Data-driven modeling and fault diagnosis for fuel cell vehicles using deep learning
The reliability and safety of fuel cell vehicle are crucial for the daily operation. Insulation resistance serves as a crucial index of vehicle reliability, especially when fuel cells operate at high voltages. Low insulation resistance can lead to vehicle malfunctions, exposing the operator to the r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-05-01
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| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824000119 |
| _version_ | 1827256588275023872 |
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| author | Yangeng Chen Jingjing Zhang Shuang Zhai Zhe Hu |
| author_facet | Yangeng Chen Jingjing Zhang Shuang Zhai Zhe Hu |
| author_sort | Yangeng Chen |
| collection | DOAJ |
| description | The reliability and safety of fuel cell vehicle are crucial for the daily operation. Insulation resistance serves as a crucial index of vehicle reliability, especially when fuel cells operate at high voltages. Low insulation resistance can lead to vehicle malfunctions, exposing the operator to the risk of electric shock. In this study, long-term insulation resistance data from thirteen vehicles equipped with three different types of fuel cell systems are analyzed to diagnose possible low insulation resistance issues. For this purpose, a robust locally weighted scatterplot smoothing method is utilized to filter the original data. In this research, an insulation variation model is developed using a data-driven long short-term memory neural network to identify insulation resistance value anomalies caused by deionizer failure. The results indicate that the coefficient of determination of the failure model is 99.78 %. Moreover, current model efficiently identifies insulation faults resulting from reliability issues, such as conductivity issues of cooling pipes and erosion of vehicle wiring harnesses. |
| first_indexed | 2024-03-08T06:54:30Z |
| format | Article |
| id | doaj.art-f8509777fbec458980f9ceb6bf28c761 |
| institution | Directory Open Access Journal |
| issn | 2666-5468 |
| language | English |
| last_indexed | 2025-03-22T01:41:17Z |
| publishDate | 2024-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj.art-f8509777fbec458980f9ceb6bf28c7612024-05-09T04:37:03ZengElsevierEnergy and AI2666-54682024-05-0116100345Data-driven modeling and fault diagnosis for fuel cell vehicles using deep learningYangeng Chen0Jingjing Zhang1Shuang Zhai2Zhe Hu3College of Science, University of Shanghai for Science and Technology (USST), Shanghai, 200093, ChinaCollege of Science, University of Shanghai for Science and Technology (USST), Shanghai, 200093, China; Corresponding author.Shanghai Refire Technology Co., Ltd., 655 Jinyuanyi Road, Jiading, Shanghai, 201800, ChinaShanghai Refire Technology Co., Ltd., 655 Jinyuanyi Road, Jiading, Shanghai, 201800, ChinaThe reliability and safety of fuel cell vehicle are crucial for the daily operation. Insulation resistance serves as a crucial index of vehicle reliability, especially when fuel cells operate at high voltages. Low insulation resistance can lead to vehicle malfunctions, exposing the operator to the risk of electric shock. In this study, long-term insulation resistance data from thirteen vehicles equipped with three different types of fuel cell systems are analyzed to diagnose possible low insulation resistance issues. For this purpose, a robust locally weighted scatterplot smoothing method is utilized to filter the original data. In this research, an insulation variation model is developed using a data-driven long short-term memory neural network to identify insulation resistance value anomalies caused by deionizer failure. The results indicate that the coefficient of determination of the failure model is 99.78 %. Moreover, current model efficiently identifies insulation faults resulting from reliability issues, such as conductivity issues of cooling pipes and erosion of vehicle wiring harnesses.http://www.sciencedirect.com/science/article/pii/S2666546824000119Fuel cell vehiclesInsulation faultsRLOWESSLSTM neural network |
| spellingShingle | Yangeng Chen Jingjing Zhang Shuang Zhai Zhe Hu Data-driven modeling and fault diagnosis for fuel cell vehicles using deep learning Energy and AI Fuel cell vehicles Insulation faults RLOWESS LSTM neural network |
| title | Data-driven modeling and fault diagnosis for fuel cell vehicles using deep learning |
| title_full | Data-driven modeling and fault diagnosis for fuel cell vehicles using deep learning |
| title_fullStr | Data-driven modeling and fault diagnosis for fuel cell vehicles using deep learning |
| title_full_unstemmed | Data-driven modeling and fault diagnosis for fuel cell vehicles using deep learning |
| title_short | Data-driven modeling and fault diagnosis for fuel cell vehicles using deep learning |
| title_sort | data driven modeling and fault diagnosis for fuel cell vehicles using deep learning |
| topic | Fuel cell vehicles Insulation faults RLOWESS LSTM neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824000119 |
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