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.
The purpose of this study was to develop a battery risk diagnosis method based on both model and data-driven approaches, aiming to improve the recognition rate of battery abnormal states and the accuracy of thermal runaway warning.
The core of the paper lies in combining the advantages of data-driven and model-driven methods, and achieving early diagnosis of battery thermal runaway behavior by real-time monitoring of outlier cells and battery data in the battery pack.
Among the numerous battery parameters, the output voltage of the battery is commonly utilized for predicting the timing of failure and diagnosing the type of failure. Shang et al. utilized a methodology of predicting failure time by analyzing the voltage sequence within a moving window, thus enhancing the precision of fault diagnosis.
Battery defect detection based on the abnormality of external parameters is a promising way to reduce this kind of thermal runaway accidents and protect EV consumers from fire danger. However, the influence of temperature and EV states, i.e., charging and driving, on the battery characteristic will complicate the method establishment.
The training process only uses data of normal batteries to cope with the inadequacy of thermal runaway battery data. The results show that the method can detect defected batteries 13 days ahead the thermal runaway while achieve the precision of 99.2%.
An exhaustive review of battery faults and diagnostic techniques …
The proposed method can efficiently and accurately detect internal short-circuit faults and has great potential for application in fault diagnosis of large energy storage battery packs. Meanwhile, Tran et al. proposed a real-time model-based sensor fault detection and isolation scheme for lithium-ion battery degradation [ 161 ].
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Research on power battery anomaly detection method based on …
Accurate and efficient power battery anomaly detection is crucial to ensure stable operation of the battery system and energy saving. However, power battery data are often non-linear and unstable due to external factors, such as temperature conditions, which pose challenges for anomaly detection. Existing methods for collecting battery data''s temporal and …
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Research on internal short circuit detection method for lithium …
Since ISCs are one of the primary reasons for battery failure [[21], [22], [23]], researchers worldwide have studied their experimental simulation and detection methods extensively.Currently, ISCs simulation experiments are carried out mainly through battery abuse and the production of defective cells [24].For instance, Zhu et al. [25] conducted a series of …
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Research on power battery anomaly detection method based on …
A novel network structure for power battery anomaly detection based on an improved TimesNet is proposed, achieving an improvement of 1%–19% in the F1 value and 1%–3% in the ACC compared to the other models. Health monitoring and abnormality detection of power batteries for new energy vehicles has been one of the hot topics in recent years. …
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Comprehensive fault diagnosis of lithium-ion batteries: An …
Multi-fault detection and diagnosis method for battery packs based on statistical analysis. Energy, 293 (2024), Article 130465, 10.1016/j.energy.2024.130465. View PDF View article View in …
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Cloud-Based Li-ion Battery Anomaly Detection, Localization and ...
3 · 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 ...
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Comprehensive fault diagnosis of lithium-ion batteries: An …
Multi-fault detection and diagnosis method for battery packs based on statistical analysis. Energy, 293 (2024), Article 130465, 10.1016/j.energy.2024.130465. View PDF View article View in Scopus Google Scholar. Ma et al., 2022. M. Ma, X. Li, W. Gao, J. Sun, Q. Wang, C. Mi. Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution …
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Prevent Winter Failures with Autel Battery Diagnostics | Triad-DS
The root causes of winter-related battery and electrical system failures and highlights Autel''s advanced diagnostic tools, available through Triad Diagnostic Solutions, as the ultimate …
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Prevent Winter Failures with Autel Battery Diagnostics | Triad-DS
The root causes of winter-related battery and electrical system failures and highlights Autel''s advanced diagnostic tools, available through Triad Diagnostic Solutions, as the ultimate solution for identifying, diagnosing, and preventing these failures.
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Ion Chromatography with Post-column Reaction and Serial
Conductivity and Spectrophotometric Detection Method Development for Quantification of Transition Metal Dissolution in Lithium Ion Battery Electrolytes Britta Vortmann‑Westhoven1 · Marcel Diehl1 · Martin Winter1,2 · Sascha Nowak1 Received: 6 February 2018 / Revised: 27 April 2018 / Accepted: 2 May 2018 / Published online: 8 May 2018
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Data-Driven Thermal Anomaly Detection in Large …
The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online anomaly detection in battery packs that uses real …
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Fault Diagnosis and Detection for Battery System in Real-World …
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. Specifically, …
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A Sensor-Fault-Estimation Method for Lithium-Ion Batteries in
Descriptor proportional and derivate observer systems are applied for sensor diagnosis, based on electrical and thermal models of lithium-ion batteries, which can realize the real-time estimation of voltage sensor fault, current sensor fault, and temperature sensor fault.
