Internal short circuit detection and evaluation in battery packs
The motivations of achieving carbon peak and carbon neutrality have accelerated the continuous development of electric vehicles (EVs) [1, 2].Lithium-ion batteries (LIBs) as a reliable and promising power source, with the advantages of high power density and long cycling life, are widely used in EVs [3, 4].However, due to manufacturing defects, various types of
Frontiers | A Fault Diagnosis Method for Lithium-Ion
This paper presents a fault diagnosis method for the electric vehicle power battery using the improved radial basis function (RBF) neural network. First, the fault information of lithium-ion battery packs was collected
Multi-fault detection and diagnosis method for battery packs
Voltage fault detection for lithium-ion battery pack using local outlier factor. Measurement (2019) B. Xia et al. A fault-tolerant voltage measurement method for series connected battery packs. J Power Sources (2016) Y. Kang et al.
A new method to perform Lithium-ion battery pack fault
Highlights A novel fault diagnosis algorithm for eVTOL battery packs was presented. The algorithm was developed to work during the charge cycle, thus minimising
Model-free detection and quantitative assessment of micro short
Therefore, we construct a fault detection model with the DBSCAN algorithm to achieve accurate detection of MSC cells within lithium-ion battery packs. Specifically, we use the first two principal components of the IC curves obtained in Section 2.2 as classification features, which are input into the DBSCAN algorithm to detect MSC cells.
Detection and quantitative diagnosis of micro-short-circuit faults
ISCs in lithium-ion batteries are usually triggered by mechanical, electrical, and thermal abuses [7].Mechanical abuse, such as collision, extrusion, or punctures, can damage the battery structure and cause the battery to suffer severe deformation, which in turn may lead to an electrical connection between the positive and negative electrodes and thus trigger a short
Application of different charging methods for lithium-ion battery packs
A control-oriented lithium-ion battery pack model for plug-in hybrid electric vehicle cycle-life studies and system design with consideration of health management. J Power Sources 2015; 279: 791 Zhang M, et al. Internal short circuit detection for lithium-ion battery pack with parallel-series hybrid connections. J Clean Prod 2020; 255:
Fault Diagnosis Method for Lithium-Ion Battery Packs in Real
Based on the voltage data, this paper develops a fault warning algorithm for electric vehicle lithium-ion battery packs based on K-means and the Fréchet algorithm.
Performance reliability analysis and optimization of lithium
There are many approaches being used to improve the reliability of lithium-ion battery packs (LIBPs). Among them, fault-tolerant technology based on redundant design is an effective method [4, 5].At the same time, redundant design is accompanied by changes in the structure and layout, which will affect the reliability of battery packs.
Fault Diagnosis Method for Lithium-Ion
The authors utilized an observer based on an electrochemical model and a fuzzy logic algorithm that can be implemented in real time. A battery internal fault diagnosis
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
Internal short circuit detection for lithium-ion battery pack
Internal short circuit is one of the unsolved safety problems that may trigger the thermal runaway of lithium-ion batteries. This paper aims to detect the internal short circuit that occurs in battery pack with parallel-series hybrid connections based on the symmetrical loop circuit topology.The theory of the symmetrical loop circuit topology answers the question that:
Fault detection method for electric vehicle battery pack based on
Conventional fault diagnosis methods often struggle to detect minor faults in their early stages. To address this challenge, this paper proposes a fault diagnosis method for
Multi-sensor multi-mode fault diagnosis for lithium-ion battery packs
As such, lithium-ion battery packs in real-world operation scenarios are typically equipped with a battery management system (BMS) for condition monitoring, thermal management, equalization management, and fault diagnosis to ensure their safe and efficient operation [4], [5], [6]. The success of any BMS depends upon the accurate acquisition of data
Online multi-fault detection and diagnosis for battery packs in
Secondly, the lithium-ion battery is a very complicated electrochemical system [12]. From the mathematical point of view, the battery cell is a nonlinear system with many influencing factors. Meanwhile, the cells in a battery pack have inconsistency in temperature, state of charge (SoC) and state of health (SoH) [13].
