in Article CrossRef Google Scholar [41]
To enhance the performance of deep learning-based defect detection models for new energy vehicle battery current collectors, this paper designs inspiration from existing …
Smart batteries, i.e., equipped with internal and external sensors, are emerging as promising solutions to enhance battery state of health and optimize operating conditions.
The diagnosis of an internal short circuit (ISC) fault is an integral part of thermal runaway warning for lithium-ion batteries. A higher level of accuracy in ISC fault diagnosis needs an artificial intelligence model, but lack of fault data and label …
As the core component for battery energy storage systems and electric vehicles, lithium-ion batteries account for about 60% of vehicular failures and have the characteristics of the rapid spread ...
Here we report a new strategy for improving safety by designing a smart battery that allows internal battery health to be monitored in situ. Specifically, we achieve early detection of...
With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale deployment of lithium batteries has also brought a …
The methods of detection of internal SC and measurement of self-discharge have been discussed in literature for decades (1-9). However, for broad industry adoption, a method should be fast, accurate, non-destructive, and applicable to any cellchemistry or battery design. Recently, we proposed a new fast approach to the detection of Li-ion cell
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 reusable battery PL was calculated at $234–278·MWh −1, whereas new battery power cost $211·MWh −1. They concluded that reusable batteries are not cost-effective although their initial costs are much lower. The new battery cost estimates from Steckel et al. were $151·kWh −1, and the one from Kamath et al. were $209·kWh −1.
One major concern that can affect Li-ion battery performance is thermal runaway, which can cause dangerous battery fires. Internal short circuits (ISCs) are believed to be the root cause of thermal runaway incidents in batteries, making early detection of spontaneous ISCs a critical diagnostic task. This study presents a new and simple early ...
In this study, we propose a new internal short detection method by using cell swelling information during the early stages of a battery heating caused by an internal short circuit. By measuring …
Here we report a new strategy for improving safety by designing a smart battery that allows internal battery health to be monitored in situ. Specifically, we achieve early detection of...
This study not only provides a new approach to assessing lithium-ion battery health but also demonstrates the significant capabilities of digital twin technology in processing …
In fault diagnosis, the capacitance and internal resistance of power batteries are important detection indicators for whether there is a fault in the battery. The calculation of …
Internal short circuit (ISCr) is one of the major obstacles to the improvement of the battery safety. The ISCr may lead to the battery thermal runaway and is hard to be detected in the early stage. In this work, a new ISCr detection method based on the symmetrical loop circuit topology (SLCT) is introduced. The SLCT ensures that every battery has the same priority in …
Moreover, ultrasonic technology allows for real-time monitoring and imaging, facilitating prompt detection of internal defects in batteries. It also enables personalized …
Moreover, ultrasonic technology allows for real-time monitoring and imaging, facilitating prompt detection of internal defects in batteries. It also enables personalized detection solutions tailored to different battery types.
In this study, we propose a new internal short detection method by using cell swelling information during the early stages of a battery heating caused by an internal short circuit. By measuring cell expansion force, higher confidence level detection can be achieved for an internal short circuit in an electric vehicle scale battery pack.
Battery Internal Short Circuit Detection Mingxuan Zhang, Minggao Ouyang, Languang Lu et al.-This content was downloaded from IP address 40.77.167.201 on 19/05/2023 at 11:39. A2738 Journal of The Electrochemical Society, 164 (12) A2738-A2745 (2017) Fusing Phenomenon of Lithium-Ion Battery Internal Short Circuit Mingxuan Zhang,a,b,z Lishuo Liu,a Anna …
The diagnosis of an internal short circuit (ISC) fault is an integral part of thermal runaway warning for lithium-ion batteries. A higher level of accuracy in ISC fault diagnosis needs an artificial intelligence model, but lack of fault data and label ambiguity present challenges. To address these demands and challenges, features are extracted ...
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 …
Smart battery equipped with potential sensor is proposed for real-time monitoring. The potential sensing separator enables long-term and non-invasive monitoring. It allows for early defect detection, unattainable through external measures alone. Multi-point sensing system provides potential distribution and defect location.
Smart batteries, i.e., equipped with internal and external sensors, are emerging as promising solutions to enhance battery state of health and optimize operating conditions.
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