Abstract: Fast and accurate battery system fault diagnosis is essential to ensure electric vehicles'' safe and reliable operation. This paper proposes an online multi-fault detection and isolation …
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 detection method of battery parameters in battery management system is simple and the accuracy is limited [, , ], but the accuracy of parameters is the direct factor affecting the fault diagnosis results. Wang et al. proposed a model-based insulation fault diagnosis method based on signal injection topology.
Based on the features, a cluster algorithm is employed to capture the battery potential failure information. Moreover, the cumulative root-mean-square deviation is introduced to quantificationally analyze the degree of the battery failures using large-scale battery data to avoid the missing fault reports using short-term data.
Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis Simultaneous fault isolation and estimation of lithium-ion batteries via synthesized design of Luenberger and learning observers
In this paper, it is shown that, various faults, including battery short and open circuit, sensor biases, input voltage drop, and semi-conductor switches (such as MOSFETs) short and open circuit, can be detected and isolated by using the magnitude and slope of a residual signal or its norm that is generated from the battery voltage.
As electric vehicles advance in electrification and intelligence, the diagnostic approach for battery faults is transitioning from individual battery cell analysis to comprehensive assessment of the entire battery system. This shift involves integrating multidimensional data to effectively identify and predict faults.
Abstract: Fast and accurate battery system fault diagnosis is essential to ensure electric vehicles'' safe and reliable operation. This paper proposes an online multi-fault detection and isolation …
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 cell groups and evaluated using …
Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in …
ject detection-based solutions, corner detectors and cout-ing methods with our segmentation-based MDCNet. We directly visualize the predicted results (MDCNet: Segmen-tation map, …
Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage...
Anomaly Detection Method for Lithium-Ion Battery Cells Based on Time Series Decomposition and Improved Manhattan Distance Algorithm Minghu Wu, Shufan Zhang, Fan Zhang,* Rui Sun, Jing Tang, and Sheng Hu Cite This: ACS Omega 2024, 9, 2409−2421 Read Online ACCESS Metrics & More Article Recommendations ABSTRACT: Abnormalities in …
To address this issue, this study utilizes the Whale Optimization Algorithm to improve the Long Short-Term Memory algorithm and constructs a fault diagnosis model based …
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, …
Short circuit (SC) is a stumbling block to battery safety. The common battery management system (BMS) holding the fixed threshold focuses overly on the absolute magnitude of battery voltage, and therefore cannot detect the early SC. This paper proposes an online method for detecting SC based on principal component analysis (PCA), which possesses an adaptive threshold. First, …
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 ...
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.
The model-based method uses a mathematical model of lithium-ion batteries to compute the residual between measured values and model outputs. By detecting and analyzing this …
In this paper, two methods of residual-based fault detection and isolation, by using historical data and observer based technique, were proposed for battery chargers power …
Overall, our method excels in ablation experiments, achieving optimal results in AUC, F1-Score, ACC, and accuracy. It demonstrates robustness and superiority in electric vehicle battery anomaly detection, making it ideal for practical applications in power battery anomaly detection.
Fig. 13 (a) exhibits the sampled battery voltage during the FUDS test with a 100 Hz sampling frequency, Fig. 13 (b) gives the battery SOC variation during the test, and Fig. 13 (c) shows the performance of the proposed detection method (namely the response of the detection output V d). We can see battery voltage fluctuates significantly and decreases from 4.28 V to …
To address this issue, this study utilizes the Whale Optimization Algorithm to improve the Long Short-Term Memory algorithm and constructs a fault diagnosis model based on the improved algorithm. The purpose of using this model for fault diagnosis of power batteries is to strengthen the safety management of batteries.
This paper proposed a novel abnormality detection method based on an Autoencoder with IAE. The proposed method belongs to unsupervised learning, where fault diagnosis of EVs can be realized after model establishment with normal samples. IAE is used to generate reconstructed signal so that RMSE can be calculated with knowledge of original signal.
A total of 92 battery packs in series to form a battery module to increase the operating voltage of the battery system, and a total of 2208 lithium-ion single cells first in parallel and then in series to achieve high voltage and high capacity standards to have sufficient output power drive the car operation. The system adopts a distributed ...
We conduct a comprehensive study on a new task named power battery detection (PBD), which aims to localize the dense cathode and anode plates endpoints from X-ray images to evaluate the quality of power batteries.
Jiang et al. [87] proposed a fault diagnosis method for power lithium batteries based on isolated forest algorithm. First, the original voltage data is processed and …
This paper proposed a novel abnormality detection method based on an Autoencoder with IAE. The proposed method belongs to unsupervised learning, where fault …
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 …
In this paper, two methods of residual-based fault detection and isolation, by using historical data and observer based technique, were proposed for battery chargers power electronics. The application of the proposed methods was tested on constant-current constant-voltage battery chargers, with both Buck and Boost power converters. The ...
Jiang et al. [87] proposed a fault diagnosis method for power lithium batteries based on isolated forest algorithm. First, the original voltage data is processed and decomposed into static components that are highly correlated with aging inconsistencies and dynamic components that reflect abnormal information. Then, the characteristic ...
Based on the findings, the models of the internal short circuits were built in Refs. [33], [34], which can be utilized to detect the fault by comparing the virtue model output/state with the battery output/state. If the residue is above a threshold, a fault is flagged. A thorough introduction of multiple model based cell condition monitoring ...
ject detection-based solutions, corner detectors and cout-ing methods with our segmentation-based MDCNet. We directly visualize the predicted results (MDCNet: Segmen-tation map, Others: Bounding box, Corner map, Density
Abstract: Fast and accurate battery system fault diagnosis is essential to ensure electric vehicles'' safe and reliable operation. This paper proposes an online multi-fault detection and isolation method for battery systems by combining improved model-based and signal-processing methods, which eliminates the limitation of interleaved voltage ...
Therefore, in Ref. [32], an arc detection method based on morphological filters is proposed in battery ESSs. In this method, M. Kavi et al. identify DC arc faults based on the output spikes and consider the health status, charging status, and temperature measurements from BMS. Additionally, an adaptive threshold classifier is employed, which ...
The model-based method uses a mathematical model of lithium-ion batteries to compute the residual between measured values and model outputs. By detecting and analyzing this residual, the method can identify the existence, type, and location of faults. Given the inherent nonlinearity and uncertainty of battery systems, sliding mode strategies and their variants have been …
China is at the forefront of the global solar energy market, offering some of the highest quality solar panels available today. With cutting-edge technology, superior craftsmanship, and competitive pricing, Chinese solar panels provide exceptional efficiency, long-lasting performance, and reliability for residential, commercial, and industrial applications. Whether you're looking to reduce energy costs or contribute to a sustainable future, China's solar panels offer an eco-friendly solution that delivers both power and savings.