IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/3106329.html
   My bibliography  Save this article

Vehicle Running State Estimation by Adaptive Soft-Sensing Algorithm

Author

Listed:
  • Liang Hao
  • Lixin Guo
  • Shuwei Liu

Abstract

Vehicle running state adaptive unscented Kalman filter soft-sensing algorithm is put forward in this paper based on traditional UKF which can estimate vehicle running state parameters and suboptimal Sage-Husa noise estimator which can effectively solve the problem of noises varying with time. Meanwhile 3-DOF dynamic model of vehicle and HSRI tire model are established. So vehicle running state can be accurately estimated by fusing the low-cost measurement information of longitudinal and lateral acceleration and handwheel steering angle. Under the typical working condition, AUKF soft-sensing algorithm is verified with substantial vehicle tests. Comparing with UKF soft-sensing algorithm, the result indicates AUKF soft-sensing algorithm has a good performance in robustness and is able to realize the effective estimation of vehicle running state more precisely than UKF soft-sensing algorithm.

Suggested Citation

  • Liang Hao & Lixin Guo & Shuwei Liu, 2018. "Vehicle Running State Estimation by Adaptive Soft-Sensing Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-9, November.
    • Handle: RePEc:hin:jnlmpe:3106329
    DOI: 10.1155/2018/3106329
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2018/3106329.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2018/3106329.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/3106329?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:3106329. 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.