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
Research on power battery anomaly detection method based on
This paper proposes a novel network structure for power battery anomaly detection based on an improved TimesNet. Firstly, the original battery data undergo
Detection and Fault Diagnosis of High-Voltage System
Composition of high voltage equipment for new energy vehicles 2.1. Power Battery Pack.
Toward the ensemble consistency: Condition-driven ensemble
Currently, many traditional energy sources, such as oil, natural gas, and coal, are accelerating global climate change, posing serious challenges to the sustainable development of energy [1], [2] pared with traditional energy storage facilities, lithium-ion batteries (LIBs) have the advantages of high energy density, high efficiency, longer lifespan, and less pollution, showing
A GPR-EDM-UPF framework with false data detection and
The current research on SOH estimation at home and abroad mainly includes model-based, data-driven, and fusion technology prediction methods. Model-based methods primarily fit the external characteristics of the battery through electrochemical, equivalent circuit, or empirical models, and then utilize filtering methods for parameter identification to achieve
Fault detection method for electric vehicle battery pack based on
Initially, a wavelet threshold denoising algorithm is used to effectively remove voltage data noise while retaining fault characteristics. Subsequently, an early fault warning
Active Passive Hybrid Binocular Intelligent Detection System for New
This paper introduces a new energy battery active-passive hybrid binocular intelligent inspection system, using structured light and laser line-scan instruments to acquire battery surface image information. Based on the existing 3D reconstruction technology, the active-passive hybrid binocular system is designed. In order to reduce the interference of multiple factors, the 3D
An early diagnosis method for overcharging thermal runaway of energy
The existing diagnosis methods for TR caused by overcharging in LIBs usually involve feature measurements based on voltage, gas, or cell temperature [[10], [11], [12]] terms of voltage-based detection, Zhong et al. [13] conducted thermal runaway tests on 18,650 batteries, indicating that the drastic voltage drop occurs between 127 and 409 s before
Spectrum-Sensing Method for Arc Fault Detection in Direct
We mainly study the detection of arc faults in the direct current (DC) system of lithium battery energy storage power station. Lithium battery DC systems are widely used, but traditional DC protection devices are unable to achieve adequate protection of equipment and circuits. We build an experimental platform based on an energy storage power station with
A new concept to improve the lithium plating detection
International Journal of Smart Grid and Clean Energy . A new concept to improve the lithium plating detection sensitivity in lithium-ion batteries . WMG, The University of Warwick, Coventry CV4 7AL, United Kingdom . Abstract Lithium plating significantly reduces the lifetime of lithium-ion batteries and may even pose a safety risk in the form
An end-to-end Lithium Battery Defect Detection Method Based
The DETR model is often affected by noise information such as complex backgrounds in the application of defect detection tasks, resulting in detection of some targets is ignored. In this paper, AIA DETR model is proposed by adding AIA (attention in attention) module into transformer encoder part, which makes the model pay more attention to correct defect
Research progress in fault detection of battery systems: A review
As electric vehicles advance in electrification and intelligence, the diagnostic approach for battery faults is transitioning from individual battery cell analysis to
The Remaining Useful Life Forecasting
Energy storage has a flexible regulatory effect, which is important for improving the consumption of new energy and sustainable development. The remaining useful life (RUL)
Data-driven Thermal Anomaly Detection for Batteries using
Abstract—For electric vehicles (EV) and energy storage (ES) batteries, thermal runaway is a critical issue as it can lead There are three mainstream methods for battery fault/anomaly detection: knowledge-based, model-based, and data-driven [1]. tage of this new method is the potential to give early warnings.
Welding defects on new energy batteries based on
Request PDF | Welding defects on new energy batteries based on 2D pre-processing and improved-region-growth method in the small field of view | The assessment of welding quality in battery shell
Research on a fast detection method of self-discharge of lithium battery
There are some difficulties in the above methods, as shown in Table 1. In view of these difficulties, according to the characteristics of lithium battery self-discharge and the influence of polarization, and combined with the OCV-SOC curve of each cell, the OCV of each cell in a short time after charging is analyzed in order to realize the rapid detection of self
Sensor Fault Detection, Isolation, and Estimation in Lithium-Ion Batteries
Using the residual between the true SOC and estimated SOC of the battery in [22], a fault detection method was addressed for voltage and current sensors.