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Precision-Concentrated Battery Defect Detection Method in Real …
The results show that the method can detect defected batteries 13 days ahead the thermal runaway while achieve the precision of 99.2%. By the three novelties and training by data of different conditions, the precisions are improved …
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Cloud-Based Li-ion Battery Anomaly Detection, Localization and ...
3 · 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 …
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Improved internal short circuit detection method for Lithium-Ion ...
Internal short circuit (ISC) has been proven to be responsible for the thermal runaway failure of lithium-ion battery (LIB). The accurate detection of the ISC failure at the early stage is critical to improve the safety of electric vehicles. In this paper, a ISC detection method with self-diagnostic feature is proposed according to the onboard measured load current and terminal voltage. The ...
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Battery Panel Defect Detection Method Based on Deep …
DOI: 10.1109/WCSP.2019.8928008 Corpus ID: 209320042; Battery Panel Defect Detection Method Based on Deep Convolutional Neural Network @article{Jiang2019BatteryPD, title={Battery Panel Defect Detection Method Based on Deep Convolutional Neural Network}, author={Shibao Jiang and Taotao Wang and Shengli Zhang and Wei Wang and Hui Wang}, …
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An exhaustive review of battery faults and diagnostic techniques …
The proposed method can efficiently and accurately detect internal short-circuit faults and has great potential for application in fault diagnosis of large energy storage battery …
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Fault Diagnosis and Detection for Battery System in Real-World …
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. Specifically, the battery fault features are extracted from the incremental capacity (IC) curves, which are smoothed by advanced filter algorithms. Second, principal component analysis ...
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Battery Internal Short Circuit Detection
ISCr detection method for battery pack based on equivalent parameter and consistency method was proposed by the authors'' research team (6). The method can quickly find the early stage ISCr in series circuits by both the nominal parameter change and the true 10.1149/07711.0217ecst ©The Electrochemical Society ECS Transactions, 77 (11) 217-223 (2017) 217. parameter …
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Research progress in fault detection of battery systems: A review
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.
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A Combined Data-Driven and Model-Based Algorithm for …
The purpose of this study was to develop a battery risk diagnosis method based on both model and data-driven approaches, aiming to improve the recognition rate of battery …
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Detection Method of Lithium Plating of Lithium-Ion Battery …
Detection Method of Lithium Plating of Lithium-Ion Battery Based on Complex Morlet Wavelet Transform. Conference paper; First Online: 09 March 2024; pp 571–578 ; Cite this conference paper; Download book PDF. Download book EPUB. The Proceedings of 2023 International Conference on Wireless Power Transfer (ICWPT2023) (ICWPT 2023) Detection …
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Research progress in fault detection of battery systems: A review
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 …
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Precision-Concentrated Battery Defect Detection Method in Real …
To cope with the issue, a precision-concentrated battery defect detection method crossing different temperatures and vehicle states is constructed. The method only uses sparse and noisy voltage from existing onboard sensors. First, a density-based semi-supervised cluster (DBSSC) method is proposed containing three novelties: the objective ...
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Anomaly Detection Method for Lithium-Ion Battery Cells Based …
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. In this paper, a battery cell anomaly detection …
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Precision-Concentrated Battery Defect Detection Method in Real …
The results show that the method can detect defected batteries 13 days ahead the thermal runaway while achieve the precision of 99.2%. By the three novelties and training …
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A Combined Data-Driven and Model-Based Algorithm for Accurate Battery …
The purpose of this study was to develop a battery risk diagnosis method based on both model and data-driven approaches, aiming to improve the recognition rate of battery abnormal states and the accuracy of thermal runaway warning. The core of the paper lies in combining the advantages of data-driven and model-driven methods, and achieving ...
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Data-Driven Thermal Anomaly Detection in Large Battery Packs
The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online anomaly detection in battery packs that uses real-time voltage and temperature data from multiple Li-ion battery cells. Mean-based residuals are generated for ...
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A Sensor-Fault-Estimation Method for Lithium-Ion …
Descriptor proportional and derivate observer systems are applied for sensor diagnosis, based on electrical and thermal models of lithium-ion batteries, which can realize the real-time estimation of voltage sensor fault, …
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