Internal Short Circuit Faults Research Articles

A battery internal short circuit fault diagnosis method based on incremental capacity curves

The safe operation of battery energy storage systems (BESSs) has become one of the research priorities in this industry. And it is usually threated by various faults caused by design flaws, environmental conditions, and operating conditions et al. Among these faults, the internal short circuit (ISC) faults pose a significant threat to the safety of BESSs. Relevant studies focus on ISC fault diagnosis itself and ignore the impact of battery aging within the pack on fault diagnosis. To solve this problem, this paper proposes an ISC fault diagnosis method based on incremental capacity (IC) curves. And a qualitative differentiation between ISC batteries and aging ones is first achieved by leveraging the characteristic variations of IC curves. Then, an equivalent circuit model is constructed for ISC batteries. Further, a joint estimation of ISC resistance and SOC of the faulty battery is performed by combining Extended Kalman Filtering (EKF) and Forgetting Factor Recursive Least Squares (FFRLS). Finally, an experimental platform is established to verify the proposed method. Results show the proposed method can effectively differentiate between ISC batteries and aging batteries. Moreover, the estimation errors of SOC are less than 0.26% and the estimation accuracy of ISC resistance is more than 99.42%.

A fault diagnosis method of battery internal short circuit based on multi-feature recognition

The internal short circuit (ISC) fault has been considered as one of the most serious problems, which may pose a threat to the operation safety of the battery system. To solve this problem, this paper proposes an ISC fault diagnosis method based on multi-feature recognition to distinguish aging and ISC fault. The ISC equivalent circuit model is established first. In addition, three characteristic parameters, including the slope of the “rebound” voltage curve, the “valley” ordinate in the differential voltage (DV) curve, and the electric quantity, namely high segment charging capacity (HSCC) between the valley point of the DV curve and the end of charging position, are extracted to distinguish the ISC battery from aging battery. The results show that the proposed method can effectively distinguish between ISC batteries, aging batteries, and normal batteries. Moreover, the ISC resistance is able to be estimated accurately, with an error of less than 5.44%.

Internal short circuit fault diagnosis for the lithium-ion batteries with unknown parameters based on transfer learning optimized residual network by multi-label data processing

In recent years, internal short circuits have frequently been the cause of safety accidents, and traditional diagnosis methods for such problem have limitations that prevent their application under complex working conditions. Furthermore, due to the scarcity of real internal short circuit data, it is difficult to obtain a large amount of real vehicle data, which makes internal short circuit fault diagnosis even more challenging. In this paper, fault batteries with unknown parameters are diagnosed using a residual network based on multi-label processed battery data and transfer learning is utilized to optimize the diagnosis effect for fault localization and fault degree identification, thus increasing the information richness of the training model samples and improving the training effect. Accuracy, recall, and false positive rate are used to evaluate the diagnosis results of the residual network. The results demonstrate that the accuracy rate increased by 11.1% after transfer learning, indicating a significant reduction in misdiagnoses. The proposed fault diagnosis method using residual network and transfer learning has practical significance for enhancing battery fault detection accuracy and efficiency. Especially in areas such as electric vehicles and energy storage systems, it can provide effective technical support for battery health monitoring and early warning.

Voltage measurement-based recursive adaptive method for internal short circuit fault diagnosis in lithium-ion battery packs

Internal short circuit (ISC) has been identified as a major cause of thermal runaway in lithium-ion (Li-ion) battery systems, making the investigation of ISC fault diagnosis a focal research topic in electric vehicles and battery energy storage systems. Recently, several studies have applied multivariate analysis techniques to analyze abnormal behavior in cell voltage measurements and achieved an accurate diagnosis of ISC faults. However, model parameter variations under different operating conditions and state of charge (SOC), which results in unacceptable false alarm rates, is yet to be fully considered. To effectively address this problem, this study introduces a recursive approach on the basis of principal component analysis (PCA) to update the monitoring model and adapt to parameter changes. Moreover, an adaptive threshold with fusion factors is devised to reduce fault false alarm rates. In particular, this study employs sliding window techniques to amplify weak fault features to tackle the challenge of early-stage detection difficulty for ISC faults and provides an optimal window width determination algorithm based on fault-free data. Experimental results on a real Li-ion battery pack test platform under various operating conditions and SOC levels demonstrate that the proposed method maintains extremely high fault detection rates for ISC faults of different intensities. Furthermore, it is established that the adaptive updating of the model in the absence of faults greatly reduces false alarm rates.

