• DocumentCode
    3314242
  • Title

    Modelling on BLDC motor performance using artificial neural network (ANN)

  • Author

    Nizam, M. ; Mujianto, Agus ; Triwaloyo, Hery ; Inayati

  • Author_Institution
    Postgrad. Sch. of Mech. Eng., Sebelas Maret Univ., Surakarta, Indonesia
  • fYear
    2013
  • fDate
    26-28 Nov. 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Brushless direct current (BLDC) motor is now widely used for industrial and automotive field since its efficiency is higher than a permanent motors and it has relatively small size. Therefore modelling of BLDC was necessary for its application. The objective of this research was to investigate the performance of BLDC motor modeled by using ANN. The target for this work was to get the maximum power consumption of a BLDC motor. Back propagation algorithm based ANN was then applied in order to achieved the efficiency target. For ANN, torque and angular velocity was set as the input. Power consumption efficiency was set as the output. The ANN model showed good performance with average of error of 0.5 and MSE of 1.5. It was concluded that an accurate prediction can be performed by using ANN.
  • Keywords
    backpropagation; brushless DC motors; neural nets; power engineering computing; ANN; BLDC modelling; BLDC motor performance; angular velocity; artificial neural network; automotive field; backpropagation algorithm; brushless direct current motor; industrial field; permanent motors; power consumption; torque; Artificial neural networks; Brushless DC motors; Mathematical model; Permanent magnet motors; Synchronous motors; BLDC; artificial neural network; back propagation; efficiency; simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Rural Information & Communication Technology and Electric-Vehicle Technology (rICT & ICeV-T), 2013 Joint International Conference on
  • Conference_Location
    Bandung
  • Print_ISBN
    978-1-4799-3363-1
  • Type

    conf

  • DOI
    10.1109/rICT-ICeVT.2013.6741520
  • Filename
    6741520