Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle
An innovative extreme learning machine optimized by genetic algorithm (GA-ELM)-based method is proposed to estimate the current system status, which improves the accuracy and timeliness
Fault Identification and Quantitative Diagnosis
The experimental results show that, a coexisting MSC fault and low-capacity fault in the battery packs could be diagnosed effectively by using the proposed method. Discover the world''s research 20
Lithium ion battery pack power fade fault identification based
DOI: 10.1016/J.JPOWSOUR.2012.09.015 Corpus ID: 110227927; Lithium ion battery pack power fade fault identification based on Shannon entropy in electric vehicles @article{Zheng2013LithiumIB, title={Lithium ion battery pack power fade fault identification based on Shannon entropy in electric vehicles}, author={Yuejiu Zheng and Xuebing Han and
Multi-sensor multi-mode fault diagnosis for lithium-ion battery packs
The results of the fault mode identification for the temperature sensors are shown in Fig. A12 in supplementary material. This work presents a feature-based method for multi-sensor fault diagnosis in lithium-ion battery packs. Fault detection and fault mode identification of sensors within the battery pack are accomplished without the need
电池系统的故障特征以及多故障的诊断与识别
Fault diagnosis technology for battery systems is an important guarantee for safe and long-lasting operation. However, the chemical properties of lithium batteries are special, and the type of failure is difficult to identify, which increases the
Fault Identification and Quantitative Diagnosis Method for
A reasonable threshold considering capacity change characteristics is established to initially identify the fault and for further quantitative diagnosis. The experimental results show that a coexisting MSC fault and low-capacity fault in the battery packs could be diagnosed effectively by using the proposed method.
Efficient Battery Fault Monitoring in Electric Vehicles
The most problem in electric vehicles is the detection of faults in the battery; in this paper we discuss a systematic data process for detecting and diagnosing faults in the battery and the
An Intelligent Fault Diagnosis Method for Lithium Battery
Using the battery thermal model and the equivalent circuit model (ECM), Dey. S. et al. [25, 26] proposed an identification scheme based on the Luenberger observer to detect and isolate three kinds of thermal faults: internal thermal resistance fault, convective cooling resistance fault, and thermal runaway. The cell surface temperature feature can represent the
Internal short circuit detection for lithium-ion battery pack with
The experimental results show that a coexisting MSC fault and low-capacity fault in the battery packs could be diagnosed effectively by using the proposed fault identification method based on capacity estimation. Expand. 38. 1 Excerpt; Save.
Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle
According to the uctuation degree of the collected signal, the working state of the battery module presents four situations, namely: good condition (I), the primary fault condition (II
SOC estimation and fault identification strategy of energy
In addition, based on the accurate identification of SOC, the short‐circuit fault diagnosis results of the battery PACK have a high accuracy, confirming the feasibility and effectiveness of the
(PDF) Fault Identification of Lithium-Ion Battery Pack
Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network. Battery fault diagnosis is crucial for stable, reliable, and safe operation of
Fault Diagnosis Method for Lithium-Ion
First, a robust locally weighted regression data smoothing method is proposed that can effectively remove noisy data and retain fault characteristics. Second, an ordinary-least
Review of Abnormality Detection and Fault Diagnosis Methods
Studies of fault mechanisms of LIB provide the basis for fault identification and diagnosis. Research on LIB safety has begun since the end of the last century [22, 23]. Battery aging and failure happen at different stages of battery life. Lin, T., Chen, Z., Zhou, S.: Voltage-correlation based multi-fault diagnosis of lithium-ion battery
Lithium-ion battery pack equalization based on charging
However, the battery pack capacity in a series-connected module is also charged/discharged in the limitation of each single cell''s terminal voltage for the safety issue of a single cell [26]. Thus, each cell capacity has great influence on battery pack capacity. aging mechanism identification. J Power Sources, 251 (2014), pp. 38-54. View
Recent advances in model-based fault diagnosis for lithium-ion
Fault identification is mainly achieved through establishing redundancy, which consists of hardware redundancy and analytical redundancy. To analyze the fault detectability and isolability, structural analysis is performed. 7.2.1. As mentioned earlier, battery packs can be categorized into series-connected packs and parallel-connected packs
SOC estimation and fault identification strategy of energy
In addition, based on the accurate identification of SOC, the short‐circuit fault diagnosis results of the battery PACK have a high accuracy, confirming the feasibility and effectiveness of the designed strategy that includes SOC estimation and short‐circuit fault identification and positioning, and has broad application prospects.
Early micro-short circuit fault diagnosis of lithium battery pack
According to the battery difference model, Gao et al. [23] used the extended Kalman filter algorithm to estimate the SOC of battery pack, and then calculated the difference between it and the average SOC, to realize the battery pack fault diagnosis. However, the battery is a complex and highly nonlinear time-varying system, and the aging of the
Multi-fault detection and diagnosis method for battery packs
The statistical analysis method sets detection thresholds based on the battery operating data, and captures fault characteristics by analyzing abnormal changes in battery
Fault detection method for electric vehicle battery pack based on
To address this challenge, this paper proposes a fault diagnosis method for lithium-ion batteries in electric vehicles that utilizes real-world operational data. Initially, a
Fault Identification and Quantitative Diagnosis Method for Series
The experimental results show that a coexisting MSC fault and low-capacity fault in the battery packs could be diagnosed effectively by using the proposed fault identification method based on capacity estimation. Internal short circuit is considered as one of the general causes that may lead to battery thermal runaway. The capacity of cells ages with the effect of
SOC estimation and fault identification strategy of energy
In addition, based on the accurate identification of SOC, the short-circuit fault diagnosis results of the battery PACK have a high accuracy, confirming the feasibility and effectiveness of the designed strategy that includes SOC estimation and short-circuit fault identification and positioning, and has broad application prospects.
