IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/8783615.html
   My bibliography  Save this article

Loss Prediction and Thermal Analysis of Surface-Mounted Brushless AC PM Machines for Electric Vehicle Application Considering Driving Duty Cycle

Author

Listed:
  • Tianxun Chen
  • Xiaopeng Wu
  • Yugang Dong
  • Chengning Zhang
  • Haipeng Liu

Abstract

This paper presents a computationally efficient loss prediction procedure and thermal analysis of surface-mounted brushless AC permanent magnet (PM) machine considering the UDDS driving duty cycle by using a lumped parameters’ thermal model. The accurate prediction of loss and its variation with load are essential for thermal analysis. Employing finite element analysis (FEA) to determine loss at every load point would be computationally intensive. Here, the finite element analysis and/or experiment based computationally efficient winding copper and iron loss and permanent magnet (PM) power loss models are employed to calculate the electromagnetic loss at every operation point, respectively. Then, the lumped parameter thermal method is used to analyse the thermal behaviour of the driving PM machine. Experiments have been carried out to measure the temperature distribution in a motor prototype. The calculation and experiment results are compared and discussed.

Suggested Citation

  • Tianxun Chen & Xiaopeng Wu & Yugang Dong & Chengning Zhang & Haipeng Liu, 2016. "Loss Prediction and Thermal Analysis of Surface-Mounted Brushless AC PM Machines for Electric Vehicle Application Considering Driving Duty Cycle," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-8, January.
  • Handle: RePEc:hin:jnlmpe:8783615
    DOI: 10.1155/2016/8783615
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2016/8783615.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2016/8783615.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2016/8783615?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:8783615. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.