• Journal of Power Electronics
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• v.11 no.4
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• pp.418-423
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• 2011

This paper presents a simulation model and a parameter identification scheme of an induction motor drive for electric vehicle. The induction motor in automotive applications should operate in very high efficiency and achieve the maximum-torque-per-ampere (MTPA) feature even with saturated magnetic flux under very high torque. The indirect vector control which is typically adopted in traction drive system requires precise information of motor parameters, particularly rotor time constants. This work models an induction motor considering magnetic saturation and proposes an empirical identification method using the current controller in the synchronous reference frame. The proposed method is applied to a 22kW-rated induction motor for electric vehicle.

• Journal of Electrical Engineering and Technology
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• v.11 no.3
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• pp.769-774
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• 2016

In the design of sensorless control system for induction motor, high-precision speed estimation is one of the most difficult problems. To solve this problem, the common method is model reference adaptive method (MRAS). MRAS requires accurate motor parameters to estimate rotor speed precisely. However, when motor is running, the variety of temperature and magnetic saturation will lead to the change of motor parameters such as stator resistance and rotor resistance, which will lower the accuracy of the speed estimation. To improve the accuracy and rapidity of speed estimation, this paper analyses the mutual MRAS speed identification based on rotor flux linkage, and proposes an improved mutual MRAS speed identification based on back-EMF. The improved method is verified by Simulink simulation and motor experimental platform based on DSP2812. The results of simulation and experiment indicate that the method proposed by this paper can significantly improve the accuracy of speed identification, and speed up the response of identification.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.852-856
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• 2004

An induction motor is one of the most popular electrical apparatuses owing to its simple structure and robust construction. Parameter identification of the induction motor has long been researched either for a vector control technique or fault detection. Since vector control is a well-established technique for induction motor control, this paper concentrates on successive identification of physical parameters with on-load data for the purpose of condition monitoring and/or fault detection. For extracting six physical parameters from the on-load data in the framework of the induction motor state equation, unmeasured initial state values and profiles of load torque have to be estimated as well. However, the analytic optimization methods in general fail to estimate these auxiliary but significant parameters owing to the difficulty of obtaining their gradient information. In this paper, the univariate dynamic encoding algorithm for searches (uDEAS) newly developed is applied to the identification of whole unknown parameters in the mathematical equations of an induction motor with normal operating data. Profiles of identified parameters appear to be reasonable and therefore the proposed approach is available for fault diagnosis of induction motors by monitoring physical parameters.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1856-1857
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• 2011

Induction motor is nonlinear multivariable system. It is not easy to control precisely. Usually Induction motor need linearized model in order to make it easy to control. In this paper, linearized model of nonlinear model in induction motor can change by using TS Fuzzy Identification.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.50 no.4
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• pp.151-157
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• 2001

This paper presents an identification method of parameters of induction motor which is driven by PWM voltage inverter. The method uses least square estimation based on the step voltage input and current response. Utilizing the fact that ratio of two characteristic roots is large in the induction motor circuit, we derived two 1st-order difference equations for direct computation of parameter values. experimental results are compared with conventional motor test results to demonstrate that the proposed method is capable of estimating parameters of induction motor at standstill.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.709-712
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• 2003

Recently, high noise problem was experienced during the development of 2-pole squirrel cage motor for industrial compressor. In order to firstly identify the noise characteristics, a variety of measurements were carried out. It was found out that high noise was dominated by linear and nonlinear slot noise components. For the development of low noise indusrial motor, the air gap between rotor and stator in the motor was firstly enlarged. Secondly, it was also modified for the cooling housing to have high absorption features. Consequencely, low noise 2-pole motor having the noise level of less 80㏈(A) was developed. In this paper, a series of noise identification and control process for this motor are introduced.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.7 no.6
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• pp.34-41
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• 1993

This paper describes an effort to develop a microcomputer-based parameter identification system for three phase and two phase brushless DC motor. Back EMF equation is derived from back EMF waveform of three phase and two phase brushless DC motor. In this paper, a new identification algorithm for the brushless DC motor parameters by Pasek's technique is de veloped. It is found that Pasek's equation is valid for the brushless DC motor, too. The results obtained clearly shows that it is possible to implement the identification system for the determination of the brushless DC motor parameters. To minimize errors due to the ripple component in the measured armature current, dlgital averaging filter is employed. The whole identification process of signal generation, measuring, parameter determination is fully automated. The use of the pmpased method in the parameter identification system shows that the averaged current curve is in excellent agreement with the estimated current curve. Therefore, this close agreement conf i i the validity of this technique.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.597-605
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• 2010

This paper presents a contribution to parameter identification of a non-linear system using a new strategy to improve the genetic algorithm (GA) method. Since cost function plays an important role in GA-based parameter identification, we propose to improve the simple version of GA, where weights of the cost function are not taken as constant values, but varying along the procedure of parameter identification. This modified version of GA is applied to the induction motor (IM) as an example of nonlinear system. The GA cost function is the weighted sum of stator current and rotor speed errors between the plant and the model of induction motor. Simulation results show that the identification method based on improved GA is feasible and gives high precision.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.177-180
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• 2000

The accurate values of parameters of an induction motor are required for its high performance control. So far many methods using current sensors voltage sensors and speed sensor have been developed. This paper proposes an identification method of parameters of induction motor only using current sensors.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.1-11
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• 1996

The rotor flux level need be changed frequently for field weakening or power efficiency control. Motor inductances depend on rotor flux but not on machine temperature. On the other hand, rotor resistance varies greatly with the machine temperature. Motor parameters such a sinductances and rotor resistance should be known precisely in order to attain high dynamic performance of inductin motor. In this paper, efficient an dnovel identification algorithms for motor inductances and rotor resistance are presented. The rotor flux is changed. As the result, the slip frequency is varied. The identificatin algorithm for rotor resistance measures the varied slip frequency and alters the estimated rotor resistance. Then, the estimated value of rotor resistance will approach its real value. The proposed identification algorithms are computationally simple and have very small identification errors.