CN117890808A
The application relates to a battery pack abnormality detection method, a battery pack abnormality detection device, a computer device and a storage medium. The method comprises the following steps: performing simulated charge and discharge on the offline battery pack to obtain a plurality of single-cell voltage data; judging whether the voltage difference of the offline battery pack is
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
JP2003282155A
PROBLEM TO BE SOLVED: To accurately detect an abnormality in cells constituting a battery pack. SOLUTION: An abnormality detection device detects an abnormality of the battery pack 300 constituted by connecting a plurality of cells in series. This abnormality detection device 100 includes a capacity equalization circuit for equalizing capacity of a plurality of cells and an
Battery pack abnormality detection device
To provide a battery pack abnormality detection device that can detect that gas is discharged from a single cell (battery cell) in a battery pack even when the system is off.SOLUTION: An abnormality detection device (2) of a battery pack (60) having a single cell (60s) having an exhaust valve for discharging internal gas when the internal pressure exceeds set pressure,
Fault Diagnosis and Abnormality Detection of Lithium-ion Battery Packs
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
Review of Abnormality Detection and Fault Diagnosis Methods
An accurate battery analytical model can be used to obtain battery parameters that indicate changes in a single cell. In a battery pack, the difference between a faulty cell and other normal cells reveals a system failure. Xue, Q., Li, G., Zhang, Y., Shen, S., Chen, Z., Liu, Y.: Fault diagnosis and abnormality detection of lithium-ion
A method for battery fault diagnosis and
In this paper, an abnormal cell identification and early warning method based on SW and IF algorithm is proposed for the cell voltage data of battery pack. The IF
KR20090023547A
Provided is a method for judging abnormalities of a battery pack, which is provided with a secondary battery composed of at least one cell, and a voltage detecting circuit for measuring the cell voltage of the secondary battery. The method is provided with a voltage measuring step of measuring the cell voltage; and a judging step of judging whether abnormality judging
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
Multiscale Fusion for Abnormality Detection and Localization of
Circuit diagram of a battery pack. The abnormality of a single unit can potentially trigger. uncontrollable failures in the DPS. For example, the thermal. abnormality in a battery cell may
A Fault Diagnosis Method for Lithium-Ion Battery Based on
However, most of the existing methods are based on abnormality of single cells while ignoring feature recognition on system level. In practice, quite a few thermal runaways are reported without obvious cell level abnormality, making existing methods inapplicable. Take a battery pack with 8 single cells as an example. When in good shape
A Method for Abnormality Detection of Lithium-Ion Battery Packs
During the operation of lithium-ion battery packs, there often exhibit certain abnormalities due to cell faults such as internal short circuit or unavoidable inconsistencies
Abnormal Battery Location Recognition and State
There are many problems in the abnormal diagnosis of the lithium battery pack, such as incomplete research structure, insufficient positioning accuracy of abnormal batteries, and inadequate combination of diagnosis and treatment. To deal with these problems, this paper systematically achieves the goal of precise positioning, state estimation, and decision-making
Fault diagnosis for cell voltage inconsistency of a battery pack in
A fault diagnosis method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed for timely localization of the
Fault Diagnosis and Abnormality Detection of Lithium
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...
WO2018230115A1
A battery pack comprises one or more of a rechargeable battery (1), an output switch (2) that is serially connected to the output side of the battery (1), a control circuit (3) that detects the voltage of the battery (1) and controls the output switch (2) so as to turn on and off, a fuse (5) that is serially connected to the battery (1) and is fused when there is an abnormality in the battery
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 proposes an early abnormal decline battery diagnosis method based on feature engineering and ensemble learning optimized convolutional neural network (CNN) applicable to unbalanced
Detecting abnormality of battery decline for unbalanced samples
A single CNN-based abnormal decline battery diagnosis model is adopted to further validate the necessity of ensemble learning in dealing with unbalanced samples. The single model only reaches 86.4 % accuracy and 77 % Recall in the same dataset, which is lower than the 100 % accuracy of the ensemble CNN model.
