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New energy battery voltage collection abnormality

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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Prediction and Diagnosis of Electric Vehicle Battery Fault Based on

Numerous studies highlight that voltage abnormalities can precipitate various battery faults, broadly categorized into four types: overvoltage, undervoltage, rapid voltage fluctuations, and inadequate battery voltage uniformity.

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Optimized GRU‐Based Voltage Fault Prediction Method for

Due to the insignificant anomalies and the nonlinear time-varying properties of the cell, current methods for identifying the diverse faults in battery packs suffer from low

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Battery safety issue detection in real-world electric vehicles by

Battery safety has become a major concern that hinders the mass adoption of electric vehicles. This paper presents a battery safety issue detection method based on voltage abnormality and integrated battery modeling. Firstly, a battery voltage abnormality degree is defined. Then an integrated battery model is proposed by combining an

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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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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 abnormality diagnosis method based on a normalized coefficient of variation in real-world electric vehicles.

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A novel battery abnormality diagnosis method using multi-scale

Clean energy development has become a key concern due to increasing environmental pollution and the energy crisis. New energy vehicles (NEVs), particularly electric vehicles (EVs), have rapidly developed due to their clean, efficient, and low-pollution characteristics [[1], [2], [3]].Lithium-ion batteries have a wide application in EVs due to their

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A method for battery fault diagnosis and early warning combining

1 INTRODUCTION. Lithium-ion batteries are widely used as power sources for new energy vehicles due to their high energy density, high power density, and long service life. 1, 2 However, it usually requires hundreds of battery cells in series and parallel to meet the requirements of pure electric vehicles for mileage and voltage. 3 The differences caused by

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Detecting Abnormality of Battery Lifetime from

We generate the largest known dataset for lifetime-abnormality detection, which contains 215 commercial lithium-ion batteries with an abnormal rate of 3.25%. Our method can accurately identify all abnormal batteries in the

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一种利用实际车辆多尺度归一化变异系数的新型电池异常诊断方法,Energy

本文提出了一种基于现实电动汽车归一化变异系数的创新电池电压异常诊断方法。收集和分析车辆和实验室数据,并进行联合预处理以提高数据质量,并对电池电压进行对数转换以改善异常电压波动的影响。提出归一化变异系数来检测单体电压的波动不一致性,并通过Z分数和归一化制定风险系数规则。此外,其有效性和鲁棒性通过实验室和现实世界的电池故障得

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Anomaly Detection Method for Lithium-Ion Battery Cells Based

The measurable parameters of new energy vehicle batteries mainly include voltage, current, and temperature, which are commonly used feature data 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

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Optimized GRU‐Based Voltage Fault Prediction Method for

Due to the insignificant anomalies and the nonlinear time-varying properties of the cell, current methods for identifying the diverse faults in battery packs suffer from low accuracy and an inability to precisely determine the type of fault, a method has been proposed that utilizes the Random Forest algorithm (RF) to select key factors influencing voltage, optimizes model

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Battery voltage fault diagnosis for electric vehicles considering

Battery voltage fault diagnosis methods can be generally classified into threshold-based, fault diagnosis method based on the actual operation data collected from National Monitoring and Management Center for New Energy Vehicles (NMMC-NEV). This method can calculate and detect the abnormal changes of cell terminal voltages in the form of

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Battery voltage fault diagnosis for electric vehicles considering

Zhao et al. proposed a big-data-statistics-based fault diagnosis method based on the actual operation data collected from National Monitoring and Management Center for New Energy Vehicles (NMMC-NEV). This method can calculate and detect the abnormal changes of cell terminal voltages in the form of probability according to machine learning

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Voltage abnormality-based fault diagnosis for batteries in electric

Wang et al. [18] presented an in-situ voltage fault diagnostic technique based on modified Shannon entropy that can forecast voltage problems in real time by monitoring

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Battery voltage fault diagnosis for electric vehicles considering

Management Center for New Energy Vehicles (NMMC-NEV). This method can calculate and detect the abnormal changes of cell terminal voltages in the form of probability according to machine learning algorithm and 3σmulti-level screening strat-egy. Li et al. [32] compared the calculated inter-class correlation coefﬁcient (ICC) of terminal voltage of the adjacent cells in