Machine learning for power transformer SFRA based fault detection

This paper presents machine learning methods for health assessment of power transformer based on sweep frequency response analysis. The paper presents an overview of monitoring and diagnostics based on statistical Sweep Frequency Response Analysis (SFRA) based indicators that are used to evaluate the state of the power transformer. Experimental data obtained from power transformers with internal short-circuit faults is used as a database for applying machine learning. Machine learning is implemented to achieve more precise asset management and condition-based maintenance. Unsupervised machine learning was applied through the k-means cluster method for classifying and dividing the examined power transformer state into groups with similar state and probability of failure. Artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) as part of supervised machine learning are created in order to detect fault severity in tested power transformers of different lifetime. The presented machine learning methods can be used to improve health assessment of power transformers.

Simultaneous diagnosis of cell aging and internal short circuit faults in lithium-ion batteries using average leakage interval

Accurate health diagnostics of lithium-ion batteries are indispensable for efficient utilization. A decrease in battery capacity not only diminishes the energy efficiency but also causes several detrimental effects, such as an internal short circuit (ISC) fault; these fault can lead to thermal runaway. However, the simultaneous impact of aging and ISC faults complicates the ability to distinguish between two factors within a singular discharging or charging process. This study focuses on the co-diagnosis of battery capacity and ISC faults, emphasizing that the amount of leakage current attributable to an ISC fault remains consistent at intervals where the average voltage is identical during discharging and charging procedures. To perform this analysis, different equivalent circuit models and parameter tables are employed for the discharging and charging processes separately. Subsequently, the ISC fault and capacity are estimated using the estimated state of charge (SOC) of intervals. The practicality of the proposed method is evaluated through experiments under various dynamic profiles, ISC fault severities, aging degrees, and temperature conditions. The results substantiate the proposed method’s efficacy in co-diagnosing the capacity and ISC faults. Therefore, this method represents an advancement in the ongoing efforts to ensure the safe and effective deployment of lithium-ion batteries.

Internal Short-Circuit Fault Location of Rail Transit Auxiliary Inverter Based on Voltage Deviation

In order to improve the ability of locating and detecting the internal short-circuit fault of rail transit auxiliary inverter, a method of locating the internal short-circuit fault of rail transit auxiliary inverter based on voltage deviation is proposed. The fault sensing information acquisition module of rail transit auxiliary inverter is constructed by using the detection method of fundamental frequency characteristics of differential mode current of excitation winding, and the voltage deviation analysis of rail transit auxiliary inverter under internal short-circuit fault state is realized by using the methods of MVCR excitation winding control and output reactive power adjustment. Combined with the dynamic distribution characteristics of output harmonics and initial phase of rail transit auxiliary inverter, the equivalent compensation and capacity expansion methods of excitation winding circuit are adopted. The signal test model of fault location is established, and the spectral characteristics of the internal short-circuit fault signal of rail transit auxiliary inverter are extracted. A fault joint feature location and monitoring model is established by using the data clustering analysis method of fusion difference, and the internal short-circuit fault location detection of inverter is realized according to the voltage deviation distribution characteristics of inverter in short-circuit state. The test results show that the accuracy of short-circuit fault location in rail transit auxiliary inverter is high, and the dynamic estimation ability of inverter output voltage deviation is strong.

