Comparative analysis of estimation techniques of SFOC induction motor for electric vehicles

Author

Haddoun, A. ; Benbouzid, M.E.H. ; Diallo, D. ; Abdessemed, R. ; Ghouili, J. ; Srairi, K.

Author_Institution

Lab. Brestois de Mec. et des Syst., Univ. of Brest, Brest

fYear

2008

fDate

6-9 Sept. 2008

Firstpage

1

Lastpage

6

Abstract

This paper presents system analysis, modeling and simulation of an electric vehicle with different sensorless control techniques. Indeed, sensorless control is considered to be a lower cost alternative than the position or speed encoder-based control of induction motors for an electric vehicle. Two popular sensorless control methods, namely, the Luenberger observer and the Kalman filter methods are compared regarding speed and torque control characteristics. They are also compared against the well-known model reference adaptive system. Simulations on a test vehicle propelled by 37-kW induction motor lead to very interesting comparison results.

Keywords

Kalman filters; adaptive control; electric vehicles; induction motors; machine vector control; position control; torque control; velocity control; Kalman filter methods; Luenberger observer; comparative analysis; electric vehicles; estimation techniques; induction motor; sensorless control techniques; speed control; speed encoder-based control; torque control; Aerodynamics; Electric vehicles; Force sensors; Induction motor drives; Induction motors; Low pass filters; Rotors; Sensorless control; Stators; Voltage; Electric Vehicle (EV); Kalman Filter (KF); Luenberger Observer (LO); Model Reference Adaptive System (MRAS); induction motor; speed estimation; traction control;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Machines, 2008. ICEM 2008. 18th International Conference on

Conference_Location

Vilamoura

Print_ISBN

978-1-4244-1735-3

Electronic_ISBN

978-1-4244-1736-0

Type

conf

DOI

10.1109/ICELMACH.2008.4800166

Filename

4800166