Torsional Vibration Characterization of Hybrid Power Systems via Disturbance Observer and Partitioned Learning
The series–parallel hybrid powertrain combines the advantages of both series and parallel configurations, offering optimal power performance and fuel efficiency. However, the presence of multiple excitation sources significantly complicates the torsional vibration behavior during engine startup. To...
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2025-05-01
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| author | Tao Zheng Hui Xie Boqiang Liang |
| author_facet | Tao Zheng Hui Xie Boqiang Liang |
| author_sort | Tao Zheng |
| collection | DOAJ |
| description | The series–parallel hybrid powertrain combines the advantages of both series and parallel configurations, offering optimal power performance and fuel efficiency. However, the presence of multiple excitation sources significantly complicates the torsional vibration behavior during engine startup. To accurately identify and analyze the torsional vibration characteristics induced by shaft resonance in this process, a torsional vibration feature identification algorithm based on disturbance observation and parameter partition learning is proposed. A simplified model of the drivetrain shaft system is first established, and an extended state Kalman filter (ESKF) is designed to accurately estimate the torque of the torsional damper. The inclusion of extended disturbance states enhances the model’s robustness against system uncertainties. Subsequently, continuous wavelet transform (CWT) is employed to identify the resonance characteristics in the torsional vibration process from the torque signal. Combined with the parameter partition learning strategy, resonance frequencies are utilized to infer key system parameters. The results demonstrate that, under a 20% perturbation of structural parameters, the observer model with fixed parameters yields a root mean square error (RMSE) of 10.16 N·m for the torsional damper torque. In contrast, incorporating the parameter self-learning algorithm reduces the RMSE to 2.36 N·m, representing an 85.2% improvement in estimation accuracy. Using the Morlet wavelet with a frequency resolution parameter (VPO) of 15 at a 50 Hz sampling rate, the identified resonance frequency was 14.698 Hz, showing a 1.1% deviation from the actual natural frequency of 14.53 Hz. |
| format | Article |
| id | doaj-art-922ea969c20e45b5bf21f173cd20729c |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj-art-922ea969c20e45b5bf21f173cd20729c2025-08-20T03:11:18ZengMDPI AGEnergies1996-10732025-05-011811284710.3390/en18112847Torsional Vibration Characterization of Hybrid Power Systems via Disturbance Observer and Partitioned LearningTao Zheng0Hui Xie1Boqiang Liang2School of Mechanical Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin 300072, ChinaThe series–parallel hybrid powertrain combines the advantages of both series and parallel configurations, offering optimal power performance and fuel efficiency. However, the presence of multiple excitation sources significantly complicates the torsional vibration behavior during engine startup. To accurately identify and analyze the torsional vibration characteristics induced by shaft resonance in this process, a torsional vibration feature identification algorithm based on disturbance observation and parameter partition learning is proposed. A simplified model of the drivetrain shaft system is first established, and an extended state Kalman filter (ESKF) is designed to accurately estimate the torque of the torsional damper. The inclusion of extended disturbance states enhances the model’s robustness against system uncertainties. Subsequently, continuous wavelet transform (CWT) is employed to identify the resonance characteristics in the torsional vibration process from the torque signal. Combined with the parameter partition learning strategy, resonance frequencies are utilized to infer key system parameters. The results demonstrate that, under a 20% perturbation of structural parameters, the observer model with fixed parameters yields a root mean square error (RMSE) of 10.16 N·m for the torsional damper torque. In contrast, incorporating the parameter self-learning algorithm reduces the RMSE to 2.36 N·m, representing an 85.2% improvement in estimation accuracy. Using the Morlet wavelet with a frequency resolution parameter (VPO) of 15 at a 50 Hz sampling rate, the identified resonance frequency was 14.698 Hz, showing a 1.1% deviation from the actual natural frequency of 14.53 Hz.https://www.mdpi.com/1996-1073/18/11/2847hybrid electric vehicletorsional vibrationdisturbance observertransient frequency-domain characteristicslearning algorithm |
| spellingShingle | Tao Zheng Hui Xie Boqiang Liang Torsional Vibration Characterization of Hybrid Power Systems via Disturbance Observer and Partitioned Learning Energies hybrid electric vehicle torsional vibration disturbance observer transient frequency-domain characteristics learning algorithm |
| title | Torsional Vibration Characterization of Hybrid Power Systems via Disturbance Observer and Partitioned Learning |
| title_full | Torsional Vibration Characterization of Hybrid Power Systems via Disturbance Observer and Partitioned Learning |
| title_fullStr | Torsional Vibration Characterization of Hybrid Power Systems via Disturbance Observer and Partitioned Learning |
| title_full_unstemmed | Torsional Vibration Characterization of Hybrid Power Systems via Disturbance Observer and Partitioned Learning |
| title_short | Torsional Vibration Characterization of Hybrid Power Systems via Disturbance Observer and Partitioned Learning |
| title_sort | torsional vibration characterization of hybrid power systems via disturbance observer and partitioned learning |
| topic | hybrid electric vehicle torsional vibration disturbance observer transient frequency-domain characteristics learning algorithm |
| url | https://www.mdpi.com/1996-1073/18/11/2847 |
| work_keys_str_mv | AT taozheng torsionalvibrationcharacterizationofhybridpowersystemsviadisturbanceobserverandpartitionedlearning AT huixie torsionalvibrationcharacterizationofhybridpowersystemsviadisturbanceobserverandpartitionedlearning AT boqiangliang torsionalvibrationcharacterizationofhybridpowersystemsviadisturbanceobserverandpartitionedlearning |