Diagnosis of connection fault for parallel-connected lithium-ion
Due to the increasing environmental pollution and the shortage of fossil fuels [1], lithium-ion batteries have been used more and more extensively as the power source of electric vehicles (EVs) and energy storage systems because of their advantages of high energy density, long life, and low self-discharge [2] order to meet the energy and power
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Review of Lithium-Ion Battery Fault Features, Diagnosis Methods,
This article reviews LIB fault mechanisms, features, and methods with object of providing an overview of fault diagnosis techniques, emphasizing feature extraction''s critical role in
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Gramian angular field-based state-of-health estimation of lithium-ion
Al-Dulaimi et al. [35] proposed a lithium-ion battery degradation diagnosis method based on data-driven methods and a novel deep neural network model, which can accurately predict the degradation patterns of different battery chemistries (LFP, NCA, NMC) by applying data conversion technologies such as GASF and DTW, showing high prediction
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A Review of Lithium-Ion Battery Fault
A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles. IEEE Trans. Power Electron. 2019, 34, 9709–9718. [Google Scholar] Zheng, C.;
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Dynamic Impedance Spectrum: A Novel Metric for Lithium-Ion Batteries
Abstract: Diagnosis of overcharging in lithium-ion batteries (LIBs) is crucial to guaranteeing the long-term thermal stability and operational lifespan of a battery system. Compared with conventional diagnosis methods that rely on cell temperature and voltage measurements, the dynamic impedance spectrum (DIS) provides novel insights into assessing
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A Lithium-Ion Batteries Fault Diagnosis Method for Accurate
Real-time and accurate estimating state-of-charge (SOC) of a lithium-ion battery is a critical but technically challenging task for battery management systems. Coulomb counting algorithm is an effective real-time SOC estimation algorithm but suffers from three typical faults: initial SOC fault, battery capacity fault, and biased load current measurement fault, making its
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Improvement of local outlier factor algorithms for lithium-ion battery
Fault diagnosis is one of the most important active strategies to protect lithium-ion batteries (LIBs) from safety accidents. The tasks of fault diagnosis usually can be divided into three levels, i.e., (1) fault detection, (2) fault isolation, and (3) fault estimation.
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Lithium-Ion Battery Cell Open Circuit Fault Diagnostics: Methods
Abstract: Battery fault diagnosis has great significance for guaranteeing the safety and reliability of lithium-ion battery (LIB) systems. Out of many possible failure modes of the series–parallel connected LIB pack, cell open circuit (COC) fault is a significant part of the causes that lead to the strong inconsistency in the pack and the reduction of pack life.
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Lithium-ion Battery Tab Welding Diagnosis using Electrical
This paper investigates the use of electrical reflectometry as a non-destructive testing technique to monitor the health of battery tab welds in a parallel pack configuration. 3D models of cylindrical lithium-ion cells, connected by tabs at each extremity via copper welding, were developed. Current surface distribution analyses were conducted to understand reflectometry signal
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Diagnosis of lithium-ion batteries degradation with P2D model
Diagnosis of lithium-ion batteries degradation with P2D model parameters identification: A case study on low temperature charging. Author links open overlay panel G. Sordi, Lithium-ion batteries are spreading thanks to their high energy density and relatively low cost, especially in the field of electric vehicles and stationary energy
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A Review on the Fault and Defect Diagnosis
An accurate and robust fault diagnosis technique is crucial to guarantee the safe, reliable, and robust operation of lithium-ion batteries. However, in battery
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Advanced Fault Diagnosis for Lithium-Ion Battery
Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This article provides a comprehensive review of the mechanisms, features, and diagnosis of various
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State-of-life prognosis and diagnosis of lithium-ion batteries
In recent years, Lithium-ion (Li-ion) batteries have gained large popularity as portable energy sources due to their significant advantages with respect to other battery types, such as: (i) the lower weight, due to the lightweight lithium and carbon-made electrodes, and, at the same time, the larger energy density, due to the high chemical reactivity of lithium; (ii) the
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Lithium-ion battery aging mechanisms and diagnosis method
This paper summarizes the aging mechanisms of lithium-ion batteries and the diagnosis methods of battery aging. A coupling result arising from a variety of aging reactions reduces the battery capacity and increases internal resistance. Different temperatures, charge-discharge rates, and DOD can give rise to the evolution of the dominant aging
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A method of lithium-ion battery failure diagnosis based on
In this work, a new method of battery failure diagnosis in terms of capacity fading is proposed based on the heterogeneous multi-physics aging model of lithium-ion batteries. The key parameters are obtained by parameter identification method, and the parameter boundaries when the battery is on the verge of failure are obtained by model driven method.
