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Real-time energy management of fuel cell hybrid electric buses: Fuel cell engines friendly intersection speed planning

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  • Jinquan, Guo
  • Hongwen, He
  • Jianwei, Li
  • Qingwu, Liu

Abstract

In this paper, a novel fuel cell engines friendly real-time energy management is proposed, which considers the intersection speed planning to reduce frequent load change conditions in the process of driving. The main advantage of this energy management is that it can improve road transportation efficiency, promote the goal of minimum hydrogen consumption and extend the service life of the fuel cell engines. For the intersection speed planning method, the information of the vehicle in front of the driving route and the traffic signal light states are considered based on dynamic programming (DP). With the intersection speed planning, the corresponding control variable is applied for the model predictive control (MPC) based energy management when the bus is 100m away from the traffic light. The simulation results show that the equivalent hydrogen saving rate can improve by approximately 3.04% and reduce 3.4% idle working conditions compared with the MPC based without intersection speed planning energy management. The hardware in the loop test show that the vehicle speed can follow the target speed, and the equivalent hydrogen consumption error is within 2.5%, which meets the allowable error range.

Suggested Citation

  • Jinquan, Guo & Hongwen, He & Jianwei, Li & Qingwu, Liu, 2021. "Real-time energy management of fuel cell hybrid electric buses: Fuel cell engines friendly intersection speed planning," Energy, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221006897
    DOI: 10.1016/j.energy.2021.120440
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    References listed on IDEAS

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    Cited by:

    1. Tang, Xiaolin & Zhou, Haitao & Wang, Feng & Wang, Weida & Lin, Xianke, 2022. "Longevity-conscious energy management strategy of fuel cell hybrid electric Vehicle Based on deep reinforcement learning," Energy, Elsevier, vol. 238(PA).
    2. Jinquan, Guo & Hongwen, He & Jianwei, Li & Qingwu, Liu, 2022. "Driving information process system-based real-time energy management for the fuel cell bus to minimize fuel cell engine aging and energy consumption," Energy, Elsevier, vol. 248(C).
    3. Zhou, Jianhao & Liu, Jun & Xue, Yuan & Liao, Yuhui, 2022. "Total travel costs minimization strategy of a dual-stack fuel cell logistics truck enhanced with artificial potential field and deep reinforcement learning," Energy, Elsevier, vol. 239(PA).
    4. Chen, Zheng & Wu, Simin & Shen, Shiquan & Liu, Yonggang & Guo, Fengxiang & Zhang, Yuanjian, 2023. "Co-optimization of velocity planning and energy management for autonomous plug-in hybrid electric vehicles in urban driving scenarios," Energy, Elsevier, vol. 263(PF).
    5. Hou, Shengyan & Yin, Hai & Xu, Fuguo & Benjamín, Pla & Gao, Jinwu & Chen, Hong, 2023. "Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles," Energy, Elsevier, vol. 266(C).

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