IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v167y2020icp478-494.html
   My bibliography  Save this article

Power management improvement of hybrid energy storage system based on H ∞ control

Author

Listed:
  • Sellali, M.
  • Betka, A.
  • Djerdir, A.

Abstract

This paper deals with the real-time implementation of a robust control for electric vehicles, supplied by a battery/capacitor super capacitor hybrid energy storage system (HESS) and piloted via a permanent magnet synchronous motor (PMSM). The main goals are to monitor the motor using the back-stepping control and to provide an efficient power management. This has been implemented by means of the DC bus regulation using an H∞ regulator, while a classical regulator is used to ensure a super capacitor (SC) voltage allowing a maximal availability during repetitive peak power, in order to ensure an optimal power flow to the load as well as to keep the SC operation in a safe voltage range. This original algorithm has been validated through experimental results provided by a tailor made test bench including both the HESS and the vehicle emulation controlled via two dSPACE 1104 cards. Furthermore, the back-stepping controller shows good dynamic performances, where the system reaches to track perfectly the speed profile, under tolerable torque and flux ripples.

Suggested Citation

  • Sellali, M. & Betka, A. & Djerdir, A., 2020. "Power management improvement of hybrid energy storage system based on H ∞ control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 167(C), pages 478-494.
    • Handle: RePEc:eee:matcom:v:167:y:2020:i:c:p:478-494
    DOI: 10.1016/j.matcom.2019.05.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037847541930151X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2019.05.003?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mehdi Sellali & Alexandre Ravey & Achour Betka & Abdellah Kouzou & Mohamed Benbouzid & Abdesslem Djerdir & Ralph Kennel & Mohamed Abdelrahem, 2022. "Multi-Objective Optimization-Based Health-Conscious Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-17, February.

    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:eee:matcom:v:167:y:2020:i:c:p:478-494. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.