Fault diagnosis for cell voltage inconsistency of a battery pack in
As the input and output of the converter can be either a single cell or the entire battery pack, four main active topologies are identified: cell to cell, cell to pack, pack to cell and cell to
Fault and defect diagnosis of battery for electric vehicles based on
DOI: 10.1016/J.APENERGY.2017.05.139 Corpus ID: 114124378; Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods @article{Zhao2017FaultAD, title={Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods}, author={Yang Zhao and Peng Liu and Zhenpo Wang and Lei Zhang and Jichao
A Method for Abnormality Detection of Lithium-Ion Battery Packs
A Method for Abnormality Detection of Lithium-Ion Battery Packs Based on Kullback-Leibler Test and Greenwald-Khanna Clustering Chong Wang1,2, Yajie Liu1,2(B), Yuanming Song1,2, and Yu Wang1,2 1 College of System Engineering, National University of Defense Technology, Changsha 410073, People''s Republic of China
Fault Diagnosis and Abnormality Detection of Lithium-ion Battery Packs
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. the systematic faults of battery pack and possible abnormal state can be timely diagnosed. 2) The t-SNE technique, The K-means
Fault diagnosis and abnormality detection of lithium-ion battery packs
Lithium-ion battery packs are widely deployed as power sources in transportation electrification solutions. To ensure safe and reliable operation of battery packs, it is of critical importance to monitor operation status and
Short circuit detection in lithium-ion battery packs
This study uses experimental current and voltage data from a Wabtec BEL battery module consisting of 66 Li-ion NMC cells in a 3P-22S arrangement. The 3P cells are considered as a single equivalent cell with the same voltage, three times the capacity, and each cell receiving approximately 1 / 3 the current. The voltage and surface temperature
Voltage-fault diagnosis for battery pack in electric vehicles using
The safety of battery system is compromised by the abusive operation and aging, potentially resulting in the abnormal voltage levels. Rapid detection and accurate diagnosis of voltage fault are crucial for ensuring the safety of battery packs. In the case of a battery pack operating in the same environment, there is a strong correlation
Frontiers | A Fault Diagnosis Method for Lithium-Ion
This paper presents a fault diagnosis method for the electric vehicle power battery using the improved radial basis function (RBF) neural network. First, the fault information of lithium-ion battery packs was collected
A method for battery fault diagnosis and
The voltage of a single battery is reflected on two voltmeters, and the voltage sensor fault, connection fault and short-circuit fault of the battery pack are analyzed by
Fault Diagnosis and Abnormality Detection of Lithium-ion Battery
By analyzing the abnormalities hidden beneath the external measurement and calculating 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. Experimental results validate that the proposed method can
Data–Driven Fault Diagnosis and Cause
To monitor battery abnormalities, we designed a new framework for diagnosing problems with battery packs. In this manner, we focused on diagnosing abnormalities and
JP2003282156A
PROBLEM TO BE SOLVED: To easily and accurately detect an abnormality of cells constituting a battery pack. SOLUTION: An abnormality detection device detects an abnormality of the battery pack 300 constituted by connecting a plurality of cells in series. This abnormality detection device 100 includes an equalization circuit for equalizing capacity of a plurality of cells and a CPU 112
Fault Diagnosis and Abnormality Detection of Lithium-ion Battery Packs
Key Words: Electric scooters, battery pack, fault diagnosis, abnormality detection, Gaussian distribution. I. on the voltage data of a single cell in battery packs, and they cannot accurately
Battery pack abnormality determination device
PROBLEM TO BE SOLVED: To determine abnormality in a battery pack separately for abnormality in a monitoring unit and abnormality in a battery cell.SOLUTION: A battery pack abnormality determination device comprises a plurality of battery modules 20 and a plurality of monitoring units 8 provided for each of the battery modules 20, characterized in being
Fault diagnosis for cell voltage inconsistency of a battery pack in
Highlights • 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
6 FAQs about [Single battery pack abnormality]
What is the fault diagnosis voltage for a battery pack?
For the upper-limit voltage of the battery pack, the fault diagnosis voltage was 410 V when the actual voltage of the battery pack recorded by the sensor was 450 V. The fault level for this condition is denoted No. I.
What happens if each cell inside a battery pack has a different SOC?
If each cell inside the battery pack has a different SOC, the high SOC cell has a higher voltage, and the low SOC cell has a lower voltage. Figure 10. Simulink data with a cell balancing problem ( ( a) voltage and ( b) ICC value). Figure 11 data is filtered, and the order of the voltage is determined.
What causes inconsistency of battery cells?
The inconsistency of the battery cells will influence the performance of the whole battery pack and lead to fault occurrence. Following are some key causes of the inconsistency of the battery: Because of the inconsistent capacity and State of Charge (SoC), the actual available energy of the battery pack is lower than any single cell.
What is inconsistency fault in battery management system?
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.
Are all abnormal batteries accurately predicted to be “abnormal”?
The scores of all batteries are lower than a predefined threshold, i.e., 50% in this work, implying that all abnormal batteries are accurately predicted to be “abnormal”. In our test, the first abnormal battery has the highest score (44.6%), and its aging trajectory is given in Figure 4c.
What happens if a single cell battery fails?
Such fault can result in abnormal responses from the battery such as over/under voltage. In practical application, single-cell is unable to satisfy the voltage, current and energy requirements for EV.