Fault diagnosis method for lithium-ion batteries in electric vehicles based on isolated forest algorithm

Jiang, Jiuchun; Li, Taiyu; Chang, Chun; Yang, Chen Wei; Liao, Li

doi:10.1016/j.est.2022.104177

Cited by 63 publications

(19 citation statements)

References 28 publications

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“…Zhou et al introduced time series data mining technology into spacecraft telemetry data analysis and processing, system state feature extraction, fault diagnosis and identification, to promote the development of spacecraft fault diagnosis technology, improve the reliability and safety of satellite orbit operation, and extend the service life of satellite, which has great significance, which also proves that data mining in the field of spacecraft fault diagnosis has broad application prospects [14]. For association rule algorithm, Jiang et al combined with the fuzzy clustering method and made it possible that association rule algorithm can not only mine Boolean attribute rules but can also be extended to the field of mining numerical properties [15]. For the decision tree algorithm, an improved algorithm combined with the ant colony algorithm is proposed to fundamentally improve the efficiency of the decision tree.…”

Section: Literature Reviewmentioning

confidence: 99%

State Diagnosis and Monitoring Method of Robot Electric Power Equipment Based on Data Mining

Guo¹,

Meng²,

Yu³

et al. 2022

International Transactions on Electrical Energy Systems

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In recent years, with the continuous improvement of system networking, the use of robot electric power equipment is becoming more and more extensive, so it is very necessary to diagnose and monitor its status. Therefore, a diagnosis and monitoring system based on the data mining algorithm is constructed. This study mainly uses the gray prediction algorithm and discrete prediction algorithm; the results showed that after the combination of gray prediction has a certain degree of increase, through the analysis, we can draw the reason for this is that gray prediction algorithm to check the failure data is no longer as input data in the detection of outliers, thereby reducing the noise of the data set, so that the perfomance of oulier detection algorithm can ahieve a great progress. In terms of the running time of outlier detection, the running time of outlier detection using K-Mean algorithm and DBSCAN algorithm increases to a certain extent because the algorithm combines gray prediction and increases the algorithm process.

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Section: Literature Reviewmentioning

confidence: 99%

State Diagnosis and Monitoring Method of Robot Electric Power Equipment Based on Data Mining

Guo¹,

Meng²,

Yu³

et al. 2022

International Transactions on Electrical Energy Systems

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“…Isolated forest is an unsupervised learning algorithm whose core principle is to detect outliers by constructing random forests [10].In the training process of isolated forest, each isolated tree randomly selects part of the sample, and different from KMeans, DBSCAN and other algorithms, isolated forest does not need to calculate the indicators related to distance and density, which can greatly improve the speed and reduce the system overhead.Because each tree is generated independently, it can be deployed on large-scale distributed systems to speed up computation.…”

Section: Isolation Forest Algorithmmentioning

confidence: 99%

Anomaly detection and identification of power consumption data based on LOF and isolation forest

Han,

Gao,

Zheng

2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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With the rapid development of the energy Internet, the power industry has ushered in the era of power big data. Through dynamic monitoring and analysis of all kinds of data in power grid operation, potential risks can be found effectively and timely, and the safety of power grid operation can be improved. As an important part of power big data, user-side power consumption data provides strong support for managers to visually display users' abnormal power consumption behaviors. It provides a theoretical basis for better and faster solution to the behavior of electricity leakage due to equipment failure or personal behavior. Based on this background, this paper first uses k-means clustering algorithm to classify electricity consumption data according to user usage habits. This method can effectively avoid misjudgment and missing judgment of electricity data. Finally, the method of combining LOF and isolation forest is used to realize the anomaly detection of user side power consumption data. By comparing the single algorithm (LOF and isolation forest), it is proved that the anomaly monitoring model combined with the two algorithms can better identify the abnormal power consumption data at the user side.

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“…[ 235 ] Liao and co‐workers propose a fault diagnosis method for power lithium batteries based on isolated forest algorithm. [ 236 ] The variational mode decomposition algorithm is used to process the voltage data collected by BMS. Static components (highly correlated with aging state inconsistency) and dynamic components (mainly reflecting anomaly information) are decoupled.…”

Section: Multiscale Model Applicationmentioning

confidence: 99%

Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle

Liu

Zhang

et al. 2022

Advanced Energy Materials

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The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/aenm.202200889. realize carbon-neutral growth have been put forward by countries all over the world, e.g., China aims to cut CO 2 emissions net-zero by 2060. [1] A promising means to reduce the dependence of our societies on fossil fuels is the development of advanced energy materials. [2] Lithium-ion batteries (LIBs) show great potential as high performance energy storage devices for consumer electronics, electrified transportation, and grid-level energy storage systems because of their inherent advantages, such as high energy density, high working voltage, low self-discharge rate, and long cycle stability, over other traditional battery systems (e.g., lead-acid battery, nickel-metal hydride battery). [3] Despite impressive progress in LIB technologies since the first invention of commercial LIBs in 1992, [4] further expansion and large-scale application of LIBs are currently hindered by limited fast charging ability, relatively low energy density, safety concerns under thermal/mechanical/electrical abuse conditions, capacity decay, and cycle stability. [4,5] Government around the world has set ambitious targets to promote more practical and higher performance batteries technology, such as the American "Battery 500 project" (500 Wh kg −1 in 2021), Chinese "Made in China 2025" (400 Wh kg −1 in 2025), Japanese "RISING II" (500 Wh kg −1 in 2030). [6] Achieving higher energy density has been a priority in recent LIB research to realize longer

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Fault diagnosis method for lithium-ion batteries in electric vehicles based on isolated forest algorithm

Cited by 63 publications

References 28 publications

State Diagnosis and Monitoring Method of Robot Electric Power Equipment Based on Data Mining

State Diagnosis and Monitoring Method of Robot Electric Power Equipment Based on Data Mining

Anomaly detection and identification of power consumption data based on LOF and isolation forest

Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle

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