IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v314y2025ics0360544224040039.html
   My bibliography  Save this article

A novel eco-driving strategy for heterogeneous vehicle platooning with risk prediction and deep reinforcement learning

Author

Listed:
  • Sun, Xiaosong
  • Lu, Yongjie
  • Zheng, Lufeng
  • Li, Haoyu
  • Zhang, Xiaoting
  • Yang, Qi

Abstract

—Eco-driving control holds significant potential for energy savings in vehicle platooning. However, the development of energy-saving strategies is hampered by complex traffic scenarios and the heterogeneity of vehicles within the platoon. This study proposes a novel eco-driving approach that comprehensively accounts for the dynamic uncertainty of obstacles in avoidance scenarios and the heterogeneity between the lead vehicle and follow-up vehicles. First, the dynamic and powertrain models for the follow-up vehicles are constructed, and a rule-based risk prediction model is designed to predict the risk level during platoon obstacle avoidance. Besides, a deep reinforcement learning-based controller is developed to optimize longitudinal speed, balancing objectives such as driving efficiency, safety, and energy consumption, thereby reducing hydrogen consumption while ensuring driving safety and driving efficiency. The simulation results indicate that the proposed eco-driving approach eliminates the impact of heterogeneity within the platoon, achieving hydrogen consumption savings of 1.47 %–4.79 % under complex obstacle avoidance scenarios.

Suggested Citation

  • Sun, Xiaosong & Lu, Yongjie & Zheng, Lufeng & Li, Haoyu & Zhang, Xiaoting & Yang, Qi, 2025. "A novel eco-driving strategy for heterogeneous vehicle platooning with risk prediction and deep reinforcement learning," Energy, Elsevier, vol. 314(C).
  • Handle: RePEc:eee:energy:v:314:y:2025:i:c:s0360544224040039
    DOI: 10.1016/j.energy.2024.134225
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224040039
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2024.134225?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.

    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:energy:v:314:y:2025:i:c:s0360544224040039. 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/energy .

    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.