Cyberattack detection methods for battery energy storage
We identified a gap in the existing BESS defense research and formulated new types of attacks against a BESS and their detection methods. The attack detection is divided into a forecast-based approach and long-term pattern analysis. T1 - Cyberattack detection methods for battery energy storage systems. AU - Kharlamova, Nina. AU - Træhold
Semantic segmentation supervised deep-learning
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
A Neural Network Based Method for Thermal Fault
DOI: 10.1109/TIE.2020.2984980 Corpus ID: 218806735; A Neural Network Based Method for Thermal Fault Detection in Lithium-Ion Batteries @article{Ojo2021ANN, title={A Neural Network Based Method for Thermal
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
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
Cyberattack detection methods for battery energy storage
Cyberattack detection methods for battery energy storage systems. Author links open overlay panel Nina Kharlamova, new methods such as Luenberger observer, The failure to use proper adjustments: hyper-parameters, activation function, and the inability of the model to work under uncertainties might result in the failure to detect a
A novel semi-supervised fault detection and isolation method for
Global problems such as environmental pollution and energy depletion have been greatly alleviated by the arrival of electric vehicles (EVs) [1, 2].Lithium-ion batteries have become the main power source for EVs due to their high energy density, high power density, long life, and no memory effect [3, 4].However, with the rapid development of EVs, the frequency of
Anomaly Detection Method for Lithium-Ion
Therefore, timely and accurate detection of abnormal monomers can prevent safety accidents and reduce property losses. In this paper, a battery cell anomaly detection
A new on-line method for lithium plating detection in lithium-ion batteries
This study aims to extend recent work, by proposing a new method of lithium plating detection, based on an estimation of cell impedance. (Li-ion) batteries, as an energy storage solution, due to their low weight, high energy density and long service life [1,2]. Within Li-ion batteries, there are many variants that employ different types of
A Welding Defect Detection Method for Battery Pole Based on
Welding defect detection plays an important role in the quality control of new energy batteries. Since the traditional manual detection methods are not intelligent enough and cost a lot, many deep learning algorithms have been proposed. With the development of detection technology, the Yolo series of algorithms have been applied to various detection tasks. Focus
Leak Detection of Lithium-Ion Batteries and Automotive
communication base stations, and new energy Leak Detection of Lithium‑Ion Batteries and Automotive Components Helium leak testing for the automotive industry. 2 (HMSLD) is the preferred method for testing in lithium-ion battery manufacturing. Keywords: Leak test; battery; automotive; lithium ion; HLD; PHD-4; cooling line
A Neural Network Based Method for Thermal Fault
DOI: 10.1109/TIE.2020.2984980 Corpus ID: 218806735; A Neural Network Based Method for Thermal Fault Detection in Lithium-Ion Batteries @article{Ojo2021ANN, title={A Neural Network Based Method for
New energy battery detection method-testingencyclopedia
Impedance spectroscopy is a method for measuring the impedance of a battery. By applying an AC voltage to the battery and analyzing the resulting current, researchers can determine the
A new on-line method for lithium plating detection in lithium-ion batteries
A number of studies advocate the use of lithium-ion (Li-ion) batteries, as an energy storage solution, due to their low weight, high energy density and long service life [1, 2].Within Li-ion batteries, there are many variants that employ different types of negative electrode (NE) materials such as graphite [3, 4] and lithium titanium oxide (LTO) [5, 6].
Anomaly Detection Method of New Energy Vehicle Battery Based
Abstract: The battery anomaly detection is critical in new energy vehicle batteries, however it has an issue with erroneous performance positioning. The typical Decision tree algorithm is unable
6 FAQs about [Correct detection method for new energy batteries]
Can model-based fault detection be used in battery management system?
In this paper, a novel model-based fault detection in the battery management system of an electric vehicle is proposed. Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults, considering the impact of battery aging.
What is the diagnostic approach for battery faults?
As electric vehicles advance in electrification and intelligence, the diagnostic approach for battery faults is transitioning from individual battery cell analysis to comprehensive assessment of the entire battery system. This shift involves integrating multidimensional data to effectively identify and predict faults.
What are the analysis and prediction methods for battery failure?
At present, the analysis and prediction methods for battery failure are mainly divided into three categories: data-driven, model-based, and threshold-based. The three methods have different characteristics and limitations due to their different mechanisms. This paper first introduces the types and principles of battery faults.
Can a battery cell anomaly detection method prevent safety accidents?
Therefore, timely and accurate detection of abnormal monomers can prevent safety accidents and reduce property losses. In this paper, a battery cell anomaly detection method is proposed based on time series decomposition and an improved Manhattan distance algorithm for actual operating data of electric vehicles.
How to detect faults in a battery?
Different fault detection approaches based on model, signal-processing, or knowledge can be applied for the battery. The model-based approaches consider an electrochemical model or an equivalent circuit model, to detect faults.
Why is voltage anomaly important in battery anomaly detection?
Many existing studies have shown that when there are various abnormal faults in the battery, the voltage of the battery exhibits more pronounced fluctuations compared to other data during abnormal conditions. Therefore, voltage anomaly is an extremely important fault indicator in battery anomaly detection.