In addition, a battery system failure index is proposed to evaluate battery fault conditions. The results indicate that the proposed long-term feature analysis method can effectively detect and diagnose faults. Accurate detection and diagnosis battery faults are increasingly important to guarantee safety and reliability of battery systems.
At present, the analysis and prediction methods for battery failure are mainly divided into three categories: data-driven, model-based, and threshold-based. The three methods have different characteristics and limitations due to their different mechanisms. This paper first introduces the types and principles of battery faults.
These algorithms analyze large volumes of data from battery sensors for example, voltage, current, temperature, and impedance in order to identify patterns indicative of faults and predict the remaining useful life of batteries.
While the automotive industry recognizes the importance of utilizing field data for battery performance evaluation and optimization, its practical implementation faces challenges in data collection and the lack of field data-based prognosis methods.
As electric vehicles advance in electrification and intelligence, the diagnostic approach for battery faults is transitioning from individual battery cell analysis to comprehensive assessment of the entire battery system. This shift involves integrating multidimensional data to effectively identify and predict faults.
In the event of a thermal runaway or fire , robust data analysis enables quicker root-cause identification, while postmarket surveillance allows for more precise and cost-effective recall management. As battery monitoring technologies advance, Utilizing this data will be crucial for improving battery performance and extending lifespan [59, 60].
Advanced data-driven fault diagnosis in lithium-ion battery …
Fault diagnosis methods for EV power lithium batteries are designed to detect and identify potential performance issues or abnormalities. Researchers have gathered …
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Analysis and key findings from real-world electric vehicle field …
We analyze, and share with the public, battery pack data collected from the field operation of an electric vehicle, after implementing a processing pipeline to analyze one year of 1,655 battery signals. We define performance indicators, driving resistance and charging impedance, to monitor online the battery pack health. An analysis of the ...
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Three-dimensional electrochemical-magnetic-thermal coupling …
In this paper, a three-dimensional model of electrochemical-magnetic field-thermal coupling is formulated with lithium-ion pouch cells as the research focus, and the spatial distribution pattern...
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Research progress in fault detection of battery systems: A review
As electric vehicles advance in electrification and intelligence, the diagnostic approach for battery faults is transitioning from individual battery cell analysis to comprehensive assessment of the entire battery system. This shift involves integrating multidimensional data to effectively identify and predict faults. The methodologies employed for fault localization and …
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Gaussian process-based online health monitoring and fault analysis …
Health monitoring, fault analysis, and detection methods are important to operate battery systems safely. We apply Gaussian process resistance models on lithium-iron-phosphate (LFP) battery field data to separate the time …
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Non-destructive detection techniques for lithium-ion batteries …
Multiple battery degradation and faults analysis. Electrochemical-magnetic field model. 1. Introduction. Lithium-ion batteries are extensively employed in a wide range of fields, owing to their notable attributes such as high energy density and long cycle life [1, 2]. Localized degradation and faults frequently occur in batteries, such as tab fractures, current collector …
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An exhaustive review of battery faults and diagnostic techniques …
As a high-energy carrier, a battery can cause massive damage if abnormal energy release occurs. Therefore, battery system safety is the priority for electric vehicles (EVs) [9].The most severe phenomenon is battery thermal runaway (BTR), an exothermic chain reaction that rapidly increases the battery''s internal temperature [10].BTR can lead to overheating, fire, …
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Lithium-Ion Battery System Health Monitoring and ...
Health monitoring is important for the safe operation of battery systems. We use recursive spatiotemporal Gaussian processes to model the resistance of lithium iron phosphate batteries from field data. These processes scale linearly with …
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Gaussian process-based online health monitoring and fault …
Health monitoring, fault analysis, and detection methods are important to operate battery systems safely. We apply Gaussian process resistance models on lithium-iron …
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Large-scale field data-based battery aging prediction driven by ...
This research emphasizes a field data-based framework for battery health management, which not only provides a vital basis for onboard health monitoring and prognosis but also paves the way for battery second-life evaluation scenarios.
