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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Published in:Proceedings (International Symposium on Parallel Architectures, Algorithms and Programming. Print) pp. 35 - 39
Main Author: Yan Liu
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2014
Subjects:
induction motor
Induction motors
Luenberger-sliding mode observer
Mathematical model
multi-parameter identification
Observers
parameter adaptation
Resistance
Rotors
Stators
ISSN:2168-3034
Online Access:Get full text
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Abstract 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.
AbstractList 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.
Author Yan Liu
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Snippet The induction motor parameters vary with the operating conditions considerably in the Hybrid Electric Vehicle. The induction motor drive cannot deliver high...
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StartPage 35
SubjectTerms induction motor
Induction motors
Luenberger-sliding mode observer
Mathematical model
multi-parameter identification
Observers
parameter adaptation
Resistance
Rotors
Stators
Title Multi-parameter Online Identification Algorithm of Induction Motor for Hybrid Electric Vehicle Applications
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