A novel online PMSM parameter identification method for electric and hybrid electric vehicles based on cluster technique

Khreis, Hussein; Deflorio, Andrea; Schmuelling, Benedikt

doi:10.1109/iemdc.2015.7409034

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…One of the common methods for determining motor modeling parameters is parameter identification, which is aimed at accurately estimating motor parameters, including stator inductance, stator resistance, rotor flux linkage, and load torque. 16,17 Another method is the cluster technique, which uses information on stator currents, stator voltages, and rotor angular speed, to identify electrical parameters, 18 while other methods consider perturbation and temperature. 19 Although these methods can accurately identify motor electrical parameters, no ready-made motor can be used to perform experiments for parameter identification during the initial development of an electric vehicle.…”

Section: Introductionmentioning

confidence: 99%

Determination of modeling parameters for a brushless DC motor that satisfies the power performance of an electric vehicle

Huang

Lei

Han

et al. 2019

Measurement and Control

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The analysis and control of powertrain systems of electric vehicle, which is an important type of new energy vehicle, have been the focus of extensive research, but determining the motor modeling parameters remains a problem. A method of parameter determination for brushless DC motor modeling based on vehicle power performance was developed in this study. The power and torque of the driving motor of an electric vehicle were obtained by using the dynamic equation of the electric vehicle to satisfy the requirements of power performance. The ranges of the back electromotive force coefficient and the winding inductance were derived from the voltage and dynamic equations of brushless DC motor, which were deduced from the expected power and torque of the motor. The modeling parameters were then determined on the basis of the influence of power source voltage, back electromotive force coefficient, winding inductance, and winding resistance on vehicle power performance. A hardware-in-loop simulation of vehicle power performance was performed to verify the effectiveness of the proposed method. Results indicate that the maximum vehicle velocity is 172 km/h, and the acceleration time of 100 km/h is 13 s, which reveal that the motor modeling parameters obtained with the method satisfy relevant requirements.

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Section: Introductionmentioning

confidence: 99%