However, the proposed methods in these works [, , , ] are mainly based on the voltage data of a single cell in battery packs, and they cannot accurately diagnose faults and anomalies incurred by variation of other parameters, such as current, temperature and even power demand.
By applying the designed coefficient, the systematic faults of battery pack and possible abnormal state can be timely diagnosed. 2) The t-SNE technique, The K-means clustering and Z-score methods are exploited to detect and accurately locate the abnormal cell voltage.
By analyzing the abnormalities hidden beneath the external measurement and calcg. the fault frequency of each cell in pack, the proposed algorithm can identify the faulty type and locate the faulty cell in a timely manner. Exptl. results validate that the proposed method can accurately diagnose faults and monitor the status of battery packs.
Firstly, the faulty or abnormal battery cells’ voltage is roughly identified and classified using the K-means clustering algorithm . Secondly, the abnormal cell voltage is located based on the designed coefficient that is calculated according to the Z-score theory .
From the detection results and the voltage variation trajectories of cells, it can be concluded that the detected abnormality is a rapid descent of voltage caused by the battery pack that is discharged with a high rate current in a low voltage stage.
Among these faults, the inconsistency fault belongs to the frequent fault in the battery management system. Next, we will review the causes and research methods of inconsistency fault. Such fault can result in abnormal responses from the battery such as over/under voltage.
Fault diagnosis and abnormality detection of lithium-ion battery packs …
In short, the conventional fault diagnosis methods for lithium-ion battery packs, to the authors'' knowledge, are inefficient for detecting the faults and abnormalities and locating faulty cells of battery packs. To address this issue, a systemic faults diagnosis method and a voltage abnormality detection approach are mainly investigated and developed for the battery …
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Anomaly Detection Method for Lithium-Ion Battery …
The data analysis and experimental verification results based on actual vehicle operating conditions indicate that this method can accurately identify an abnormal cell within the battery pack and diagnose the specific …
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A method for battery fault diagnosis and early warning …
Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the...
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Individual Cell-Level Temperature Monitoring of a …
The temperature response of FBGs positioned between battery cells demonstrates that, in addition to sensing temperature at the cell level, temperature data can be effectively acquired between cells, suggesting that …
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Fault diagnosis for cell voltage inconsistency of a battery pack in ...
Cell voltage inconsistency of a battery pack is important for the safety of electric vehicle. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is able to localize cell fault. Least-Square Support Vector Regression …
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Fault diagnosis for cell voltage inconsistency of a battery pack in ...
Cell voltage inconsistency of a battery pack is the main problem of the Electric …
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A method for battery fault diagnosis and early warning combining ...
When the score of a cell exceeds the threshold at each time point, the cell is diagnosed as an abnormal cell. If the score of a single battery increases with time and exceeds the threshold value, the time exceeding the threshold can be compared with the actual vehicle alarm time to achieve the warning effect.
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Fault Diagnosis Method for Lithium-Ion Battery Packs in Real …
In this paper, an initial microfault diagnosis method is proposed for the data of electric vehicles in actual operation. First, a robust locally weighted regression data smoothing method is proposed that can effectively remove noisy data and retain fault characteristics.
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Battery voltage fault diagnosis for electric vehicles considering ...
1 INTRODUCTION. Lithium-ion batteries (LIBS) are widely used in electric vehicles (EVs) as the energy storage devices due to their superior properties like high energy density, long cycle life and low self-discharge [] ually, multiple LIBS cells are connected in series and/or parallel configurations to meet the requirements of high energy and high power …
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Multimonth-ahead data-driven remaining useful life ...
Figure 1 shows the workflow of the newly developed on-line RUL cell voltage inconsistency prognostic approach in this study. As shown in Fig. 1, the approach for predictive maintenance of battery modules, consisting of LiBs, for an FR-BESS is composed of the two cascade processes, i.e., an HI construction process to construct a compact HI from (Delta …
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Fault Diagnosis and Abnormality Detection of Lithium-ion Battery Packs …
coefficient (ICC) method is investigated to calculate the correlation values of voltages of adjacent battery cells for fault diagnosis and fault location. However, the proposed methods in these works [29-32] are mainly based on the voltage data of a single cell in battery packs, and they cannot accurately diagnose faults and anomalies
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Fault diagnosis for cell voltage inconsistency of a battery pack in ...
