Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features
Accurately identifying a specific faulty monomer in a battery pack in the early stages of battery failure is essential to preventing safety accidents and minimizing property damage. While there are existing lithium-ion power battery fault diagnosis methods used in laboratory settings, their effectiv...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/7/1568 |
| _version_ | 1797212615390265344 |
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| author | Fan Zhang Xiao Zheng Zixuan Xing Minghu Wu |
| author_facet | Fan Zhang Xiao Zheng Zixuan Xing Minghu Wu |
| author_sort | Fan Zhang |
| collection | DOAJ |
| description | Accurately identifying a specific faulty monomer in a battery pack in the early stages of battery failure is essential to preventing safety accidents and minimizing property damage. While there are existing lithium-ion power battery fault diagnosis methods used in laboratory settings, their effectiveness in real-world vehicle conditions is limited. To address this, fault diagnosis methods for real-vehicle conditions should incorporate fault characteristic parameters based on external battery fault characterization, enabling the accurate identification of different fault types. However, these methods are constrained when confronted with complex fault types. To overcome these limitations, this paper proposes a battery fault diagnosis method that combines multidimensional fault features. By merging different fault feature parameters and mapping them to a high-dimensional space, the method utilizes a local outlier factor (LOF) algorithm to detect anomalous values, enabling fault diagnosis in complex working conditions. This method improves the detection time by an average of 22 min compared to the extended RMSE method and maintains strong robustness while correctly detecting faults compared to other conventional methods. |
| first_indexed | 2024-04-24T10:45:12Z |
| format | Article |
| id | doaj.art-17deff568cd4415d9cd250f8b0917796 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-24T10:45:12Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-17deff568cd4415d9cd250f8b09177962024-04-12T13:17:45ZengMDPI AGEnergies1996-10732024-03-01177156810.3390/en17071568Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault FeaturesFan Zhang0Xiao Zheng1Zixuan Xing2Minghu Wu3Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, ChinaHubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, ChinaHubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, ChinaHubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, ChinaAccurately identifying a specific faulty monomer in a battery pack in the early stages of battery failure is essential to preventing safety accidents and minimizing property damage. While there are existing lithium-ion power battery fault diagnosis methods used in laboratory settings, their effectiveness in real-world vehicle conditions is limited. To address this, fault diagnosis methods for real-vehicle conditions should incorporate fault characteristic parameters based on external battery fault characterization, enabling the accurate identification of different fault types. However, these methods are constrained when confronted with complex fault types. To overcome these limitations, this paper proposes a battery fault diagnosis method that combines multidimensional fault features. By merging different fault feature parameters and mapping them to a high-dimensional space, the method utilizes a local outlier factor (LOF) algorithm to detect anomalous values, enabling fault diagnosis in complex working conditions. This method improves the detection time by an average of 22 min compared to the extended RMSE method and maintains strong robustness while correctly detecting faults compared to other conventional methods.https://www.mdpi.com/1996-1073/17/7/1568electric vehiclelithium-ion battery packsfault diagnosislocal outlier factorreal vehicle conditions |
| spellingShingle | Fan Zhang Xiao Zheng Zixuan Xing Minghu Wu Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features Energies electric vehicle lithium-ion battery packs fault diagnosis local outlier factor real vehicle conditions |
| title | Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features |
| title_full | Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features |
| title_fullStr | Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features |
| title_full_unstemmed | Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features |
| title_short | Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features |
| title_sort | fault diagnosis method for lithium ion power battery incorporating multidimensional fault features |
| topic | electric vehicle lithium-ion battery packs fault diagnosis local outlier factor real vehicle conditions |
| url | https://www.mdpi.com/1996-1073/17/7/1568 |
| work_keys_str_mv | AT fanzhang faultdiagnosismethodforlithiumionpowerbatteryincorporatingmultidimensionalfaultfeatures AT xiaozheng faultdiagnosismethodforlithiumionpowerbatteryincorporatingmultidimensionalfaultfeatures AT zixuanxing faultdiagnosismethodforlithiumionpowerbatteryincorporatingmultidimensionalfaultfeatures AT minghuwu faultdiagnosismethodforlithiumionpowerbatteryincorporatingmultidimensionalfaultfeatures |