• DocumentCode
    1551195
  • Title

    Improvement of control characteristics of interior permanent-magnet synchronous motor for electric vehicle

  • Author

    Park, Jung-Woo ; Koo, Dae-Hyun ; Kim, Jong-Moo ; Kim, Heung-Geun

  • Author_Institution
    Korea Electrotechnol. Res. Inst., Changwon, South Korea
  • Volume
    37
  • Issue
    6
  • fYear
    2001
  • Firstpage
    1754
  • Lastpage
    1760
  • Abstract
    This paper describes a method to improve the control characteristics of an interior permanent-magnet synchronous motor for an electric vehicle (EV). At the motor for an electric vehicle, d- and q-axes inductances are varied to a great extent. Therefore, variation characteristics for d- and q-axes inductances are analyzed as a function of both current magnitude and current phase angle. A new vector control algorithm is proposed that makes use of the learning capability of a neural network to implement d- and q-axes inductances to be varied according to current magnitude and also current phase angle. Also, a distribution law of torque reference to improve the driving characteristics of the two motor-driven EV is proposed. Advanced performance is verified through numerical methods and experiments
  • Keywords
    control system analysis; control system synthesis; electric vehicles; inductance; learning (artificial intelligence); machine testing; machine vector control; neurocontrollers; permanent magnet motors; synchronous motors; traction motors; control characteristics improvement; control design; control simulation; current magnitude; current phase angle; driving characteristics; electric vehicle; inductances; interior permanent-magnet synchronous motor; learning capability; neural network; torque reference distribution law; vector control algorithm; Electric vehicles; Induction motors; Industry Applications Society; Machine vector control; Neural networks; Prototypes; Synchronous motors; Torque control; Traction motors; Wheels;
  • fLanguage
    English
  • Journal_Title
    Industry Applications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0093-9994
  • Type

    jour

  • DOI
    10.1109/28.968188
  • Filename
    968188