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Multi-scenario failure diagnosis for lithium-ion battery based on
Such concerns mostly can be attributed to lithium-ion batteries, which are the main energy storage system. The lithium-ion batteries always suffer from harsh driving conditions, tough seasonal environments and incidental manufacturing defects, and then they lead to the accelerating degradation of battery performances and even thermal runaway.
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Model-Based Multi-Fault Diagnosis for Lithium-Ion Battery
Request PDF | On Oct 28, 2022, Kai Zhang and others published Model-Based Multi-Fault Diagnosis for Lithium-Ion Battery Systems | Find, read and cite all the research you need on ResearchGate
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Review of sensor fault diagnosis and fault-tolerant control
Number of publications on the ISC and sensor fault diagnosis for EVs (source: Web of Science; keyword: sensor fault for lithium-ion battery, ISC for lithium-ion battery; date: November 13, 2023). Therefore, the main objective of this review is to comprehensively analyze sensor faults in LIBs and summarize the efforts by researchers in developing fault diagnosis
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Online diagnosis of state of health for lithium-ion batteries based
Lithium-ion batteries have been considered as a mainstream solution of storing electrical energy and have been popularly used as energy storage elements in electric vehicles (EVs) due to their long lifespan, fast charging capability and high energy density, compared to other energy storage media [1].However, the performances of lithium-ion batteries will be
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A review of lithium-ion battery state of health and remaining
Degradation diagnosis of lithium-ion batteries with a LiNi 0.5 Co 0.2 Mn 0.3 O 2 and LiMn 2 O 4 blended cathode using dV/dQ curve analysis. Journal of Power Sources, 390 (2018), pp. 278-285. View PDF View article View in Scopus Google Scholar. Ansari et al., 2022. S. Ansari, A. Ayob, M.S. Hossain Lipu, et al.
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Unsupervised learning for lithium-ion batteries fault diagnosis and
Battery fault diagnosis is developing rapidly in two directions. The first one is to apply new sensors such as mechanics and optical fiber, or the use of ultrasonic and impedance detection
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EV Lithium Ion Battery Fault Diagnosis Dataset
To train and test two ANN models, two separate datasets are developed based on battery modelling in MATLAB. State of Charge (SOC), Temperature and Voltage are the input features in both datasets. Where as against output label column in first dataset 0 represents healthy battery and 1 represents unhealthy battery. Similarly in second dataset 0 represents
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Knowledge-data driven sampling diagnosis algorithm for lithium
As the concerning of robust voltage sampling, researchers have paid amounts of efforts to investigate the failure mode and diagnosis. Recently, Zhao et al. [6] developed a multi-step voltage prediction and voltage fault diagnosis method based on gated recurrent unit neural network and incremental training. The method has the ability to predict the battery
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A Review of Lithium-Ion Battery Fault
This paper provides a comprehensive review of various fault diagnostic algorithms, including model-based and non-model-based methods. The advantages and
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Recent Advances in Model-Based Fault Diagnosis for Lithium-Ion
Table 2: Differences between different reviews on lithium-ion battery fault diagnosis. The rest of this review is organized as follows. Section 2 introduces the battery models including physics-based electrochemical models (EMs) and electrical equivalent circuit models (ECMs). Section 3 presents a general state-space representation for the ECM
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Review of Abnormality Detection and Fault Diagnosis Methods for
Cloud-based battery management provides a new route for effective monitoring, control, diagnosis, and fault correction of battery systems. Novel insights on cloud-based
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Lithium-Ion Battery Real-Time Diagnosis
The health and safety of lithium-ion batteries are closely related to internal parameters. The rapid development of electric vehicles has boosted the demand for online battery
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Basics of lithium-ion batteries, deterioration diagnosis and
Introduction to Lithium-Ion Batteries. Lithium-ion batteries have become a cornerstone of modern technology, powering everything from smartphones to electric vehicles. These batteries are renowned for their energy efficiency, high power density, and long lifespan compared to older battery technologies like nickel-cadmium (NiCd) or lead-acid
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Online health diagnosis of lithium-ion batteries based on
Compared with the other battery technologies, lithium-ion (Li-ion) batteries have achieved dominance in terms of applicability in energy storage systems (ESS) such as electric mobility and stationary applications. This relies on their promising characteristics, such as superior energy and power density, high efficiency, and long lifetime [1].