Quantitative diagnosis of micro-short circuit for lithium-ion batteries considering aging based on incremental capacity curve

Early diagnosis of internal short-circuit faults for preventing thermal runaway in lithium-ion batteries stands as a pivotal task within battery management systems. With the continuous use of electric vehicles, the coexistence of aging and micro-short circuit (MSC) within battery packs is possible. These two faults manifest identical characteristics during discharge, rendering them susceptible to erroneous diagnoses. In this study, a novel fault diagnosis method based on the incremental capacity (IC) curves is proposed, devised to discern aging cells from those afflicted by MSC. This method screens out aging cells by feature parameters of IC curve. Based on the unique correspondence between IC peak and state of charge in this study, the cell that reaches the peak first is designated as the reference cell. By calculating the leakage current based on the temporal divergence of peak arrivals of each cell from this reference, this method estimates the MSC resistance to quantify the degree of the short-circuit fault. A series of experiments involving the presence of aging batteries, simultaneous occurrence of multiple MSC faults, or the evolution of MSC severity indicate the method is feasible and effective for diagnosing aging and MSC faults in battery packs simultaneously.

A Stator Internal Short-Circuit Fault Protection Method for Turbo-Generator Based on Instantaneous Power Oscillation Ratio

For large turbo-generators, the longitudinal differential protection can only reflect the stator internal inter-phase short-circuit fault, and cannot reflect the inter-turn short-circuit fault. The protection methods for inter-turn short-circuit faults have the problem of low sensitivity, such as the longitudinal zero-sequence voltage protection and the negative sequence component-based protection. Therefore, a stator internal short-circuit fault protection method for turbo-generator based on instantaneous power oscillation ratio (IPOR) is proposed in this paper. The instantaneous power variation of the generator before and after the fault is analyzed, and the IPOR is calculated by using the balanced power component and the oscillatory power component. Then, the method to judge whether the generator has an internal short-circuit fault based on the amplitude and phase of IPOR is proposed. The experimental results verify the effectiveness of the proposed protection method, which has a high sensitivity to the inter-turn short-circuit faults with a small turn difference. The protection is stable and does not malfunction during the system oscillation. In addition, the action time of the protection method does not exceed 1.5 power frequency cycles under various working conditions, which can meet the speed requirements of generator protection.

An independent component analysis based correlation coefficient method for internal short-circuit fault diagnosis of battery-powered intelligent transportation systems

Battery-powered intelligent transportation systems (BPITS) are growing at an alarming rate, and a problem that cannot be ignored is that the safety of battery packs is currently the weak link in the security of BPITS. However, developing a fast and reliable diagnostic method is not easy due to the large number of battery cells and the difficulty to guarantee consistency among the individual cells. To better resolve this problem, a new fault diagnosis scheme based on voltage correlation coefficient (VCC) and independent component analysis (ICA) model is proposed in this paper. Where VCC is used to extract the fault signature of the battery pack independent of the cell inconsistency; and ICA is used to model the non-Gaussian distributed high-dimensional VCC signals in parallel and derive a statistical-based fault detection index. The kernel density estimation-based algorithm is used to calculate the theoretical threshold and to determine the optimal window width for VCC. In particular, a variable reconstruction-based fault isolation algorithm is proposed for accurate and reliable determination of the location of the faulty cell to prevent the smearing effect. Experimental results conducted on a real battery testbed under different conditions and configurations show that the new method provides significant improvements in both detection latency and positioning reliability in diagnosing internal short-circuit (ISC) faults with different failure grades.

Research on short-circuit fault-diagnosis strategy of lithium-ion battery in an energy-storage system based on voltage cosine similarity

In recent years, accidents such as spontaneous combustion and explosion have frequently occurred in the field of electrochemical energy storage, and thermal runaway caused by short-circuit faults in lithium-ion (Li-ion) batteries is one of the main reasons. This study investigated the internal short circuit (ISC) fault diagnosis method for Li-ion (LiFePO4) batteries in energy storage devices. A short-circuit fault diagnosis method for battery module components based on voltage cosine similarity is proposed based on the characteristics extracted from the ISC fault battery. In this method, the voltage and current of the battery were used to derive a two-dimensional feature vector, and a gain multiple with excitation information was introduced to perform secondary processing on the eigenvectors and achieve effective separation of the fault-signal features. The experimental results show that the proposed method can achieve a second-level rapid diagnosis of short-circuit faults in batteries when the simulated ISC resistance is below 5 Ω, and the excellent fault-detection capabilities for modules with inconsistent states of charge and the performance of the proposed method under dynamic conditions are verified.