Lithium ion battery pack power fade fault identification based
The thermal runaway of the battery pack could thus occur without fault diagnosis or prognosis. Consequently, to detect cell resistance inconsistencies in the battery pack, it is necessary to achieve fault diagnosis. As stated in Section 2.2, cell resistance fault identification is challenging due to the similar behaviors of the internal and
Fault Identification and Quantitative Diagnosis Method for Series
A fault identification method based on capacity estimation is proposed to distinguish MSC and low-capacity cells in the paper. capacity fault in the battery packs could be diagnosed
Enhancing battery durable operation: Multi-fault diagnosis and
Frequent failure abuse for cells as well as unbalanced initial cell capacity in the battery pack can result in the CA fault. Nevertheless, motivated by the confusing external properties and similar evolutionary progress of these faults, this paper aims to enhance the safety risk early-warning capability of battery systems considering these two
Fault Diagnosis and Abnormality Detection of Lithium-ion Battery
This theoretical study with practical implications shows the promising research direction of combining data mining technologies with machine learning methods for fault diagnosis and
Multi-fault detection and diagnosis method for battery packs
Lithium ion battery pack power fade fault identification based on Shannon entropy in electric vehicles. J Power Sources, 223 (2013), pp. 136-146. View PDF View article View in Scopus Google Scholar [17] M. Ma, Q. Duan, C. Zhao, Q. Wang, J. Sun.
Anti‐interference lithium‐ion battery intelligent perception for
After that, stage one realises the identification and localisation of the faulty cells in the lithium-ion battery pack. Subsequently, the thermal fault battery cell labels, confidence scores, bbox, and fine mask thermal fault area are output after the coarse fault area has been corrected in stage two. 6.3 Training results
Efficient battery fault monitoring in electric vehicles: Advancing
Battery fault monitoring relies on fault-sensitive data gathered by sensors, such as voltage and temperature, because abnormal changes in voltage and temperature are typical signs of fault [6].Those fault-sensitive data are analyzed using diagnostic methods to determine the presence of anomalies, pinpoint their specific locations, and, in some cases, identify the
A New Method of Lithium Battery Insulation Fault Diagnosis
This paper firstly proposes an equivalent model for battery pack insulation fault diagnosis based on the signal injection method; then uses a double Kalman filter algorithm to identify the model parameters to improve the identification accuracy, and at the same time makes an estimate of the end voltage and charge state; finally, the lithium battery pack is tested and
Fault Identification of Lithium-Ion Battery Pack for Electric
The battery system is one of the core technologies of the new energy electric vehicle, so the frequent occurrence of safety accidents seriously limits the large-scale promotion and application. An innovative extreme learning machine optimized by genetic algorithm (GA-ELM)-based method is proposed to estimate the current system status, which improves the accuracy and timeliness
Voltage-fault diagnosis for battery pack in electric vehicles using
Signal analysis-based method: The signal analysis-based method focuses on analyzing the battery voltage signals directly, including extracting the correlation between voltages, curves analysis, etc. By delving into these signals, features related to the cell fault can be identified. The method mainly collects voltage signals and compares them with a certain
[Power Sources-2013] Lithium ion battery pack power
[논문 요약] Lithium ion battery pack power fade fault identification based on Shannon entropy in electric vehicles . 추정한 내부저항을 Shannon Entropy를 통해 진단하는 Minggao 교수님 연구실에서 나온 논문입니다.
Frontiers | A Fault Diagnosis Method for Lithium-Ion
This paper presents a fault diagnosis method for the electric vehicle power battery using the improved radial basis function (RBF) neural network. First, the fault information of lithium-ion battery packs was collected
Advanced data-driven fault diagnosis in lithium-ion battery
The goal of battery fault diagnosis in BMS is to achieve rapid and precise detection, separation, and identification of faults while implementing fault-tolerant control
6 FAQs about [Battery Pack Fault Identification]
How to identify a faulty battery pack?
By analyzing the abnormalities hidden beneath the external measurement and calcg. the fault frequency of each cell in pack, the proposed algorithm can identify the faulty type and locate the faulty cell in a timely manner. Exptl. results validate that the proposed method can accurately diagnose faults and monitor the status of battery packs.
What is the fault diagnosis voltage for a battery pack?
For the upper-limit voltage of the battery pack, the fault diagnosis voltage was 410 V when the actual voltage of the battery pack recorded by the sensor was 450 V. The fault level for this condition is denoted No. I.
What is the fault diagnosis method for electric vehicle power battery?
This paper presented a fault diagnosis method for the electric vehicle power battery using the improved RBF neural networks. Six parameters of the lithium iron phosphate battery pack were selected as the variables, and the fault levels were selected as the target. The CAN bus was used to collect all the experimental data.
How to identify a fault in a reconfigurable battery system?
To effective and accurate identification of failures for the battery, Schmid et al. (2021) developed a fault diagnosis method by using the fuzzy clustering algorithm. In this algorithm, the switches of reconfigurable battery system were used to isolate the fault of the electric vehicles.
What is a battery internal fault diagnosis method?
A battery internal fault diagnosis method was developed using the relationship of residuals, which can reliably detect various faults inside lithium-ion batteries. (23) However, the method requires a large amount of historical fault data for rule building and fewer fault data in actual operation.
Is there a fault warning algorithm for electric vehicle lithium-ion battery packs?
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.