Detection and isolation of faults in a lithium-ion battery pack
Lithium-ion battery packs are typically built as a series network of Parallel Cell Modules (PCM). A fault can occur within a specific cell of a PCM, in the sensors, or the numerous connection joints and bus conductors. This paper presents a method of detecting a single occurrence of various common faults in a Lithium-ion battery pack and isolating the fault to the
Data-Driven Fault Detection for Lithium-Ion Battery Packs via
Fault detection and diagnosis of lithium-ion batteries have been of intense investigation in energy systems, but most applicable methods rely on precise and com
Voltage measurement-based recursive adaptive method for
Electric vehicles (EVs) and battery energy storage systems (BESS) that use lithium-ion (Li-ion) batteries as the energy medium are becoming increasingly important in our daily lives (Aubeck et al., 2022, Shafikhani et al., 2021).However, various failures can occur during the usage of Li-ion batteries, leading to accidents such as fires and explosions of EVs
Faulty sensor signal reconstruction in Li-ion battery packs
Early detection and isolation of faults in battery packs are critical to improving performance and ensuring safety. Sensor-related faults such as noisy measurements, sensor bias, sensor drift,
An Approach for Automated Disassembly of Lithium-Ion Battery Packs
To improve the sorting of the battery pack components to achieve high-quality recycling after the disassembly, a labeling system containing the relevant data (e.g., cathode chemistry) about the battery pack is proposed. In addition, the use of sensor-based sorting technologies for peripheral components of the battery pack is evaluated. For this
An Early Soft Internal Short-Circuit Fault Diagnosis Method for Lithium
The early detection of soft internal short-circuit faults in lithium-ion battery packs is critical to ensuring the safe and reliable operation of electric vehicles. This article proposes a fault diagnosis method that can achieve the detection and assessment of soft internal short-circuit faults for lithium-ion battery packs. Specifically, based on the incremental capacity curve, fault
Online lithium-ion battery intelligent perception for thermal fault
The impact of the battery pack''s packaging shape [12] and cooling technique [13] on its thermal performance, as well as variations in battery voltage, current, state of charge (SOC), and other parameters, must all be taken into consideration in BTMS research addition to preheating the battery in a low-temperature environment [14], BTMS must prevent thermal
Early Internal Short Circuit Detection and Quantitative Diagnosis
Li-ion batteries are extensively utilized in energy storage and automotive fields due to their high energy density, long lifespan, and low cost advantages. However, thermal runaway caused by internal short circuits in Li-ion battery cells occasionally happens. Early internal short circuit detection and warning are crucial for ensuring the safe and stable operation of lithium-ion
Review of Abnormality Detection and Fault Diagnosis Methods for Lithium
Electric vehicles are developing prosperously in recent years. Lithium-ion batteries have become the dominant energy storage device in electric vehicle application because of its advantages such as high power density and long cycle life. To ensure safe and efficient battery operations and to enable timely battery system maintenance, accurate and reliable
Fault Diagnosis Method for Lithium-Ion Battery Packs in Real
The current research on battery for electric vehicles has been mentioned in many types of literature, such as battery fault diagnosis, estimation of remaining useful life for batteries, state of the health estimation, etc. 6−12 And the research approaches in the literature about fault diagnosis can be broadly classified into three categories: knowledge-based, model
Koopman Mode-Based Detection of Internal Short Circuits in
Monitoring of internal short circuit (ISC) in Lithium-ion battery packs is imperative to safe operations, optimal performance, and extension of pack life. M. D''Arpino, and G. Rizzoni, "Optimal sensor placement in lithium-ion battery pack for fault detection and isolation," arXiv preprint arXiv:2008.10533, 2020. [36] K. Zhang, X. Hu, Y
Fault detection of lithium-ion battery packs with a graph
A fast fault detection of lithium-ion battery (LiB) packs is critically important for electronic vehicles. In previous literatures, an interleaved voltage measurement topology is commonly used to collect working voltage of each cell in LiB packs. However, previous studies ignore the structure information of voltage sensor layout, leading to a
A Method for Abnormality Detection of Lithium-Ion Battery Packs
Accordingly, this paper proposes a feature selection method based on Kullback-Leibler (K-L) test and an improved Greenwald-Khanna (GK) clustering algorithm.
Short circuit detection in lithium-ion battery packs
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.
Data-Driven Fault Detection for Lithium-Ion Battery Packs via
Fault detection and diagnosis of lithium-ion batteries have been of intense investigation in energy systems, but most applicable methods rely on precise and complicated mechanistic models, which are nontrivial to establish in practice. The recently emerging behavioral system theory yields a new model-free representation of dynamical systems using only a single input-output
6 FAQs about [Detection and repair of lithium battery packs]
Can a lithium-ion battery pack detect a single occurrence of a fault?
This paper presents a method of detecting a single occurrence of various common faults in a Lithium-ion battery pack and isolating the fault to the faulty PCM, its connecting conductors, and joints, or to the sensor in the pack using a Diagnostic Automata of configurable Equivalent Cell Diagnosers.
Is there a fault warning algorithm for electric vehicle lithium-ion battery packs?
Based on the voltage data, this paper develops a fault warning algorithm for electric vehicle lithium-ion battery packs based on K-means and the Fréchet algorithm. And the actual collected EV driving data are used to verify.
What is a fault diagnosis model for lithium-ion batteries?
The principal component analysis method was used first to estimate the fault information. Then a developed cross-cell monitoring algorithm was used to carry out the fault diagnosis. Jiang et al. (2021) proposed a new signal-based fault diagnosis model for lithium-ion batteries.
What is a diagnostic algorithm for lithium ion battery packs?
Diagnostic algorithm is executed on a microcontroller and tested in real-time. Lithium-ion battery packs are typically built as a series network of Parallel Cell Modules (PCM). A fault can occur within a specific cell of a PCM, in the sensors, or the numerous connection joints and bus conductors.
How can faults detection and abnormality of battery pack be detected?
As discussed above, the faults diagnosis and abnormality of battery pack can be detected in real time. In addition, timely detection and positioning of faults and defects of cells can improve the health and safety of the whole battery pack.
Can RBF neural networks detect faults in lithium-ion battery packs?
The diagnosis test results showed that the improved RBF neural networks could effectively identify the fault diagnosis information of the lithium-ion battery packs, and the diagnosis accuracy was about 100%. With the increasing attractiveness of new energy vehicles, the safety of the electric vehicle battery is crucial.