Battery safety issue detection in real-world electric vehicles by
Detecting battery safety issues is essential to ensure safe and reliable operation of electric vehicles (EVs). This paper proposes an enabling battery safety issue detection method for real-world EVs through integrated battery modeling and voltage abnormality detection. Firstly, a battery voltage abnormality degree that is adaptive to different battery types and working
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Fault Diagnosis and Abnormality Detection of
the abnormal cell voltage are attained by combining the data analysis method and the v isualization technique. Firstly, the faulty or abnormal battery cells '' voltage is roughly identified and
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Detection and Analysis of Abnormal High
The improvement of battery management systems (BMSs) requires the incorporation of advanced battery status detection technologies to facilitate early warnings of
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Prediction and Diagnosis of Electric Vehicle
Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection
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Fault diagnosis and abnormality detection of lithium-ion battery
For the voltage abnormality, an accurate detection and location algorithm of the abnormal cell voltage are attained by combining the data analysis method and the visualization technique. Firstly, the faulty or abnormal battery cells'' voltage is roughly identified and classified using the K-means clustering algorithm [33].
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Battery safety issue detection in real-world electric vehicles by
To make further statistical analysis on voltage abnormality, the abnormality frequency is defined as (26) On September 5th 2021, a battery fault occurred. Prior to that, all battery abnormality degrees were below 1, and the abnormality frequency was 0. After the fault occurrence, the voltage of the relavant battery cell showed significant
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Cloud Platform-Oriented Electrical Vehicle Abnormal Battery Cell
This paper proposes a method for observing battery pack characteristics and variations from a macroscopic perspective, enabling rapid identification and analysis of pack abnormalities. The method involves calculating the area under the voltage curve of battery packs and extracting outlier cells and pack state changes using quartile normalization and Kullback-Leibler divergence.
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A fast data analysis method for abnormity detecting of lithium-ion
This paper proposes a method for observing battery pack characteristics and variations from a macroscopic perspective, enabling rapid identification and analysis of pack abnormalities. The method involves calculating the area under the voltage curve of battery packs and extracting outlier cells and pack state changes using quartile normalization and Kullback
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Review of Abnormality Detection and Fault Diagnosis Methods
In this paper, the state-of-the-art battery fault diagnosis methods are comprehensively reviewed. First, the degradation and fault mechanisms are analyzed and
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Anomaly Detection Method for Lithium-Ion
Aiming at the phenomenon of individual battery abnormalities during the actual operation of electric vehicles, this paper proposes a lithium-ion battery anomaly
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Realistic fault detection of li-ion battery via dynamical deep
Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies.
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CN117438329A
According to the invention, the specific solution is used for polishing the battery piece structure, so that the method for detecting and analyzing the end structure in the process of replication is recovered, whether the doping process and the raw material end are abnormal or not in the process of reverse analysis can be intuitively and rapidly carried out, abnormal piece output is
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A novel battery abnormality diagnosis method using multi-scale
Schmid et al. [38] proposed a data-driven fault diagnosis method based on voltage comparison of a single battery, which detects abnormal voltages through statistical evaluation based on principal component analysis, and the results showed that the method had excellent fault detection and isolation capability for a battery system consisting of 432 lithium
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A Novel Battery Abnormality Diagnosis Method Using Multi
A Novel Battery Abnormality Diagnosis Method Using Multi-Scale Normalized Coefficient of Variation in Real-world Vehicles. Jichao Hong, Fengwei Liang, Yingjie Chen, Facheng Wang, Xinyang Zhang, Kerui Li, Huaqin Zhang, Jingsong Yang, Chi Zhang, Haixu Yang, Shikun Ma, Qianqian Yang.
