Modern Manufacturing Engineering ›› 2024, Vol. 523 ›› Issue (4): 80-86.doi: 10.16731/j.cnki.1671-3133.2024.04.011

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Co-optimization of vehicle queue speed planning and energy management with reinforcement learning

LU Bing1, LIU Teng2,3, HUO Weiwei2,3   

  1. 1 School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China;
    2 Mechanical Electrical Engineering School,Beijing Information Science & Technology University, Beijing 100192,China;
    3 Collaborative Innovation Center for Electric Vehicles,Beijing 100192,China
  • Received:2023-06-16 Online:2024-04-18 Published:2024-05-31

Abstract: In recent years,with the rapid development of intelligent transportation system,including vehicle-vehicle communication,vehicle-road communication and other short-range real-time wireless communication information,as well as road traffic information and other long-distance traffic information,so that the vehicle is able to obtain real-time knowledge of the surrounding vehicle movement as well as the traffic environment in front of it,which is conducive to improving the vehicle′s perception of the surrounding traffic environment in order to achieve reasonable travel arrangements and driving control,thus improving the vehicle performance. In order to realize the energy-saving driving of fleet in multi-signal light scenarios,a co-optimization method based on reinforcement learning for vehicle queuq speed planning and energy management was proposed.Through the SUMO platform,a scenario including five vehicles for a fleet of vehicles passing through multiple signals was established.The results show that the proposed method outperforms the traditional driving model Intelligent Driver Model (IDM) in terms of comfort,economy and efficiency.

Key words: multi-agent reinforcement learning, energy management, fuel cell vehicles, co-optimization

CLC Number: 

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