HOME / Battery pack voltage detection method

Battery pack voltage detection method

A Multi-Fault Diagnosis Method for Battery Packs Based on Low

A low-redundancy battery pack diagnosis method is proposed to address the data redundancy issue in electric vehicle battery pack fault detection of ISC and VC. The fault diagnosis efficiency can be improved dramatically if the fault diagnosis process is executed only in abnormal cells. Via extracting a novel extreme voltage sequences, a low

Fault detection of lithium-ion battery packs with a graph-based

To tackle the issues described above, this work focuses on three LiB pack faults (i.e., sensor fault, connection fault and ESC fault), and proposes a graph-based method to

Internal short circuit detection for lithium-ion battery pack with

DOI: 10.1016/j.jclepro.2020.120277 Corpus ID: 213338368; Internal short circuit detection for lithium-ion battery pack with parallel-series hybrid connections @article{Yue2020InternalSC, title={Internal short circuit detection for lithium-ion battery pack with parallel-series hybrid connections}, author={Pan Yue and Xuning Feng and Zhang Mingxuan and Xuebing Han and

Fault detection of lithium-ion battery packs with a graph-based method

To tackle the issues described above, this work focuses on three LiB pack faults (i.e., sensor fault, connection fault and ESC fault), and proposes a graph-based method to locate the anomaly voltage sensors and detect the fault types of LiB packs. More specifically, the graph-based method takes advantages of deep autoencoder and the structure

Battery voltage transfer method for multi-cells Li-ion battery pack

In order to suppress leakage current caused in the traditional multi-cells series Li-ion battery pack protection system, a new battery voltage transfer method is presented in this paper, which

Voltage-fault diagnosis for battery pack in electric vehicles using

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.

A Sensor-Fault-Estimation Method for Lithium-Ion Batteries in

Descriptor proportional and derivate observer systems are applied for sensor diagnosis, based on electrical and thermal models of lithium-ion batteries, which can realize the real-time estimation of voltage sensor fault, current sensor fault, and temperature sensor fault.

Internal short circuit detection in Li-ion batteries using

CV time (T cv)Typically, battery charging is performed using the protocol of constant current (CC) or constant power (CP) charging followed by constant voltage (CV) charging 61,62.The time taken

Frontiers | A Fault Diagnosis Method for Lithium-Ion Battery Packs

The battery pack voltage of lithium iron phosphate battery packs ranges from 275 to 401.5 V. Considering the safety during the experiments, a 315–361.5 V battery pack voltage was adopted. 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

Voltage measurement-based recursive adaptive method for

Zhang et al. and Pan et al. proposed a method for diagnosing ISC faults in Li-ion battery packs based on symmetric loop topology, which performs fault detection and localization through the distribution of short-circuit currents in the symmetric loop topology (Pan et al., 2020, Zhang et al., 2018). However, the method only applies to parallel batteries and requires single

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 the cloud monitoring platform. According to the battery current and scooter speed, the operation states of electric scooters are clarified, and the

Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery

However, when voltages of individual cells in a lithium-ion battery pack are not provided, the effect of internal short circuit in the battery pack is not readily observed in whole terminal

A Sensor-Fault-Estimation Method for Lithium-Ion

Descriptor proportional and derivate observer systems are applied for sensor diagnosis, based on electrical and thermal models of lithium-ion batteries, which can realize the real-time estimation of voltage sensor fault,

Fault Diagnosis Method for Lithium-Ion Battery Packs

First, a robust locally weighted regression data smoothing method is proposed that can effectively remove noisy data and retain fault characteristics. Second, an ordinary-least-squares-based voltage potential

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

Frontiers | A Fault Diagnosis Method for Lithium-Ion

The battery pack voltage of lithium iron phosphate battery packs ranges from 275 to 401.5 V. Considering the safety during the experiments, a 315–361.5 V battery pack voltage was adopted. For the upper-limit voltage of the battery pack, the

Fault Diagnosis Method for Lithium-Ion Battery Packs in Real

Since there is an equalization mechanism within the battery pack, it slows down the voltage drop trend of the single faulty cell #Cell 31. For this phenomenon, the algorithm does not warn about this tiny internal short circuit anymore since it was already warned at the 320th sampling moment earlier. However, #Cell 31 shows a sudden voltage drop again at the

Fault diagnosis and abnormality detection of lithium-ion battery

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

Fault Diagnosis Method for Lithium-Ion Battery Packs in Real

First, a robust locally weighted regression data smoothing method is proposed that can effectively remove noisy data and retain fault characteristics. Second, an ordinary-least-squares-based voltage potential feature extraction method is proposed, which can effectively capture the small fault features of battery cells and achieve early warning.

