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Total travel costs minimization strategy of a dual-stack fuel cell logistics truck enhanced with artificial potential field and deep reinforcement learning

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  • Zhou, Jianhao
  • Liu, Jun
  • Xue, Yuan
  • Liao, Yuhui

Abstract

To fulfill the increasing power level of fuel cell, a self-adaptive energy management strategy (EMS) with considerations of the efficiency and health of dual-stack fuel cell (DFC) and the total traveling costs for a logistics truck is proposed. The virtual attractive/repulsive forces generated by artificial potential field (APF) functions are applied to DFC and battery system as performance regulator in order to guarantee the efficiency of DFC and the maintenance of SOC. Deep reinforcement learning algorithm, namely deep deterministic policy gradient (DDPG), is leveraged to automatically adjust the virtual force exerted to APF functions in order to assist the power allocation between various energy sources. In comparison to identical power allocation via equivalent hydrogen consumption minimization strategy, APF function generated uneven power distribution of DFC by prohibiting high/low current and frequently start/stop operations of single fuel cell, especially under charge depletion stage. Meanwhile, DDPG-tuner is effective to soften the interaction effect between DFC and battery while meeting the multi-objectives of the EMS. The proposed EMS in cooperation of APF function and DDPG tuner is expected to cope with the dynamic price fluctuation of various energy sources and beneficial to reduce total travel costs as well as extend the DFC's longevity.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pa:s0360544221021149
    DOI: 10.1016/j.energy.2021.121866
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    References listed on IDEAS

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    1. Liu, Jiaran & Tan, Jinzhu & Yang, Weizhan & Li, Yang & Wang, Chao, 2021. "Better electrochemical performance of PEMFC under a novel pneumatic clamping mechanism," Energy, Elsevier, vol. 229(C).
    2. Erdinc, O. & Uzunoglu, M., 2010. "Recent trends in PEM fuel cell-powered hybrid systems: Investigation of application areas, design architectures and energy management approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2874-2884, December.
    3. Sulaiman, N. & Hannan, M.A. & Mohamed, A. & Ker, P.J. & Majlan, E.H. & Wan Daud, W.R., 2018. "Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 2061-2079.
    4. Lin, Xinyou & Xia, Yutian & Huang, Wei & Li, Hailin, 2021. "Trip distance adaptive power prediction control strategy optimization for a Plug-in Fuel Cell Electric Vehicle," Energy, Elsevier, vol. 224(C).
    5. Jinquan, Guo & Hongwen, He & Jiankun, Peng & Nana, Zhou, 2019. "A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 175(C), pages 378-392.
    6. Cai, Yonghua & Wu, Di & Sun, Jingming & Chen, Ben, 2021. "The effect of cathode channel blockages on the enhanced mass transfer and performance of PEMFC," Energy, Elsevier, vol. 222(C).
    7. Zhang, Cheng & Allafi, Walid & Dinh, Quang & Ascencio, Pedro & Marco, James, 2018. "Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique," Energy, Elsevier, vol. 142(C), pages 678-688.
    8. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    9. Zeng, Tao & Zhang, Caizhi & Zhang, Yanyi & Deng, Chenghao & Hao, Dong & Zhu, Zhongwen & Ran, Hongxu & Cao, Dongpu, 2021. "Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle," Energy, Elsevier, vol. 227(C).
    10. 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).
    11. Olabi, A.G. & Wilberforce, Tabbi & Abdelkareem, Mohammad Ali, 2021. "Fuel cell application in the automotive industry and future perspective," Energy, Elsevier, vol. 214(C).
    Full references (including those not matched with items on IDEAS)

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    2. Mojgan Fayyazi & Paramjotsingh Sardar & Sumit Infent Thomas & Roonak Daghigh & Ali Jamali & Thomas Esch & Hans Kemper & Reza Langari & Hamid Khayyam, 2023. "Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles," Sustainability, MDPI, vol. 15(6), pages 1-38, March.
    3. Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).

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