Multivariate statistical analysis based cross voltage correlation method for internal short-circuit and sensor faults diagnosis of lithium-ion battery system

Multivariate statistical analysis based cross voltage correlation method for internal short-circuit and sensor faults diagnosis of lithium-ion battery system

A Spatio-Temporal Inference System for Abnormality Detection and Localization of Battery Systems

In this research, a spatio-temporal inference system is proposed to detect and locate thermal abnormalities of battery systems. The proposed spatio-temporal inference system consists of three modules: spatio-temporal processing module, abnormality inference module, and spatial inference module. Based on the distributed temperatures on the battery system, the monitoring statistic can be developed in the spatio-temporal processing module. The abnormality inference module is constructed to detect the abnormality based on the derived statistic index. Then, the spatial Bayes model is designed to estimate the abnormality location. The Bayes risk analysis indicates that the proposed method has a bounded error. Experiments on a lithium-ion (Li-ion) battery cell and a battery pack demonstrate that the proposed spatio-temporal inference system can detect and locate the internal short circuit (ISC) fault before it develops into a thermal runaway.

Internal short circuit fault diagnosis based on 1DVCNN in lithium battery storage system

Because LiFePO4 batteries are widely used in the energy storage system, their safety has received a great deal of research. If an internal short circuit (ISC) in a Li-ion battery energy storage system leads to thermal runaway, it will pose an uncontrollable hazard. Therefore, there is a proposed method in this study that is a one-dimensional voltage-correlated convolutional neural network (1DVCNN) to detect and localize ISC faults in time, which is a combination of feature extraction, correlation analysis, and convolutional neural networks. First, the voltage signal of the LiFePO4 battery collected in the energy storage system is then converted into a Pearson Correlation Coefficient (PCC) by correlation analysis of the data. Specific PCCs are then selected as features of a one-dimensional convolutional neural network (1DCNN) to diagnose faults. Finally, the fault dataset is used to train the model and the results are then validated against the untrained fault data. In this paper, the ISC faults are modeled by both building an actual training platform for lithium batteries and MATLAB simulations to obtain the dataset used to train the proposed model. By comparing the accuracy of the training results of the models using different features, it is found that 1DVCNN is able to detect the ISC fault in the energy storage system in a more timely and effective manner than in other studies.

An Early Soft Internal Short-Circuit Fault Diagnosis Method for Lithium-Ion Battery Packs in Electric Vehicles

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 features are extracted from the data, making them easier to identify than small voltage differences. Then, the local outlier factor method is proposed to detect the early soft internal short-circuit fault by calculating the local outlier factor value of each cell within the battery pack. Furthermore, soft short-circuit simulations of a series-connected battery pack under different conditions and various short-circuit resistance values are conducted to generate an internal short-circuit fault data set. Finally, the validity of the proposed fault diagnosis method is verified using simulation and real-world vehicle data. The results show that the proposed method can effectively identify the short-circuit fault of the battery at the early stage, accurately locate the faulty cells in the battery pack, and describe the severity of the fault.

Identification of Internal Short-Circuit Faults in Lithium-Ion Batteries Based on a Multi-Machine Learning Fusion

Internal short-circuit (ISC) faults are a common cause of thermal runaway in lithium-ion batteries (LIBs), which greatly endangers the safety of LIBs. Different LIBs have common features related to ISC faults. Due to the insufficient volume of acquired ISC fault data, conventional machine learning models could not effectively identify ISC faults. To compensate for the above deficiencies, this paper proposes a multi-machine learning fusion method to predict ISC faults and to perform faults warning classification under multiple operating conditions using the input of voltage normalization. Firstly, learning data acquisition is captured by experiments and simulation. Secondly, the simulation data are inputted into the ResNet-convolutional neural network (CNN) for pretraining, followed by the transfer learning method to freeze parts of the model layers in the CNN, and part of the experimental data are also inputted into the CNN model for parameter fine-tuning to build a multi-machine learning model. Finally, the degree of ISC faults within the laboratory battery is predicted based on the multi-machine learning model. The results show that the CNN model had a 99.9% prediction accuracy on the simulated dataset, and the multi-machine learning fusion model after transfer learning had a 96.67% prediction accuracy on the laboratory battery dataset, which can accurately identify different levels of ISC faults in batteries and realize the graded warning of ISC faults.

