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Collaborative optimization of velocity planning and energy management for fuel cell hybrid buses at multiple intersections and bus stations

Author

Listed:
  • Wu, Xiaohua
  • Ma, Pengfei
  • Zhou, Lingxue
  • Fan, Zhanfeng
  • Zhu, Qingbo
  • Yin, Xiaofeng
  • Ouyang, Quan

Abstract

With the rapid advancement of intelligent transportation systems, a unique opportunity has emerged for optimizing energy management for hybrid electric vehicle. This paper presents a collaborative optimization method that integrates velocity planning and energy management for fuel cell hybrid buses (FCHBs) at multiple intersections and bus stations. First, an energy-saving velocity planning strategy (EVPS) is developed based on minimizing equivalent hydrogen consumption. EVPS exhibits significant advantages with respect to time savings, stopping duration, time savings rate, and overall pass efficiency at signalized intersections compared to real-based driving scenarios and modified intelligent driver model-based velocity planning strategies. Furthermore, a Q-learning-based energy management strategy (EMS) is proposed to enhance the energy efficiency and longevity of power sources in FCHBs. The EVPS is seamlessly integrated with the Q-learning-based EMS to facilitate eco-driving for the FCHBs. Finally, the effectiveness of this collaborative approach combining EVPS and EMS is experimentally validated through three real demonstration drive cycles involving FCHBs. The results indicate that the proposed EVPS-based EMS can achieve an impressive reduction in equivalent hydrogen consumption by over 21.35%.

Suggested Citation

  • Wu, Xiaohua & Ma, Pengfei & Zhou, Lingxue & Fan, Zhanfeng & Zhu, Qingbo & Yin, Xiaofeng & Ouyang, Quan, 2025. "Collaborative optimization of velocity planning and energy management for fuel cell hybrid buses at multiple intersections and bus stations," Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:energy:v:326:y:2025:i:c:s0360544225015543
    DOI: 10.1016/j.energy.2025.135912
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    References listed on IDEAS

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