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Guided control for plug-in fuel cell hybrid electric vehicles via vehicle to traffic communication

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  • Wei, Xiaodong
  • Wang, Jiaqi
  • Sun, Chao
  • Liu, Bo
  • Huo, Weiwei
  • Sun, Fengchun

Abstract

A guided proportional integral controller-based Pontryagin’s minimum principle (PI-PMP) energy management control strategy based on dynamic traffic information for plug-in fuel cell vehicles is proposed in this paper. Combined with the real-world traffic flow data of high-way driving scenarios, an improved equivalent consumption minimization strategy (ECMS) based on dichotomy is used to quickly search for the optimal initial costate value and reference battery state of charge (SOC) trajectory. A horizon velocity predictor based on artificial neural networks (ANNs) is used to achieve short-term velocity prediction, and a PI-PMP control strategy is adopted to realize SOC trajectory following. Simultaneously, the sensitivity of the costate in ECMS and PI-PMP is analyzed in depth. The simulation results of three scenarios with no/static/dynamic traffic flow information show that the guided PI-PMP energy management strategy based on dynamic traffic flow information has a significant energy-saving effect and real-time optimization potential.

Suggested Citation

  • Wei, Xiaodong & Wang, Jiaqi & Sun, Chao & Liu, Bo & Huo, Weiwei & Sun, Fengchun, 2023. "Guided control for plug-in fuel cell hybrid electric vehicles via vehicle to traffic communication," Energy, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:energy:v:267:y:2023:i:c:s0360544222033552
    DOI: 10.1016/j.energy.2022.126469
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    References listed on IDEAS

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    1. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    2. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    3. Liu, Bo & Sun, Chao & Wang, Bo & Liang, Weiqiang & Ren, Qiang & Li, Junqiu & Sun, Fengchun, 2022. "Bi-level convex optimization of eco-driving for connected Fuel Cell Hybrid Electric Vehicles through signalized intersections," Energy, Elsevier, vol. 252(C).
    4. Chen, Zhihang & Liu, Yonggang & Zhang, Yuanjian & Lei, Zhenzhen & Chen, Zheng & Li, Guang, 2022. "A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles," Energy, Elsevier, vol. 243(C).
    5. Zhou, Wei & Chen, Yaoqi & Zhai, Haoran & Zhang, Weigang, 2021. "Predictive energy management for a plug-in hybrid electric vehicle using driving profile segmentation and energy-based analytical SoC planning," Energy, Elsevier, vol. 220(C).
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    Cited by:

    1. Chen, Xu & Li, Mince & Chen, Zonghai, 2023. "Meta rule-based energy management strategy for battery/supercapacitor hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    2. Ren, Xiaoxia & Ye, Jinze & Xie, Liping & Lin, Xinyou, 2024. "Battery longevity-conscious energy management predictive control strategy optimized by using deep reinforcement learning algorithm for a fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 286(C).
    3. Guo, Ningyuan & Zhang, Wencan & Li, Junqiu & Chen, Zheng & Li, Jianwei & Sun, Chao, 2024. "Predictive energy management of fuel cell plug-in hybrid electric vehicles: A co-state boundaries-oriented PMP optimization approach," Applied Energy, Elsevier, vol. 362(C).

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