• DocumentCode
    119248
  • Title

    Drive system for electric vehicle power train application using DC to AC matrix converter

  • Author

    Prabhakar, Kashyap Kumar ; Singh, Amit Kumar ; Reddy, C. Upendra ; Kumar, Praveen

  • Author_Institution
    EEE Dept., IIT Guwahati, Guwahati, India
  • fYear
    2014
  • fDate
    16-19 Dec. 2014
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    In drive system of an Electric Vehicle (EV) power train, the battery and power converters are most important parts. In this paper a noval drive system is proposed for Electric Vehicle(EV) power train. Here we used a real time electric circuit based battery model as a power source of drive system unlike conventional ideal battery source. To convert fixed DC to three phase AC, a new topology of DC to AC matrix converter (MC) has been proposed. which is able to perform bidirectional power flow and multi quadrant operations. which is essential in EVs. The vector control based Direct torque control (DTC) technique is used to generate switching sequence for the DC to AC MC. The complete drive system is simulated using MATLAB/Simulink enviroment.
  • Keywords
    DC-AC power convertors; battery powered vehicles; induction motor drives; machine vector control; matrix convertors; power transmission (mechanical); torque control; DC-AC matrix converter; Matlab; Simulink; battery model; bidirectional power flow; drive system; electric vehicle power train; ideal battery source; multiquadrant operation; power source; real time electric circuit; switching sequence generation; three phase AC; vector control based direct torque control; Batteries; Electric vehicles; Integrated circuit modeling; Machine vector control; Matrix converters; Real-time systems; Topology; battery; drive system; electric vehicle; matrix converter; vector control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Electronics, Drives and Energy Systems (PEDES), 2014 IEEE International Conference on
  • Conference_Location
    Mumbai
  • Print_ISBN
    978-1-4799-6372-0
  • Type

    conf

  • DOI
    10.1109/PEDES.2014.7042104
  • Filename
    7042104