A fault detection method of electric vehicle battery through Hausdorff distance and modified Z-score for real-world data

Conventional fault diagnosis methods are tough to detect early faults when the abnormal characteristics of the battery are not obvious. The main purpose of this manuscript is to propose an online fault detection method for lithium-ion battery pack based on the combination of Hausdorff distance and modified Z-score. It enables the detection and location of the internal short circuit fault of the battery pack by detecting the Hausdorff distance between the voltage curve of each cell and the median voltage curve in a moving window. The validity of the algorithm is further verified by utilizing real-world data and compared with threshold method, Pearson correlation coefficient method, and Shannon entropy weighting method. The results show that the proposed method not only possesses higher reliability than these above but also does well without any model. In addition, the proposed method owns good robustness to the battery system with poor consistency and can be applied online.

Pressure changes of nitrogen blanket transformers under internal short circuit faults

The root cause of the transformer fire is an oil tank crack caused by an internal short circuit fault. The gas layer of the nitrogen blanket transformer helps buffer the pressure change caused by the internal short-circuit fault. Moreover, the pressure change simulation is the basis for the configuration of non-electrical protection. This paper analyzes the relationship between the energy and pressure source of the internal short-circuit fault. Based on finite element simulation, the internal pressure transfer model of the transformer is built, and the pressure of the oil tank is obtained. By comparing with the pressure of conventional transformer, the excellent explosion-proof performance of nitrogen blanket transformer is verified.

Early stage internal short circuit fault diagnosis for lithium-ion batteries based on local-outlier detection

Internal short circuit (ISC) is considered to be one of the main causes of battery thermal runaway, which is a critical obstacle to the application of lithium-ion batteries for energy storage. Aiming at inconspicuous characteristics and slow detection speed of early stage ISC faults, this paper proposes a fast diagnostic method for ISC based on local-gravitation outlier detection. In the serial battery module, the cell terminal voltages are normalized to characterize voltage trends that are more sensitive to faults than voltage magnitudes, which improves the speed of fault diagnosis. The normalized voltages are then evaluated by a local gravity outlier detection algorithm to detect faults, by which the anomalies caused by the faults are amplified to achieve the ability to diagnose early ISC faults. The performance of the method is validated under the Urban Dynamometer Driving Schedule test, where several sets of experimental results for ISC faults of varying severity showed that the proposed method could detect them accurately and rapidly even when the fault characteristics are not obvious.

A Waveform-Based Approach for Transformer Differential Protection Under GIC Conditions

Harmonic blocking (HB)-based differential relays, generally employed in the transformer protection, are likely to fail to interrupt internal short-circuit faults that occur under geomagnetically induced current (GIC) conditions. This paper proposes a waveform-based approach that enhances the differential protection dependability under the GIC conditions by detecting the GIC and overriding the HB signals for the internal faults. A novel algorithm is developed for the GIC detection, which employs three-phase instantaneous differential current waveforms and recognizes a GIC condition if the waveforms are all asymmetric in one direction. The proposed waveform-based approach also utilizes trip request signals of the differential relay's fault detection module to unblock the relay for the internal faults during the GIC. The results show that the proposed waveform-based approach (i) can successfully unblock the differential protection for the internal faults under the GIC conditions, (ii) does not malfunction for the external faults during the GIC, (iii) discriminates transformer energization cases from the GIC conditions, and (iv) is immune to the measurement noise. The current transformer (CT) saturation is also considered in the studies. The proposed approach's effectiveness is verified for different transformer types, including three-phase and single-phase transformers.