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Research on Speed Planning and Energy Management Strategy for Fuel Cell Hybrid Bus in Green Wave Scenarios at Traffic Light Intersections Based on Deep Reinforcement Learning

Author

Listed:
  • Fengyan Yi

    (School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China)

  • Wei Guo

    (School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China)

  • Hongtao Gong

    (School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China)

  • Yang Shen

    (School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China)

  • Jiaming Zhou

    (School of Intelligent Manufacturing, Weifang University of Science and Technology, Weifang 262700, China)

  • Wenhao Yu

    (School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China)

  • Dagang Lu

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Chunchun Jia

    (Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Caizhi Zhang

    (The State Key Laboratory of Mechanical Transmissions, School of Mechanical and Vehicle Engineering, Chongqing Automotive Collaborative Innovation Centre, Chongqing University, Chongqing 400044, China)

  • Farui Gong

    (School of Intelligent Manufacturing, Weifang University of Science and Technology, Weifang 262700, China)

Abstract

In the context of intelligent and connected transportation, obtaining the real-time vehicle status and comprehensive traffic data is crucial for addressing challenges related to speed optimization and energy regulation in intricate transportation situations. This paper introduces a control method for the speed optimization and energy management of a fuel cell hybrid bus (FCHB) based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The strategy framework is built on a dual-objective optimization deep reinforcement learning (D-DRL) architecture, which integrates traffic signal information into the energy management framework, in addition to conventional state spaces to guide control decisions. The aim is to achieve “green wave” traffic while minimizing hydrogen consumption. To validate the effectiveness of the proposed strategy, simulation tests were conducted using the SUMO platform. The results show that in terms of speed planning, the difference between the maximum and minimum speeds of the FCHB was reduced by 21.66% compared with the traditional Intelligent Driver Model (IDM), while the acceleration and its variation were reduced by 8.89% and 13.21%, respectively. In terms of the hydrogen fuel efficiency, the proposed strategy achieved 95.71% of the performance level of the dynamic programming (DP) algorithm. The solution proposed in this paper is of great significance for improving passenger comfort and FCHB economy.

Suggested Citation

  • Fengyan Yi & Wei Guo & Hongtao Gong & Yang Shen & Jiaming Zhou & Wenhao Yu & Dagang Lu & Chunchun Jia & Caizhi Zhang & Farui Gong, 2024. "Research on Speed Planning and Energy Management Strategy for Fuel Cell Hybrid Bus in Green Wave Scenarios at Traffic Light Intersections Based on Deep Reinforcement Learning," Sustainability, MDPI, vol. 16(24), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:24:p:11156-:d:1547741
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    References listed on IDEAS

    as
    1. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang & Shi, Man, 2023. "A novel energy management strategy for hybrid electric bus with fuel cell health and battery thermal- and health-constrained awareness," Energy, Elsevier, vol. 271(C).
    2. Peng, Jiankun & Shen, Yang & Wu, ChangCheng & Wang, Chunhai & Yi, Fengyan & Ma, Chunye, 2023. "Research on energy-saving driving control of hydrogen fuel bus based on deep reinforcement learning in freeway ramp weaving area," Energy, Elsevier, vol. 285(C).
    3. Lu, Dagang & Yi, Fengyan & Hu, Donghai & Li, Jianwei & Yang, Qingqing & Wang, Jing, 2023. "Online optimization of energy management strategy for FCV control parameters considering dual power source lifespan decay synergy," Applied Energy, Elsevier, vol. 348(C).
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