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Analysis and key findings from real-world electric …
We analyze, and share with the public, battery pack data collected from the field operation of an electric vehicle, after implementing a processing pipeline to analyze one year of 1,655 battery signals. We define …
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Advanced data-driven fault diagnosis in lithium-ion battery …
Fault diagnosis methods for EV power lithium batteries are designed to detect and identify potential performance issues or abnormalities. Researchers have gathered valuable insights into battery health, detecting potential faults that are critical to maintaining the reliable and efficient operation of EV lithium batteries [[29], [30], [31], [32]].
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Comprehensive fault diagnosis of lithium-ion batteries: An …
Statistical analysis-based methods diagnose battery faults by identifying abnormal characteristics in observation data and comparing these with predefined thresholds. These approaches …
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In Situ Inversion of Lithium-Ion Battery Pack Unbalanced Current …
The performance inconsistency of lithium-ion battery packs is one of the key factors that lead to their accelerated lifespan degradation and reduced reliability. Hence, it is of great significance to accurately detect the consistency of cell parameters within the pack without destructive testing. The working current of the cell is the most direct and effective parameter to characterize the ...
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Large-scale field data-based battery aging prediction …
This research emphasizes a field data-based framework for battery health management, which not only provides a vital basis for onboard health monitoring and prognosis but also paves the way for battery second-life …
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Research progress in fault detection of battery systems: A review
As electric vehicles advance in electrification and intelligence, the diagnostic approach for battery faults is transitioning from individual battery cell analysis to comprehensive assessment of the entire battery system. This shift involves integrating multidimensional data to effectively identify and predict faults. The methodologies employed ...
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Short circuit detection in lithium-ion battery packs
The proposed method is tested using field data from a battery electric locomotive under nominal operation and external short circuits (ESC). With sufficiently excited current inputs, the experimental results show that a leakage current of more than 27 mA C / 4000) can be accurately detected. Using field test data from a battery electric locomotive, an experimental 15 Ω ESC …
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Battery degradation diagnosis with field data ...
The urgency of reducing CO 2 emissions provides a strong impetus for developing battery systems as one of the most popular energy storage systems in the field of both stationary and automotive applications [1, 2] pared with other battery chemistry, lithium-ion batteries (LIBs) show advantages in energy density, power density, and self-discharge rate.
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Lithium-Ion Battery System Health Monitoring and ...
Health monitoring is important for the safe operation of battery systems. We use recursive spatiotemporal Gaussian processes to model the resistance of lithium iron phosphate batteries …
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Battery degradation diagnosis with field data ...
Here, we present a degradation diagnosis framework for lithium-ion batteries by integrating field data, impedance-based modeling, and artificial intelligence, revolutionizing the degradation identification with accurate and robust estimation of both capacity and power fade together with degradation mode analysis. By integrating an impedance ...
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Three-dimensional electrochemical-magnetic-thermal coupling …
In this paper, a three-dimensional model of electrochemical-magnetic field-thermal coupling is formulated with lithium-ion pouch cells as the research focus, and the …
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Defect Detection in Lithium-Ion Batteries Using Non-destructive ...
Ultrasonic battery detection depends on the transmission and analysis of high-frequency sound waves (ultrasonic waves) through the battery. These waves interact with the interior components of battery, including electrodes, electrolyte, and separators, providing valuable information about the battery condition (Wang et al. 2024b ).
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Enhanced Wavelet Transform Dynamic Attention Transformer …
Rapid advancements in electric vehicle (EV) technology have highlighted the importance of lithium-ion (Li) batteries. These batteries are essential for safety and reliability. Battery data show non-stationarity and complex dynamics, presenting challenges for current monitoring and prediction methods. These methods often fail to manage the variability seen in …
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Fault Diagnosis and Detection for Battery System in Real-World …
Accurate detection and diagnosis battery faults are increasingly important to guarantee safety and reliability of battery systems. Developed methods for battery early fault diagnosis concentrate on short-term data to analyze the deviation of external features without considering the long-term latent period of faults.
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Fault Diagnosis and Detection for Battery System in Real-World …
Accurate detection and diagnosis battery faults are increasingly important to guarantee safety and reliability of battery systems. Developed methods for battery early fault …
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Comprehensive fault diagnosis of lithium-ion batteries: An …
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 …
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Research progress in fault detection of battery systems: A review
As electric vehicles advance in electrification and intelligence, the diagnostic approach for battery faults is transitioning from individual battery cell analysis to …
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