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Fault Diagnosis and Abnormality Detection of Lithium-ion Battery
abnormality detection method for battery packs of electric scooters based on statistical distribution of operation data that are stored in the cloud monitoring platform. According to the battery
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A method for battery fault diagnosis and
The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75. The results show that
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Data–Driven Fault Diagnosis and Cause Analysis of
Furthermore, we propose a framework for diagnosing problems with battery packs, which could be used to detect abnormal behavior. The proposed method calculates ICC values based on the terminal
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Detecting abnormality of battery decline for unbalanced samples
This work highlights the opportunities to diagnose lifetime abnormalities via "big data" analysis, without requiring additional experimental effort or battery sensors, thereby leading to
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Analysis of cell-level abnormality diagnosis based on battery
Download Citation | On Nov 28, 2023, Woochan Kam and others published Analysis of cell-level abnormality diagnosis based on battery pack voltage information | Find, read and cite all the research
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A novel battery abnormality diagnosis method using multi-scale
Accurate and efficient diagnosis of battery voltage abnormality is crucial for the safe operation of electric vehicles. This paper proposes an innovative battery voltage
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Data-Driven Thermal Anomaly Detection in
The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online
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A Review of Lithium-Ion Battery Fault
This paper provides a comprehensive review of various fault diagnostic algorithms, including model-based and non-model-based methods. The advantages and
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An exhaustive review of battery faults and diagnostic techniques
Various abusive behaviors and working conditions can lead to battery faults or thermal runaway, posing significant challenges to the safety, durability, and reliability of
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Anomaly Detection Method for Lithium-Ion
Abnormalities in individual lithium-ion batteries can cause the entire battery pack to fail, thereby the operation of electric vehicles is affected and safety accidents even occur in
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Integrated framework for battery cell state-of-health estimation in
Based on the analysis in Section 2.2, the internal current distribution within a battery module determines the charging current allocated to each battery. Therefore, in this study, an end-to-end SOH estimation model based on neural networks is constructed, taking the current as the input observation and the battery SOH as the output.
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Detecting Abnormality of Battery Lifetime
This work proposes a lifetime abnormality detection method for batteries based on few-shot learning and using only the first-cycle aging data. Verified with the largest
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Detecting abnormality of battery decline for unbalanced samples
Aiming at the challenges of one single algorithm''s limited performance on unbalanced samples and restricted analysis dimensions in battery risk detection, this paper
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Cloud Platform Oriented Electrical Vehicle Abnormal Battery Cell
Flow chart of battery consistency analysis model. A flow chart of the proposed abnormal cell detection method and the battery pack consistency evaluation is given in Fig. 1.
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Aging abnormality detection of lithium-ion batteries combining
As one of the most popular energy storage devices, lithium-ion batteries have dominated the consumer electronics market and electric vehicles on account of high energy density and long lifespan [[1], [2], [3]].The safety, durability, and reliable operation of battery systems attract more attention [4] pared with normal batteries, abnormal degradation
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ProADD: Proactive battery anomaly dual detection leveraging
Liu et al. predicted the remaining cycle life of battery using health indicator (HI) [11], and Tian et al. extracted samples from charging curves and applied transfer learning [12]. Zhang et al. detected battery abnormality and electrically interpret with semi-supervised learning using an interpretable autoencoder [13].
View more6 FAQs about [Battery abnormality analysis]
What is a fault diagnosis method based on battery parameter estimation?
The fault diagnosis method based on battery parameter estimation generally includes three steps: (1) identifying the relevant parameters, (2) analysis of the evolving characteristics, and (3) comparison with the parameter values of normal battery operation.
Can a battery detection method detect abnormal batteries?
Verified with the largest known dataset with 215 commercial lithium-ion batteries, the method can identify all abnormal batteries, with a false alarm rate of only 3.8%. It is also found that any capacity and resistance-based approach can easily fail to screen out a large proportion of the abnormal batteries, which should be given enough attention.
What is a diagnosis method based on battery parameter estimation?
diagnosis method based on battery parameter estimation generally includes three steps: (1) identifying the relevant parameters, (2) analysis of the evolving characteristics, and (3) comparison with the parameter values of normal battery operation.
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.
Is there a lifetime abnormality detection method for lithium-ion batteries?
This work proposes a lifetime abnormality detection method for batteries based on few-shot learning and using only the first-cycle aging data. Verified with the largest known dataset with 215 commercial lithium-ion batteries, the method can identify all abnormal batteries, with a false alarm rate of only 3.8%.
What are the state-of-the-art battery fault diagnosis methods?
In this paper, the state-of-the-art battery fault diagnosis methods are comprehen-sively reviewed. First, the degradation and fault mechanisms are analyzed and common abnormal behaviors are summarized. Then, the fault diagnosis methods are categorized into the statistical analysis-, model-, signal processing-, and data-driven methods.
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