IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v11y2025i1id928.html

AI-Based Hybrid EV Assistive System for Fuel and Electric Switching

Author

Listed:
  • R Savitha
  • Alluri Venkata Satwika
  • Gandavaram Lakshmi Poojitha
  • Gundavaram Abhinaya
  • Jeevitha P

Abstract

This project presents an innovative energy management system for hybrid electric vehicles (HEVs) that optimizes fuel efficiency and reduces emissions. The system integrates advanced slope detection techniques to automatically switch between electric and fuel modes based on the vehicle's terrain and driving requirements. Additionally, an RF-based speed control mechanism is implemented to adjust vehicle speed according to designated zones, such as hospitals, schools, and accident-prone areas. The system utilizes sensors and machine learning algorithms to detect and respond to various driving conditions, ensuring improved fuel efficiency, reduced emissions, and enhanced safety. By seamlessly integrating energy management and automated speed control, this project offers a comprehensive solution for next-generation HEVs, promoting sustainable transportation and intelligent mobility.

Suggested Citation

  • R Savitha & Alluri Venkata Satwika & Gandavaram Lakshmi Poojitha & Gundavaram Abhinaya & Jeevitha P, 2025. "AI-Based Hybrid EV Assistive System for Fuel and Electric Switching," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(1), pages 2586-2596, February.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i1:id:928
    DOI: 10.32628/CSEIT251112280
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251112280
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT251112280
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/home/article/download/CSEIT251112280/CSEIT251112280
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/CSEIT251112280?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

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:jbh:ijsrcs:v11:y2025:i1:id:928. 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: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    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.