Multi-parameter Online Identification Algorithm of Induction Motor for Hybrid Electric Vehicle Applications
The induction motor parameters vary with the operating conditions considerably in the Hybrid Electric Vehicle. The induction motor drive cannot deliver high static and dynamic performance without the correct machine parameter values in the controller. Therefore, an online multi-parameter identificat...
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| Veröffentlicht in: | Proceedings (International Symposium on Parallel Architectures, Algorithms and Programming. Print) S. 35 - 39 |
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| 1. Verfasser: | |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
01.07.2014
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| Schlagworte: | |
| ISSN: | 2168-3034 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | The induction motor parameters vary with the operating conditions considerably in the Hybrid Electric Vehicle. The induction motor drive cannot deliver high static and dynamic performance without the correct machine parameter values in the controller. Therefore, an online multi-parameter identification technique for induction motor is necessary. In this paper, a novel Luenberger-sliding mode observer with adaptive identification is presented to simultaneously track and adapt the stator current and rotor flux. Estimated stator current dynamic output error is employed to identify rotor resistance, stator resistance with Lyapunov's stability criterion in real time. The proposed algorithm relies upon the signals and states readily available within the digital controller memory. These are the flux estimate and the stator current. The software-in-the-loop simulation has been implemented in Matlab to validate the algorithm in fast convergence of parameters as well as robust in the event of internal noise and large disturbances in measurements. |
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| ISSN: | 2168-3034 |
| DOI: | 10.1109/PAAP.2014.35 |