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 large time delay for LiB
Lithium-ion battery packs are widely deployed as power sources in transportation electrification solutions. To ensure safe and reliable operation of battery packs, it is of critical importance to monitor operation status and diagnose the running faults in a timely manner. This study investigates a novel fault diagnosis and abnormality detection
Liu et al. [160] applied the structural analysis theory for a battery pack to detect and isolate the various sensor faults and cooling system faults. A comparison is performed between the hardware redundancy and analytical redundancy-based fault identification methods in terms of practicability and functionality, which is listed in Table 9 .
Timely and accurate fault diagnosis for a lithium-ion battery pack is critical to ensure its safety. However, the early fault of a battery pack is difficult to detect because of its unobvious fault effect and nonlinear time
The structural flow of the multi-fault diagnosis method for lithium-ion battery packs is shown in Fig. 4. The local weighted Manhattan distance is used to measure and locate the faulty cells within the lithium-ion battery pack, and the type of fault is determined by the combined analysis of voltage ratio and temperature. The multi-faults in the
Experimental results validate that the proposed method can accurately diagnose faults and monitor the status of battery packs. This theoretical study with practical implications shows the
Liu et al. [160] applied the structural analysis theory for a battery pack to detect and isolate the various sensor faults and cooling system faults. A comparison is performed between the
Semantic Scholar extracted view of "Fault detection of lithium-ion battery packs with a graph-based method" by Guijun Ma et al. Skip to search form Skip to main content Skip to account menu. Semantic Scholar''s Logo . Search 223,126,953 papers from all fields of science. Search. Sign In Create Free Account. DOI: 10.1016/j.est.2021.103209; Corpus ID:
Various failures of lithium-ion batteries threaten the safety and performance of the battery system. Due to the insignificant anomalies and the nonlinear time-varying properties of the cell, current methods for identifying the diverse faults in battery packs suffer from low accuracy and an inability to precisely determine the type of fault, a method has been proposed that
Experimental results validate that the proposed method can accurately diagnose faults and monitor the status of battery packs. This theoretical study with practical implications shows the
To tackle the issues described above, this work focuses on three LiB pack faults (i.e., sensor fault, connection fault and ESC fault), and proposes a graph-based method to
Abstract: Lithium-ion battery packs are widely deployed as power sources in transportation electrification solutions. To ensure safe and reliable operation of battery packs, it is of critical
Lithium-ion battery aging macro performance is manifested as the reduction of battery pack performance, the reduction of vehicle mileage, the rapid decline in power, the abnormal temperature during charging and discharging, and the battery drum. The main macro factors affecting battery aging are the following four aspects: 1.
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
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...
In this paper, an initial microfault diagnosis method is proposed for the data of electric vehicles in actual operation. First, a robust locally weighted regression data smoothing method is proposed that can effectively remove
The early detection of soft short-circuit (SC) faults in lithium-ion battery packs is critical to enhance electric vehicle safety and prevent catastrophic hazards. This article proposes a battery fault diagnosis method that achieves joint soft SC fault detection and estimation.
Lithium-ion (Li-ion) batteries have been widely used in a wide range of applications such as portable electronics, vehicles, and energy storage, thanks to their high energy density, long lifespan, low self-discharging rate, and wide temperature range [1], [2].However, the internal short circuit (ISC) in Li-ion batteries, commonly regarded as the main
One of the main obstacles for the reliability and safety of a lithium-ion battery pack is the difficulty in guaranteeing its capacity consistency at harsh operating conditions, while the key solution is accurate detection of cell capacity inconsistency within the battery pack without taking it apart for destructive testing. Here, an in situ and nondestructive technology is
Timely and accurate fault diagnosis for a lithium-ion battery pack is critical to ensure its safety. However, the early fault of a battery pack is difficult to detect because of its unobvious fault effect and nonlinear time-varying characteristics.
Various failures of lithium-ion batteries threaten the safety and performance of the battery system. Due to the insignificant anomalies and the nonlinear time-varying
In this paper, a battery cell anomaly detection method is proposed based on time series decomposition and an improved Manhattan distance algorithm for actual operating data of electric vehicles.
To tackle the issues described above, this work focuses on three LiB pack faults (i.e., sensor fault, connection fault and ESC fault), and proposes a graph-based method to locate the anomaly voltage sensors and detect the fault types of LiB packs. More specifically, the graph-based method takes advantages of deep autoencoder and the structure
The early detection of soft short-circuit (SC) faults in lithium-ion battery packs is critical to enhance electric vehicle safety and prevent catastrophic hazards. This article
In Ref. , a lithium-ion battery fault diagnosis system suitable for high-power scenarios is designed, and it can evaluate the degradation of lithium-ion batteries and conduct diagnosis with the full knowledge of internal fault mechanism. Ref.
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.
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.
State-of-health (SOH) monitoring of lithium-ion batteries plays a key role in the reliable and safe operation of battery systems. Influenced by multiple factors, SOH is an aging path-dependent parameter, which challenges its accurate estn. and prediction.
By applying the designed coefficient, the systematic faults of battery pack and possible abnormal state can be timely diagnosed. 2) The t-SNE technique, The K-means clustering and Z-score methods are exploited to detect and accurately locate the abnormal cell voltage.
Xion g et al. proposed a rule-based detection method for the over-discharged L i-ion batteries. Based upon the respectively, and fa ilure detection and earl y warning are directly given by a Boolean e xpression. However, the appropriate fi xed or time -varying thresholds in the rules are not easy to be determined in real applications.
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