Degradation Diagnosis and Control Strategy for a Diesel Hybrid Powertrain Considering State of Health
Hybrid electric vehicles (HEV) are a practical choice for energy saving in the transportation field. Degradation diagnosis (DD) is one of the main methods to guarantee system robustness. However, the classical DD methods cannot meet the requirements of HEV due to their system complexity. In this stu...
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MDPI AG
2024-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/21/5413 |
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| author | Jingxian Tang Bolan Liu Wenhao Fan Dawei Zhong Liang Liu |
| author_facet | Jingxian Tang Bolan Liu Wenhao Fan Dawei Zhong Liang Liu |
| author_sort | Jingxian Tang |
| collection | DOAJ |
| description | Hybrid electric vehicles (HEV) are a practical choice for energy saving in the transportation field. Degradation diagnosis (DD) is one of the main methods to guarantee system robustness. However, the classical DD methods cannot meet the requirements of HEV due to their system complexity. In this study, a novel Prognostics and Health Management (PHM) study was conducted to face these challenges. Firstly, a physical P2 HEV model with a rule-based controller was built, and its diesel engine sub-model was simplified by a neural network (NN) to ensure real-time performance of the degradation prognostics. Secondly, a degradation prognostics method based on gray relation analysis–principal component analysis (GRA-PCA) was illustrated, which could confirm degradation 2 s after the health index fell below the threshold. Finally, a degradation tolerance strategy based on long short term memory–model predictive control (LSTM-MPC) was performed to optimize vehicle speed tracing with minimal energy consumption and was validated by three cases. The result shows that the energy consumption stayed nearly unchanged for the engine degradation case. For the battery degradation case, the tracing error was reduced by 11.7% with 4.3% more energy consumption. For combined degradation, the strategy achieved a 12.3% tracing error reduction with 3.7% more energy consumption. The suggested PHM method guaranteed vehicle power performance under degradation situations. |
| format | Article |
| id | doaj-art-48cba866e8b2405c8d90304e90e6f364 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-48cba866e8b2405c8d90304e90e6f3642025-08-20T02:14:15ZengMDPI AGEnergies1996-10732024-10-011721541310.3390/en17215413Degradation Diagnosis and Control Strategy for a Diesel Hybrid Powertrain Considering State of HealthJingxian Tang0Bolan Liu1Wenhao Fan2Dawei Zhong3Liang Liu4School 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, ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaHybrid electric vehicles (HEV) are a practical choice for energy saving in the transportation field. Degradation diagnosis (DD) is one of the main methods to guarantee system robustness. However, the classical DD methods cannot meet the requirements of HEV due to their system complexity. In this study, a novel Prognostics and Health Management (PHM) study was conducted to face these challenges. Firstly, a physical P2 HEV model with a rule-based controller was built, and its diesel engine sub-model was simplified by a neural network (NN) to ensure real-time performance of the degradation prognostics. Secondly, a degradation prognostics method based on gray relation analysis–principal component analysis (GRA-PCA) was illustrated, which could confirm degradation 2 s after the health index fell below the threshold. Finally, a degradation tolerance strategy based on long short term memory–model predictive control (LSTM-MPC) was performed to optimize vehicle speed tracing with minimal energy consumption and was validated by three cases. The result shows that the energy consumption stayed nearly unchanged for the engine degradation case. For the battery degradation case, the tracing error was reduced by 11.7% with 4.3% more energy consumption. For combined degradation, the strategy achieved a 12.3% tracing error reduction with 3.7% more energy consumption. The suggested PHM method guaranteed vehicle power performance under degradation situations.https://www.mdpi.com/1996-1073/17/21/5413hybrid electric vehicleprognostics and health managementdegradation prognosticshealth status assessmentdegradation tolerance strategymodel predictive control |
| spellingShingle | Jingxian Tang Bolan Liu Wenhao Fan Dawei Zhong Liang Liu Degradation Diagnosis and Control Strategy for a Diesel Hybrid Powertrain Considering State of Health Energies hybrid electric vehicle prognostics and health management degradation prognostics health status assessment degradation tolerance strategy model predictive control |
| title | Degradation Diagnosis and Control Strategy for a Diesel Hybrid Powertrain Considering State of Health |
| title_full | Degradation Diagnosis and Control Strategy for a Diesel Hybrid Powertrain Considering State of Health |
| title_fullStr | Degradation Diagnosis and Control Strategy for a Diesel Hybrid Powertrain Considering State of Health |
| title_full_unstemmed | Degradation Diagnosis and Control Strategy for a Diesel Hybrid Powertrain Considering State of Health |
| title_short | Degradation Diagnosis and Control Strategy for a Diesel Hybrid Powertrain Considering State of Health |
| title_sort | degradation diagnosis and control strategy for a diesel hybrid powertrain considering state of health |
| topic | hybrid electric vehicle prognostics and health management degradation prognostics health status assessment degradation tolerance strategy model predictive control |
| url | https://www.mdpi.com/1996-1073/17/21/5413 |
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