Fault mode detection of a hybrid electric vehicle by using support vector machine
Hybrid electric vehicle (HEV) is one of the ideal transportation tools to face the challenge of ‘Carbon peak carbon neutralization’. The high complexity of the latest HEVs result to the difficulties in vehicle fault diagnostics, which is considered to be the main factors of vehicle durability. In th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723006728 |
| _version_ | 1797688417434206208 |
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| author | Fanshuo Liu Bolan Liu Junwei Zhang Peng Wan Ben Li |
| author_facet | Fanshuo Liu Bolan Liu Junwei Zhang Peng Wan Ben Li |
| author_sort | Fanshuo Liu |
| collection | DOAJ |
| description | Hybrid electric vehicle (HEV) is one of the ideal transportation tools to face the challenge of ‘Carbon peak carbon neutralization’. The high complexity of the latest HEVs result to the difficulties in vehicle fault diagnostics, which is considered to be the main factors of vehicle durability. In this study, a multi-fault mode detection method of a P2 diesel HEV was investigated by using support vector machine(SVM). The HEV physical model was built and validated by using the experimental data under China typical urban driving cycle (CTUDC). The SVM algorithm was coded in Matlab/Simulink environment. The training data vectors for normal mode and fault mode were acquired from the HEV model. Through the analysis of failure mode, the OVO-SVM method was proved as the best accuracy, the success rate of diagnosis reaches 98%. Case studies of single fault mode and multi-fault mode fault detection were conducted. Offline and online level test were performed to show the effectiveness of the detection algorithm. The study may support the development of intelligent diagnostic methods for the different types of HEVs. |
| first_indexed | 2024-03-12T01:30:41Z |
| format | Article |
| id | doaj.art-cf9ebbfdc14c402e87c3ae51a6fd6b4c |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-12T01:30:41Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-cf9ebbfdc14c402e87c3ae51a6fd6b4c2023-09-12T04:15:54ZengElsevierEnergy Reports2352-48472023-09-019137148Fault mode detection of a hybrid electric vehicle by using support vector machineFanshuo Liu0Bolan Liu1Junwei Zhang2Peng Wan3Ben Li4School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaCorresponding author.; School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaHybrid electric vehicle (HEV) is one of the ideal transportation tools to face the challenge of ‘Carbon peak carbon neutralization’. The high complexity of the latest HEVs result to the difficulties in vehicle fault diagnostics, which is considered to be the main factors of vehicle durability. In this study, a multi-fault mode detection method of a P2 diesel HEV was investigated by using support vector machine(SVM). The HEV physical model was built and validated by using the experimental data under China typical urban driving cycle (CTUDC). The SVM algorithm was coded in Matlab/Simulink environment. The training data vectors for normal mode and fault mode were acquired from the HEV model. Through the analysis of failure mode, the OVO-SVM method was proved as the best accuracy, the success rate of diagnosis reaches 98%. Case studies of single fault mode and multi-fault mode fault detection were conducted. Offline and online level test were performed to show the effectiveness of the detection algorithm. The study may support the development of intelligent diagnostic methods for the different types of HEVs.http://www.sciencedirect.com/science/article/pii/S2352484723006728Hybrid electric vehicleFault mode detectionModeling and validationSupport vector machineReal time simulation |
| spellingShingle | Fanshuo Liu Bolan Liu Junwei Zhang Peng Wan Ben Li Fault mode detection of a hybrid electric vehicle by using support vector machine Energy Reports Hybrid electric vehicle Fault mode detection Modeling and validation Support vector machine Real time simulation |
| title | Fault mode detection of a hybrid electric vehicle by using support vector machine |
| title_full | Fault mode detection of a hybrid electric vehicle by using support vector machine |
| title_fullStr | Fault mode detection of a hybrid electric vehicle by using support vector machine |
| title_full_unstemmed | Fault mode detection of a hybrid electric vehicle by using support vector machine |
| title_short | Fault mode detection of a hybrid electric vehicle by using support vector machine |
| title_sort | fault mode detection of a hybrid electric vehicle by using support vector machine |
| topic | Hybrid electric vehicle Fault mode detection Modeling and validation Support vector machine Real time simulation |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723006728 |
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