Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9698074/ |
| _version_ | 1828152092015984640 |
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| author | Lei Yao Shiming Xu Yanqiu Xiao Junjian Hou Xiaoyun Gong Zhijun Fu Aihua Tang |
| author_facet | Lei Yao Shiming Xu Yanqiu Xiao Junjian Hou Xiaoyun Gong Zhijun Fu Aihua Tang |
| author_sort | Lei Yao |
| collection | DOAJ |
| description | 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 of fault identification. It is feasible in the application of electric vehicles. To ensure the effectiveness of the signal, the proposed method is adopted using the simple mean filter to clean the data with eliminate wrong points. After the variance analysis, covariance, a horizontal variance of the filtered data, a modified feature parameters matrix is presented. The dimension is reduced by principal component analysis to improve the engineering application ability. Furthermore, a comprehensive GA-ELM-based identification method is proposed to reduce the resulting identification error of extreme learning machines due to the initial value change. More importantly, the sensitivity and accuracy of different solutions are compared and verified, which shows the technique has great potential in battery fault diagnosis based on the voltage signal. |
| first_indexed | 2024-04-11T22:06:47Z |
| format | Article |
| id | doaj.art-12f4a3a38bb5493fa3e268e4815f5548 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T22:06:47Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-12f4a3a38bb5493fa3e268e4815f55482022-12-22T04:00:40ZengIEEEIEEE Access2169-35362022-01-0110150071502210.1109/ACCESS.2022.31478029698074Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural NetworkLei Yao0Shiming Xu1https://orcid.org/0000-0002-5295-4282Yanqiu Xiao2Junjian Hou3Xiaoyun Gong4Zhijun Fu5Aihua Tang6Henan Engineering Research Center of New Energy Vehicle Lightweight Design and Manufacturing, Zhengzhou University of Light Industry, Zhengzhou, ChinaHenan Engineering Research Center of New Energy Vehicle Lightweight Design and Manufacturing, Zhengzhou University of Light Industry, Zhengzhou, ChinaHenan Engineering Research Center of New Energy Vehicle Lightweight Design and Manufacturing, Zhengzhou University of Light Industry, Zhengzhou, ChinaHenan Engineering Research Center of New Energy Vehicle Lightweight Design and Manufacturing, Zhengzhou University of Light Industry, Zhengzhou, ChinaHenan Engineering Research Center of New Energy Vehicle Lightweight Design and Manufacturing, Zhengzhou University of Light Industry, Zhengzhou, ChinaHenan Engineering Research Center of New Energy Vehicle Lightweight Design and Manufacturing, Zhengzhou University of Light Industry, Zhengzhou, ChinaVehicle Engineering Institute, Chongqing University of Technology, Chongqing, ChinaThe 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 of fault identification. It is feasible in the application of electric vehicles. To ensure the effectiveness of the signal, the proposed method is adopted using the simple mean filter to clean the data with eliminate wrong points. After the variance analysis, covariance, a horizontal variance of the filtered data, a modified feature parameters matrix is presented. The dimension is reduced by principal component analysis to improve the engineering application ability. Furthermore, a comprehensive GA-ELM-based identification method is proposed to reduce the resulting identification error of extreme learning machines due to the initial value change. More importantly, the sensitivity and accuracy of different solutions are compared and verified, which shows the technique has great potential in battery fault diagnosis based on the voltage signal.https://ieeexplore.ieee.org/document/9698074/Lithium-ion batteryfault diagnosisextreme learning machinesfeature parameters |
| spellingShingle | Lei Yao Shiming Xu Yanqiu Xiao Junjian Hou Xiaoyun Gong Zhijun Fu Aihua Tang Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network IEEE Access Lithium-ion battery fault diagnosis extreme learning machines feature parameters |
| title | Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network |
| title_full | Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network |
| title_fullStr | Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network |
| title_full_unstemmed | Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network |
| title_short | Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network |
| title_sort | fault identification of lithium ion battery pack for electric vehicle based on ga optimized elm neural network |
| topic | Lithium-ion battery fault diagnosis extreme learning machines feature parameters |
| url | https://ieeexplore.ieee.org/document/9698074/ |
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