In this work, we make the first attempt to identify the lifetime abnormality of lithium-ion batteries using only the first-cycle aging data. A few-shot learning network is developed to detect the lifetime abnormality, without requiring prior knowledge of degradation mechanisms.
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
Addresses some clinical, social, and philosophical issues related to the many definitions of abnormality. The authors challenge the idea that psychological abnormality, as portrayed through definitions and diagnoses of disorder, pertains to some inherently real or abnormal condition existing within the human being. Rather, this chapter considers the question of what
In this work, we make the first attempt to identify the lifetime abnormality of lithium-ion batteries using only the first-cycle aging data. A few-shot learning network is developed to detect the lifetime abnormality, without
To deal with these problems, this paper systematically achieves the goal of precise positioning, state estimation, and decision-making processing of abnormal batteries in a complete series-parallel battery pack. It also provides effective basic methods and exploration ideas for lithium battery energy storage systems to achieve intelligent
This paper proposes an innovative battery voltage abnormality diagnosis method based on a normalized coefficient of variation in real-world electric vehicles. Vehicle and laboratory data are collected and analyzed, with joint preprocessing to improve data quality, and battery voltages are log-transformed to improve the contribution of anomalous voltage
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DOI: 10.1016/J.JPOWSOUR.2020.228964 Corpus ID: 224923318; Fault diagnosis and abnormality detection of lithium-ion battery packs based on statistical distribution @article{Xue2021FaultDA, title={Fault diagnosis and abnormality detection of lithium-ion battery packs based on statistical distribution}, author={Qiao Xue and Guang Li and Yuanjian Zhang
To ensure safe and efficient battery operations and to enable timely battery system maintenance, accurate and reliable detection and diagnosis of battery faults are necessitated. In this paper, the state-of-the-art battery fault diagnosis methods are comprehensively reviewed. First, the degradation and fault mechanisms are analyzed and
Some common internal battery faults are overcharge, overdischarge, internal and external short circuit, overheating, accelerated degradation, and thermal runaway. These battery faults lead to potentially
The battery system, as the core energy storage device of new energy vehicles, faces increasing safety issues and threats. An accurate and robust fault diagnosis technique is crucial to guarantee the safe, reliable, and robust operation of lithium-ion batteries. However, in battery systems, various faults are difficult to diagnose and isolate due to their similar features
In this study, a novel data-driven framework for abnormality detection is developed through establishment of a neural network with interpretable modules on top of an
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In this study, a novel data-driven framework for abnormality detection is developed through establishment of a neural network with interpretable modules on top of an Autoencoder using data from real EVs to recognize abnormality while charging.
Common Causes of Toyota RAV4 Voltage Abnormality and Low Steering Power. Toyota RAV4 Model-Specific Issues: Some Toyota RAV4 models may have specific electrical or power steering system issues that can lead to voltage
The usage of Lithium-ion (Li-ion) batteries has increased significantly in recent years due to their long lifespan, high energy density, high power density, and environmental benefits. However, various internal and external faults can occur during the battery operation, leading to performance issues and potentially serious consequences, such as thermal
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. This work proposes a novel data-driven method to detect long-term latent fault and abnormality for electric vehicles (EVs) based on real-world operation data.
To deal with these problems, this paper systematically achieves the goal of precise positioning, state estimation, and decision-making processing of abnormal batteries in a complete series
Some common internal battery faults are overcharge, overdischarge, internal and external short circuit, overheating, accelerated degradation, and thermal runaway. These battery faults lead to potentially hazardous consequences, such as an increase in temperature and pressure, which could increase the risk of combustion and explosion [7].
Battery storage is usually applied in the renewable energy (RE) plant for improving RE utilization and integration ability to the power grid. Battery health status detection is essential for plant
Abnormality as a concept has been used in many cultures for a long time. Many synonyms and antonyms of abnormality exist. Abnormality is used to judge abilities and morphologies of the human body and other biological entities such
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 detection of abnormal monomers can prevent safety accidents and reduce property losses.
Abnormal battery temperature can result in decreased battery performance, shortened lifespan, safety hazards such as fire or explosion, potential system faults, and
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
To address these issues, this paper proposes a comprehensive fault diagnosis method utilizing hybrid coding and genetic search. The Lyapunov index between predicted and faulty battery
To ensure safe and efficient battery operations and to enable timely battery system maintenance, accurate and reliable detection and diagnosis of battery faults are
To address these issues, this paper proposes a comprehensive fault diagnosis method utilizing hybrid coding and genetic search. The Lyapunov index between predicted and faulty battery states is applied to calculate trajectory divergence rates, facilitating the detection of abnormal battery conditions. Fault modes are uniformly characterized
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Abnormal battery temperature can result in decreased battery performance, shortened lifespan, safety hazards such as fire or explosion, potential system faults, and unstable operation. Remedies include cool-down treatments, system resets, overhaul and maintenance, software updates, and safe energy discharge.
Battery storage is usually applied in the renewable energy (RE) plant for improving RE utilization and integration ability to the power grid. Battery health status detection is essential for plant reliable, safe, and efficient operation. This paper presents a battery anomaly and degradation diagnosis method based on data mining technology.
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.
These seven batteries are, therefore, defined as “abnormal”. From the data monitoring point of view, these abnormal samples are also defined as “positive samples”, while the normal batteries are termed as “negative samples” in the following discussions. Illustration of our battery aging data. a) Initial resistance versus capacity of 215 batteries.
Battery temperature abnormalities mainly included excessive temperature and rapid temperature rise. The dangers of high temperatures, as detailed in the previous discussion, include accelerated battery capacity decay, power loss, structural dissolution, electrolyte decomposition, and the potential for thermal runaway.
Abstract: 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.
Verified with the largest known dataset with 215 commercial lithium-ion batteries, the method can identify all abnormal batteries, with a false alarm rate of only 3.8%. It is also found that any capacity and resistance-based approach can easily fail to screen out a large proportion of the abnormal batteries, which should be given enough attention.
With these issues in mind, the early-stage identification of the battery lifetime abnormality remains an unsolved problem in the field of battery manufacturing and management. In this work, we make the first attempt to identify the lifetime abnormality of lithium-ion batteries using only the first-cycle aging data.
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