Internal short circuit detection for battery pack using equivalent parameter and consistency method. Journal of Power Sources 294, 272–283 (2015). Article ADS CAS Google Scholar
The inconsistencies in battery packs were detected at high state of charge (SOC) levels at the end of charging. This method can evaluate the consistency of battery packs online based on EV operation data to monitor battery safety and provide detailed information for
In order to verify the feasibility and performance of the detection and diagnosis method, several types of fault detection and diagnosis experiments are set up, which use a temperature chamber, charge equipment and several 50Ah LFP batteries, as shown in Fig. 3. The frequency of measurement is 10 Hz, and the voltage accuracy is 0.1 %. In the experiments,
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
First, the difference sample entropy (DSE) rapidly detects suspicious battery faults to ensure high FDR. Then, the correlation coefficient method precisely diagnoses suspicious faults to significantly improve DAR. Finally, the deep neural network is used to quantify the defined state of fault (SOF) for the first time. The SOF can indicate the
Liu et al. [160] applied the structural analysis theory for a battery pack to detect and isolate the various sensor faults and cooling system faults. A comparison is performed between the
This article will introduce several common lithium battery pack quality inspection methods, including visual inspection, electrical performance test, safety assessment, etc., to
To effective and accurate identification of failures for the battery, Schmid et al. (2021) developed a fault diagnosis method by using the fuzzy clustering algorithm. In this algorithm, the switches of reconfigurable battery system
To address these challenges, we develop a periodic segmentation Transformer-based ISC detection method for battery packs. Firstly, considering three different operating conditions, a comprehensive
The inconsistencies in battery packs were detected at high state of charge (SOC) levels at the end of charging. This method can evaluate the consistency of battery packs online based on EV operation data to monitor battery safety
To effective and accurate identification of failures for the battery, Schmid et al. (2021) developed a fault diagnosis method by using the fuzzy clustering algorithm. In this algorithm, the switches of reconfigurable battery system were used to isolate the fault of the electric vehicles.
To solve this problem, a non-destructive testing method for capacity consistency of lithium-ion battery pack based on 1-D magnetic field scanning is proposed in this article.
This article will introduce several common lithium battery pack quality inspection methods, including visual inspection, electrical performance test, safety assessment, etc., to help understand how to effectively evaluate the quality of lithium battery pack.
To solve this problem, a non-destructive testing method for capacity consistency of lithium-ion battery pack based on 1-D magnetic field scanning is proposed in this article. First, a magnetic field simulation model and measurement setup of lithium-ion battery are developed to study the principle of detection technology. On such basis, a
In this study, the relationship between equalization electric quantity (EEQ) and residual charge capacity (RCC) for lithium-ion battery pack is investigated. Then, an RCC-based internal short circuit...
First, the difference sample entropy (DSE) rapidly detects suspicious battery faults to ensure high FDR. Then, the correlation coefficient method precisely diagnoses
Table 1: Battery test methods for common battery chemistries. Lead acid and Li-ion share communalities by keeping low resistance under normal condition; nickel-based and primary batteries reveal end-of-life by
Although various leak detection methods are available, helium mass spectrometer leak detection (HMSLD) is the preferred and is being used broadly to ensure low air and water permeation rates in cells. Even though battery leak rate standards have yet to be established, HMSLD is the preferred choice as the leak rate required to ensure battery tightness is in the 10–6 to 10–10
Liu et al. [160] applied the structural analysis theory for a battery pack to detect and isolate the various sensor faults and cooling system faults. A comparison is performed between the hardware redundancy and analytical redundancy-based fault identification methods in terms of practicability and functionality, which is listed in Table 9 .
He has been a featured speaker at numerous trade conferences on topics related to battery pack design and testing, battery-cell leak detection and leak testing of battery-pack thermal management systems. Parker joined INFICON in 2006 and previously had been the company''s leak-detection segment manager for the eastern United States. He holds a
State-of-Charge estimation for power Li-ion battery pack using V min -EKF; State-of-Charge Uncertainty of Lithium-Ion Battery Packs Considering the Cell-to-Cell Variability; State-of-Charge Estimation of Li-ion Battery Packs Based on Optic Fibre Sensor Measurements; A Modified State of Charge Estimation Method for Li-ion Batteries
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
As a brief conclusion, the major contributions and advantages compared with existing methods are as follows: (1) The proposed method adopts the idea of unsupervised learning for abnormality detection, avoiding the problems trouble caused by small training set or lack of abnormal samples; (2) An encoding guide matrix is established based on deep
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
Fault diagnosis method for lithium-ion battery packs in real-world electric vehicles based on k-means and the fréchet algorithm
Abnormalities in individual lithium-ion batteries can cause the entire battery pack to fail, thereby the operation of electric vehicles is affected and safety accidents even occur in severe cases. Therefore, timely and accurate
Besides our dynamicVAE, we also evaluate several traditional statistic methods used in battery failure detection and advanced time series anomaly detection algorithms on our dataset. As mentioned above, we perform five-fold cross validation for all the experiments. Specifically, 201 normal vehicles are divided into five folds. For each trial, we pick four folds of the normal
State-of-Charge estimation for power Li-ion battery pack using V min -EKF; State-of-Charge Uncertainty of Lithium-Ion Battery Packs Considering the Cell-to-Cell Variability; State-of
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.
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.
Considerable research efforts have been devoted to the diagnosis and evaluation of battery pack consistency. To diagnose faults and provide early warning of the inconsistencies, existing methods can be mainly divided into model-based and data-driven methods .
And adaptive thresholds are set for the detection and localization of faulty cells. To the best of our knowledge, the discrete Fréchet algorithm is presented for the first time in the field of faulty detection of battery packs. The remainder of this paper is organized as follows.
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.
A battery management system (BMS) is critical to ensure the reliability, efficiency and longevity of LIBs. Recent research has witnessed the emergence of model-based fault diagnosis methods for LIBs in advanced BMSs. This paper provides a comprehensive review on these methods.
However, the portability of the method is poor. The authors in ref (26) use the Kernel Principal Component Analysis (KPCA) approach to train a nonlinear data model for internal short-circuit detection of lithium-ion batteries. However, the method requires a large amount of historical data for offline training.
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