Cell voltage inconsistency of a battery pack is the main problem of the Electric Vehicle (EV) battery system, which will affect the performance of the battery and the safe...
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Cell Capacity and Pack Size
Capacity of a single cell (Ah) Nominal voltage of a single cell (V nom) Usable SoC window (%) Energy (kWh) = S x P x Ah x V nom x SoC usable / 1000. Note: this is an approximation as the nominal voltage is dependent on the usable window. Also, the variation in cell capacity will be needed to be understood to establish accurate pack capacity ...
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Fault Diagnosis Method for Lithium-Ion Battery Packs in Real …
Battery failure has traditionally been a major concern for electric vehicle (EV) safety, and early fault diagnosis will reduce many EV safety accidents. However, the short-circuit signal is generally very weak, so it is still a challenge to achieve a timely warning of battery failure. In this paper, an initial microfault diagnosis method is proposed for the data of electric vehicles …
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Fault Diagnosis Method for Lithium-Ion Battery Packs in Real …
The conventional fault-diagnosis methods are difficult to detect the battery faults in the early stages without obvious battery abnormality because lithium-ion batteries are complex nonlinear time-varying systems with abs. cell inconsistency. Therefore, this paper proposes a real-time multi-fault diagnosis method for the early battery failure ...
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Fault Diagnosis and Abnormality Detection of Lithium-ion Battery Packs …
This study investigates a novel fault diagnosis and abnormality detection method for battery packs of electric scooters based on statistical distribution of operation data that are stored in...
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A method for battery fault diagnosis and early warning combining ...
Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the...
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Detection and isolation of faults in a lithium-ion battery pack …
A single faulty cell of a PCM, a faulty sensor, or a faulty connection can …
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Anomaly Detection Method for Lithium-Ion Battery Cells Based …
The data analysis and experimental verification results based on actual vehicle operating conditions indicate that this method can accurately identify an abnormal cell within the battery pack and diagnose the specific moment of abnormality in the battery cell at an early stage of failure, with good robustness.
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Fault Diagnosis and Abnormality Detection of Lithium-ion Battery Packs …
Fault Diagnosis and Abnormality Detection of Lithium-ion Battery Packs Based on Statistical Distribution Qiao Xue1, Guang Li2, Yuanjian Zhang3, Shiquan Shen1, Zheng Chen1, 2*, and Yonggang Liu4 ...
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Data-Driven Thermal Anomaly Detection in Large Battery Packs
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 anomaly detection in battery packs that uses real-time voltage and temperature data from multiple Li-ion battery cells. Mean-based residuals are generated for cell groups and evaluated using …
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Fault diagnosis and abnormality detection of lithium-ion battery packs …
In short, the conventional fault diagnosis methods for lithium-ion battery packs, to the authors'' knowledge, are inefficient for detecting the faults and abnormalities and locating faulty cells of battery packs. To address this issue, a systemic faults diagnosis method and a voltage abnormality detection approach are mainly investigated and ...
Learn More
Detection and isolation of faults in a lithium-ion battery pack …
A single faulty cell of a PCM, a faulty sensor, or a faulty connection can substantially reduce the whole battery pack''s performance or cause a hazard. So, it is crucial to identify the faulty PCM from the pack and infer the type of fault that has occurred for quick and cost-effective maintenance or fault-tolerant and safe operation. This ...
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Fault diagnosis and abnormality detection of lithium-ion battery …
In short, the conventional fault diagnosis methods for lithium-ion battery …
Learn More
Fault Diagnosis Method for Lithium-Ion Battery Packs …
The conventional fault-diagnosis methods are difficult to detect the battery faults in the early stages without obvious battery abnormality because lithium-ion batteries are complex nonlinear time-varying systems with abs. cell …
Learn More