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Diagnosis for wetting condition of silicon/graphite (Si/Gr) anode
The need for the longer mileage of electric vehicles has pushed the energy density of lithium ion batteries (LIBs) to their limits. However, enhancing the energy density of LIBs usually causes poor wetting of an anode electrode and its higher ionic resistance (R ion), resulting in lithium (Li) plating.Therefore, it is highly crucial to monitor the anode R ion
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Comprehensive fault diagnosis of lithium-ion batteries: An
4 天之前· Download Citation | On Feb 1, 2025, Chunhui Ji and others published Comprehensive fault diagnosis of lithium-ion batteries: An innovative approach based on hybrid coding and
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Recent advances in model-based fault diagnosis for lithium-ion
Challenges and outlook for lithium-ion battery fault diagnosis methods from the laboratory to real world applications. eTransp, 17 (2023), Article 100254. View PDF View article View in Scopus Google Scholar [20] Yang Y., Wang R., Shen Z., Yu Q., Xiong R., Shen W.
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Towards High-Safety Lithium-Ion Battery Diagnosis Methods
Based on lithium-ion batteries'' aging mechanism and fault causes, this paper summarizes the general methods of fault diagnosis at a macro level. Moreover, lithium-ion
View more6 FAQs about [Lithium-ion Batteries and Diagnosis]
Can lithium-ion battery data be used for fault diagnosis?
Lithium-ion battery data for fault diagnosis in different applications are comprehensively analyzed. Fault modes and diagnosis methods across application scenarios are reviewed. Fault diagnosis methods for both laboratory and real-world applications are summarized.
How to diagnose a lithium ion battery?
For multi-fault diagnosis and localization of lithium-ion batteries, the voltage sensor measurement topology of the series-connected battery pack is designed. Then the connection fault (CF), ESC, ISC, and voltage sensor fault (VSF) diagnosis only require the voltage data [47, 48].
Why is voltage used in lithium-ion battery fault diagnosis?
Measurement data Among the lithium-ion battery measurement data, voltage is widely used in fault diagnosis methods because of its simple acquisition, its ability to characterize the battery state, and its ease of distinguishing the lithium-ion battery fault type.
Can a laboratory simulation be used to diagnose lithium-ion battery faults?
Applying the laboratory simulation to a real-world scenario is one of the primary challenges in lithium-ion battery fault diagnosis, and there are few solutions available. Gan et al. realized the accurate diagnosis of OD fault by training the unified framework of voltage prediction based on the predicted voltage residual.
Do lithium-ion batteries have faults?
For the battery to run safely, stably, and with high efficiency, the precise and reliable prognosis and diagnosis of possible or already occurred faults is a key factor. Based on lithium-ion batteries’ aging mechanism and fault causes, this paper summarizes the general methods of fault diagnosis at a macro level.
How to transition lithium-ion battery fault diagnosis from laboratory to real world?
In general, there are three ways to transition lithium-ion battery fault diagnosis from the laboratory to the real world: unified framework of fault diagnosis method, cloud big data fusion, and application of laboratory measurement technology.
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