To ensure the real-time operation safety of electric vehicles (EVs), it is essential to diagnose the fault in a battery pack timely and accurately. In this paper, with considering driving condition, a battery voltage fault
Understanding BMS Battery Pack Current Measurement Requirements. A battery pack, as shown in Figure 2, typically has two operating modes: charging mode and discharging mode. Figure 2: Operating modes in a BMS . In charging mode, a charging circuit charges the battery pack; current flows into its HV+ terminal.
One of the most useful measurements for a battery cell or pack is the open circuit voltage (OCV), but the considerations that must be made at the module or pack level differ from the cell level. This application note describes several ways of measuring open circuit voltage on a battery pack including at the full pack level, on individual cells
To ensure the real-time operation safety of electric vehicles (EVs), it is essential to diagnose the fault in a battery pack timely and accurately. In this paper, with considering driving condition, a battery voltage fault diagnosis method is proposed based on the real-world operation data of EVs with a high sampling frequency.
many series-connected cells to achieve a certain pack voltage. In order to provide a required pack capacity, for each layer in the series-connection, either several cells are . arallel-connected or
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
PDF | On Jan 1, 2017, MEHMET UĞRAŞ CUMA and others published DESIGN CONSIDERATIONS OF HIGH VOLTAGE BATTERY PACKS FOR ELECTRIC BUSES | Find, read and cite all the research you need on ResearchGate
Reliable online internal short circuit diagnosis on lithium-ion battery packs via voltage anomaly detection based on the mean-difference model and the adaptive prediction
many series-connected cells to achieve a certain pack voltage. In order to provide a required pack capacity, for each layer in the series-connection, either several cells are . arallel-connected or of sufficiently high individual capacity. Due to its favorable energy and power density together with its resilience to memory ef.
In this paper, the battery inconsistency equalisation strategy is investigated and a novel fusion model based on equivalent circuit models is proposed. The three equivalent circuit models, 1RC, 2RC and PNGV, are weighted and fused by BP neuron network, which realizes the complementary advantages of the three equivalent circuit models. Even though the estimated
In practical application, single-cell is unable to satisfy the voltage, current and energy requirements for EV. Hundreds or thousands of individual cells need to be connected in series/parallel configuration to construct battery packs in order to provide sufficient voltage, current, power and energy for EV [7, 8].Unfortunately, cell differences always exist and are
And battery packs are often accompanied by inconsistencies, which further increases the difficulty of effective identification of faulty cells. This article proposes a battery fault diagnosis method combining variational mode decomposition (VMD) and edit distance. Combined with a thermal runaway case in actual profiles, VMD is adopted to decompose discharging
Automatic identification of battery type and characteristic is useful for development versatile battery management systems. This study presents a battery identification...
Battery packs are a core component of many emerging technologies such as Electric Vehicles (EVs) or smart grid energy storage solutions for renewable sources. Such battery packs consist of many series-connected cells to achieve a certain pack voltage. In order to provide a required pack capacity, for each layer in the series-connection, either several cells are parallel-connected or
Statistical analysis-based methods diagnose battery faults by identifying abnormal characteristics in observation data and comparing these with predefined thresholds. These approaches include techniques such as Shannon entropy, principal component analysis (PCA), and independent principal component analysis (ICA). Liu et al. (2024) proposed a multi-fault diagnosis method
A common size for cells inside cordless tool battery packs. This size is also used in radio-controlled scale vehicle battery packs and some Soviet multimeters. 1 ⁄ 2-, 4 ⁄ 5 - and 5 ⁄ 4-sub-C sizes (differing in length) are also available. Soviet 332 type can be replaced with R10 (#4, 927, BF, U8) or 1.5 V elements from 3 V 2xLR10 packs
For each condition, the cells voltage, temperature, pack current, the State of Charge (SOC), the battery management system (BMS) state and the balancing command are obtained. View full-text Method
To emulate an ISC faulty battery pack, we assembled a series-connected pack of eight cells, as shown in Fig. 5 b, with the specific parameters of the utilized cells outlined in Table 1. Taking real-world battery usage into account, we introduced cell inconsistencies within the pack, such as disparities in capacity and initial SOC, by setting the states of the eight cells
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.
Based on the onboard data from the cloud battery management system (BMS), this work proposes an ISC diagnosis algorithm for battery packs with high accuracy and high robustness via voltage anomaly detection. The mean-difference model (MDM) is applied to characterize large battery packs.
Statistical analysis-based methods diagnose battery faults by identifying abnormal characteristics in observation data and comparing these with predefined thresholds. These approaches
Reliable online internal short circuit diagnosis on lithium-ion battery packs via voltage anomaly detection based on the mean-difference model and the adaptive prediction algorithm
Based on the onboard data from the cloud battery management system (BMS), this work proposes an ISC diagnosis algorithm for battery packs with high accuracy and high robustness via voltage anomaly detection. The
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
Rapid detection and accurate diagnosis of voltage fault are crucial for ensuring the safety of battery packs. A battery voltage fault diagnosis method is proposed by using the mutual information in this work, which can identify faulty cells timely.
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
Received January 18, 2022, accepted January 27, 2022, date of publication January 31, 2022, date of current version February 10, 2022. Digital Object Identifier 10.1109/ACCESS.2022.3147802
Automatic identification of battery type and characteristic is useful for development versatile battery management systems. This study presents a battery
Wu et al. proposed a battery pack fault diagnosis method based on the combination of Hausdorff distance and modified Z-score. The faulty cell is detected by comparing the Hausdorff distance between the voltage curve of each battery and the median voltage curve in the moving window.
A method for diagnosing battery voltage faults using the mutual information is proposed in this work. Specifically, the faulty cells are diagnosed by calculating the mutual information between voltages of each paired-cells. The occurrence of abnormal cells can lead to a decrease in mutual information related to the cells.
As can be seen in Fig. 2, the connection fault of the battery pack has the following two characteristics: 1. When the fault occurs, the voltage of the faulty single unit is characterized by a gradual deviation from that of the healthy single team.
To this end, the study proposes an intelligent diagnosis method for battery pack connection faults based on multiple correlation analysis and adaptive fusion decision-making.
This testing can be a bottleneck in the manufacturing process, so test solutions that reduce time or increase test density are highly desirable. One of the most useful measurements for a battery cell or pack is the open circuit voltage (OCV), but the considerations that must be made at the module or pack level differ from the cell level.
A battery voltage fault diagnosis method is proposed by using the mutual information in this work, which can identify faulty cells timely. Specifically, the voltage of battery pack in an electric vehicle is collected, and the mutual information of voltages between each paired-cells is calculated.
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