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Prediction and Diagnosis of Electric Vehicle Battery

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Voltage fault diagnosis of a power battery based on wavelet time

Abnormal voltage, such as a sudden increase or decrease in voltage, may mean more early faults, including short circuits and open circuits [7]. Therefore, the detection of

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Autoencoder-Enhanced Regularized Prototypical Network for New Energy

Multiple sensors are implemented to monitor the new energy battery, taking measurements of the battery pack''s voltage, current, and temperature, and estimating its State of Charge (SOC) and State of Health (SOH). The data collection was conducted over a seven-month period from ten tested vehicles, with a set sampling cycle that resulted in an accumulated data

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Battery safety issue detection in real-world electric vehicles by

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

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Voltage abnormity prediction method of lithium-ion energy

Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage...

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一种利用实际车辆多尺度归一化变异系数的新型电池异常诊断方

本文提出了一种基于现实电动汽车归一化变异系数的创新电池电压异常诊断方法。收集和分析车辆和实验室数据,并进行联合预处理以提高数据质量,并对电池电压进行对

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Voltage abnormality-based fault diagnosis for batteries in

Wang et al. [18] presented an in-situ voltage fault diagnostic technique based on modified Shannon entropy that can forecast voltage problems in real time by monitoring battery voltage. Furthermore, with the advancement of artificial intelligence ( AI ) technology, data-driven fault detection techniques using AI algorithms have been

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 1

IEEE Proof 2 IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 72 knowledge- and model-based methods relies on deterministic 73 fault types and known mechanisms. This may lead to limited 74

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Anomaly Detection Method for Lithium-Ion Battery Cells Based on

The measurable parameters of new energy vehicle batteries mainly include voltage, current, and temperature, which are commonly used feature data in battery anomaly

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Detecting Abnormality of Battery Lifetime from First‐Cycle Data

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Battery voltage fault diagnosis for electric vehicles

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Voltage fault diagnosis of a power battery based on wavelet time

Abnormal voltage, such as a sudden increase or decrease in voltage, may mean more early faults, including short circuits and open circuits [7]. Therefore, the detection of abnormal changes in battery voltage can be used to detect faults in advance.

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New energy battery voltage collection abnormality [PDF]

6 FAQs about [New energy battery voltage collection abnormality]

Why is voltage abnormality a problem in battery management system?

Furthermore, voltage abnormalities imply the potential occurrence of more severe faults. Due to the inconsistency in the voltage of the battery pack, when the battery management system fails to effectively monitor the individual voltages of power battery cells, the cell with the lowest voltage will experience over-discharge first.

How can we diagnose anomalies in battery voltage?

The accuracy and timeliness of the predictions are validated through a comprehensive evaluation and comparison of the forecasted voltages. To diagnose anomalies in battery voltage, the paper proposes a fault diagnosis method that combines the Isolation Forest and Boxplot techniques.

Can we predict abnormal power battery voltages early?

The voltages of these cells show an expanding trend of anomalies, and the MRE between all predicted and actual voltages is 0.155%. This indicates that the proposed method can achieve early prediction of abnormal power battery voltages. Figure 9. Prediction results of all battery cell voltages of the faulty vehicle before the fault occurred. 5.2.

Can abnormal battery voltage be used to detect faults in advance?

Therefore, the detection of abnormal changes in battery voltage can be used to detect faults in advance. However, the battery voltage presents nonlinear and time-varying characteristics, so the analysis of the abnormally sharp challenges hidden under the voltage can be challenging.

Can a faulty battery system be detected and diagnosed accurately?

The above analysis proves that even the slight voltage abnormities of battery system during vehicular operation can be detected and diagnosed accurately by the method proposed in this work. Moreover, this method can achieve voltage fault diagnosis in advance when the voltage of the faulty cell still within the normal range.

How can power battery anomalies be predicted accurately?

To achieve timely and accurate prediction of power battery anomalies, two factors need to be considered. On the one hand, to maximize the accuracy of voltage prediction, provide more precise data for voltage anomaly diagnosis, thereby enhancing the accuracy of safety warnings.

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