Internal short circuit detection method for battery pack based

The battery pack based on the individual DP (dual polarization) battery model is established to verify the ISCr detection method. The 1–1000 Ω s ISCr (the early stage ISCr) can be effectively detected within 1–125 s. The SLCT provides the possibility of new battery pack designs and new battery management methods. The proposed ISCr

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

A Multi-Fault Diagnosis Method for Battery Packs Based on Low

A low-redundancy battery pack diagnosis method is proposed to address the data redundancy issue in electric vehicle battery pack fault detection of ISC and VC. The fault diagnosis

Anomaly Detection Method for Lithium-Ion Battery Cells Based

By analyzing the data of three actual electric vehicles in operation, it is shown that the method proposed in this paper can effectively and accurately detect an abnormal battery cell in a lithium-ion battery pack. Compared with other methods, the proposed method has more advantages, and the results show that this method exhibits strong

Multi-fault detection and diagnosis method for battery packs

In this paper, a statistical analysis-based multi-fault diagnosis method is proposed to detect and localize short circuit faults, electrical connection faults and voltage

Battery internal short-circuit detection apparatus and method,

Patent Document 3 describes that when the temperature of the battery rises due to an internal short circuit, the rise of the temperature is stored, thereby enabling a detection of an internal short circuit or the like that occurs when the battery is not operated. Patent Document 3 further describes that when a significant temperature increase with respect to a significant voltage

Multi-fault detection and diagnosis method for battery packs

In this paper, a statistical analysis-based multi-fault diagnosis method is proposed to detect and localize short circuit faults, electrical connection faults and voltage sensor faults in LFP battery packs. This method uses non-redundant interleaved voltage measurement topology to detect battery voltages, where every voltage sensor measures the

Anomaly Detection Method for Lithium-Ion Battery

By analyzing the data of three actual electric vehicles in operation, it is shown that the method proposed in this paper can effectively and accurately detect an abnormal battery cell in a lithium-ion battery pack.

A correlation based fault detection method for short circuits in

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

A real-time insulation detection method for battery packs

Moreover, the battery pack is always in the states of charging and discharging during driving, which will lead to frequent changes in the voltage of the battery pack and affect the estimation accuracy of insulation detector. Therefore the recursive least squares algorithm is adopted to solve the problem that the detection results of insulation detector mutate with the

Battery pack voltage detection method
Battery pack voltage detection method [PDF]

6 FAQs about [Battery pack voltage detection method]

How to detect abnormal cell voltage in a battery pack?

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.

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.

How to detect a faulty battery pack?

The systematic faults of battery pack and possible abnormal state can be diagnosed by one coefficient. 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.

What is battery voltage fault diagnosis method?

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.

Can a recursive least square algorithm be used to diagnose a battery pack?

Tian et al. (2020) developed a sensor fault diagnosis algorithm using the equivalent models and particle filters. Then this diagnosis was employed to test the battery pack using the recursive least square algorithm. The results show that the algorithm proposed in this study can be used to identify the diagnosis of the battery pack.

Which method is suitable for detecting faults in lithium-ion batteries?

The proposed method is more suitable for handling constant or slow-varying faults. Three sliding mode observers and three filters are designed, to realize fault diagnosis, isolation, and estimation in the lithium-ion battery voltage, current, and temperature sensors.

Related links

Unlock Sustainable Power with High-Performance Solar Storage

We provide innovative photovoltaic storage systems, including advanced battery cabinets and containerized energy solutions, ensuring stable and eco-friendly power for homes, businesses, and industries.