Login | Register

Speed-sensorless torque control of induction motors for hybrid electric vehicles


Speed-sensorless torque control of induction motors for hybrid electric vehicles

Fu, Tianjun (2005) Speed-sensorless torque control of induction motors for hybrid electric vehicles. Masters thesis, Concordia University.

Text (application/pdf)
MR10263.pdf - Accepted Version


Hybrid Electric Vehicles (HEVs) are exciting new additions to the car markets since they combine the best features of conventional and electric cars to improve environmental performance and reduce fuel consumption. HEVs get their driving power from both an internal combustion engine and an electric motor. Many researches have demonstrated the induction motor is one of the right electric motor candidates for the most HEVs due to its low cost, robustness and low maintenance. The objective of this research work is to develop a new speed-sensorless control method for induction motors to optimize torque response and improve robustness in order to meet the requirement of HEV applications. The proposed new control method is based on Sliding-Mode Control (SMC) combined with Space Vector Modulation (SVM) technique. The SMC contributes to the robustness of induction motor drives, and the SVM improves the torque, flux, and current steady-state performance by reducing the ripple. The Lyapunov direct method is used to ensure the reaching and sustaining of sliding-mode and stability of the control system. A sliding-mode observer is proposed to estimate the rotor flux and speed. Computer simulation results show that the proposed control scheme owns very good dynamic characteristics, high accuracy in torque tracking to various reference signals and strong robustness to external load disturbances

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical and Industrial Engineering
Item Type:Thesis (Masters)
Authors:Fu, Tianjun
Pagination:xv, 118 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Mechanical and Industrial Engineering
Thesis Supervisor(s):Xie, Wen Fang
ID Code:8686
Deposited By: Concordia University Library
Deposited On:18 Aug 2011 18:32
Last Modified:18 Jan 2018 17:33
Related URLs:
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Back to top Back to top