Abstract: The increasing adoption of lithium-ion batteries (LIBs) in low-carbon power systems is driven by their advantages, including long life, low self-discharge, and high-energy density. However, LIB failures degrade performance and cause fire hazards. Effective fault diagnosis is thus critical yet challenging. This article reviews LIB
This paper provides a comprehensive review of various fault diagnostic algorithms, including model-based and non-model-based methods. The advantages and disadvantages of the reviewed algorithms, as well as
Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems and configured by social...
Lithium-ion batteries are extensively used in electric vehicles, aerospace, communications, healthcare, and other sectors due to their high energy density, long lifespan, low self-discharge rate, and environmentally friendly characteristics (Xu et al., 2024a).However, complex
3 天之前· A multifunctional battery anomaly diagnosis method deployed on a cloud platform is proposed, meeting the needs of anomaly detection, localization, and classification. First, the proposed method extracts four anomaly features from discharge voltage to indicate battery anomalies. A risk screening process is applied to classify vehicles into high, medium, and low
3 天之前· A multifunctional battery anomaly diagnosis method deployed on a cloud platform is proposed, meeting the needs of anomaly detection, localization, and classification. First, the
This paper provides a comprehensive review of various fault diagnostic algorithms, including model-based and non-model-based methods. The advantages and disadvantages of the reviewed algorithms, as well as some future challenges for Li-ion battery fault diagnosis, are also discussed in this paper.
Aiming to address the problems of uneven brightness and small defects of low contrast on the surface of lithium-ion battery electrode (LIBE) coatings, this study proposes a defect detection method that combines background reconstruction with an enhanced Canny algorithm. Firstly, we acquire and pre-process the electrode coating image, considering the
Lithium plating, induced by fast charging and low-temperature charging, is one of the reasons for capacity fading and causes safety problems for lithium-ion batteries. Hence, reliable and effective non-destructive detection methods for lithium plating are needed. In this research, electrochemical impedance and internal resistance for batteries are measured
This article provides a comprehensive review of the mechanisms, features, and diagnosis of various faults in LIBSs, including internal battery faults, sensor faults, and actuator faults. Future trends in the
Owing to the high integration of the lithium battery management chip, simple application circuitry, full functionality, and high detection accuracy, it has been widely used to produce wearables [8, 9].However, in the lithium battery management system, the lithium battery management chip is responsible for determining the safety status of the battery and then
Abusive lithium-ion battery operations can induce micro-short circuits, which can develop into severe short circuits and eventually thermal runaway events, a significant safety concern in
This article provides a comprehensive review of the mechanisms, features, and diagnosis of various faults in LIBSs, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed.
Abusive lithium-ion battery operations can induce micro-short circuits, which can develop into severe short circuits and eventually thermal runaway events, a significant safety concern in lithium-ion battery packs. This paper aims to detect and quantify micro-short circuits before they become a safety issue. We develop offline batch least
In this paper, a simulation model of a lithium battery with thermal characteristics is established. This thermal model is coupled with a temperature-dependent 2-RC equivalent circuit model to form an electro-thermal model for lithium-ion batteries. The hybrid pulse power characterization test is used to estimate the equivalent circuit
In this paper, a battery cell anomaly detection method is proposed based on time series decomposition and an improved Manhattan distance algorithm for actual operating data of electric vehicles.
They have targeted the main components of lithium battery electrolyte such as DMC and EMC, and developed different sensors to detect them. Yang, B et al. have produced polymer semiconductor films with 2 nm thick by spin-coating, and used the organic transistors produced to detect the lithium battery electrolyte. The sensor shows a 7% response
Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to
In this paper, a battery cell anomaly detection method is proposed based on time series decomposition and an improved Manhattan distance algorithm for actual operating data of electric vehicles.
Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in
Internal short circuit mechanisms, experimental approaches and detection methods of lithium-ion batteries for electric vehicles: A review
Sheikh, M., Elmarakbi, A. & Elkady, M. Thermal runaway detection of cylindrical 18650 lithium-ion battery under quasi-static loading conditions. Journal of Power Sources 370, 61–70 (2017).
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
Lithium-ion batteries are considered the most suitable option for powering electric vehicles in modern transportation systems due to their high energy density, high energy efficiency, long cycle life, and low weight. Nonetheless, several safety concerns and their tendency to lose charge over time demand methods capable of determining their state of
With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale deployment of lithium batteries has also brought a
Lithium-ion batteries are extensively used in electric vehicles, aerospace, communications, healthcare, and other sectors due to their high energy density, long lifespan, low self-discharge rate, and environmentally friendly characteristics (Xu et al., 2024a).However, complex operating conditions and improper handling can lead to various issues, including accelerated aging,
Abstract: The increasing adoption of lithium-ion batteries (LIBs) in low-carbon power systems is driven by their advantages, including long life, low self-discharge, and high-energy density.
Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of
Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems
Statistical testing results show fast and accurate fault detection capabilities. Abusive lithium-ion battery operations can induce micro-short circuits, which can develop into severe short circuits and eventually thermal runaway events, a significant safety concern in lithium-ion battery packs.
Therefore, the most effective approach for Li-ion battery fault diagnosis should be a combination of both model-based and non-model-based methods. Table 1. Summary of Lithium-ion (Li-ion) fault diagnostic algorithms.
Micro short detection framework in lithium-ion battery pack is presented. Offline least square-based and real-time gradient-based SoH estimators are proposed. SoH estimators accurately estimate cell capacity, resistances, and current mismatch. Micro short circuits are identified by cell-to-cell comparison of current mismatch.
Sidhu et al. (31) employed the equivalent circuit and impedance spectrum methods of lithium-ion batteries to construct multiple nonlinear characteristic fault models characterizing battery overcharging, discharging, and other anomalies.
Therefore, timely and accurate detection of abnormal monomers can prevent safety accidents and reduce property losses. In this paper, a battery cell anomaly detection method is proposed based on time series decomposition and an improved Manhattan distance algorithm for actual operating data of electric vehicles.
Non-model-based methods, particularly data-driven methods, can have a crucial role in predicting battery behavior as it degrades and aiding the model development process. Therefore, the most effective approach for Li-ion battery fault diagnosis should be a combination of both model-based and non-model-based methods. Table 1.
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