IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v306y2024ics0360544224023053.html
   My bibliography  Save this article

Deep reinforcement learning with deep-Q-network based energy management for fuel cell hybrid electric truck

Author

Listed:
  • Wang, Zhifu
  • Zhang, Shunshun
  • Luo, Wei
  • Xu, Song

Abstract

To better utilize hydrogen energy from fuel cell electric vehicles (FCEVs), an investigation is conducted into how a proton exchange membrane fuel cell (PEMFC) hybrid vehicle's 100-km hydrogen consumption rate is affected by a dynamic programming-based energy management method. The Dueling Deep Q-Network (Dueling DQN) algorithm's energy management approach is proposed. The DQN algorithm is optimized to increase the method's stability and quicken its pace of convergence. The simulation results show that the fuel economy of DQN and Dueling DQN algorithm are 94.28 % and 95.7 % respectively, both of which are improved, while the Dueling DQN algorithm converges at 480 steps higher than the DQN algorithm converges at 800 steps, and the algorithm comparison verifies the two algorithms' The validity of both algorithms was verified by algorithm comparison. In China Truck Driving Conditions (CHTC-LT), the hardware-in-the-loop simulation of CAN communication protocol using NI-PXI hardware-in-the-loop test system achieves the target vehicle speed following and verifies the real-time performance of the strategy.

Suggested Citation

  • Wang, Zhifu & Zhang, Shunshun & Luo, Wei & Xu, Song, 2024. "Deep reinforcement learning with deep-Q-network based energy management for fuel cell hybrid electric truck," Energy, Elsevier, vol. 306(C).
    • Handle: RePEc:eee:energy:v:306:y:2024:i:c:s0360544224023053
    DOI: 10.1016/j.energy.2024.132531
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224023053
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.132531?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. İnci, Mustafa & Büyük, Mehmet & Demir, Mehmet Hakan & İlbey, Göktürk, 2021. "A review and research on fuel cell electric vehicles: Topologies, power electronic converters, energy management methods, technical challenges, marketing and future aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    2. Renzhi Lyu & Zhenpo Wang & Zhaosheng Zhang, 2024. "Multi-Objective Optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern Recognition," Energies, MDPI, vol. 17(6), pages 1-15, March.
    3. Ganesh, Akhil Hannegudda & Xu, Bin, 2022. "A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. He, Hongwen & Meng, Xiangfei & Wang, Yong & Khajepour, Amir & An, Xiaowen & Wang, Renguang & Sun, Fengchun, 2024. "Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    2. Sumitkumar, Rathor & Al-Sumaiti, Ameena Saad, 2024. "Shared autonomous electric vehicle: Towards social economy of energy and mobility from power-transportation nexus perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    3. Matteo Acquarone & Claudio Maino & Daniela Misul & Ezio Spessa & Antonio Mastropietro & Luca Sorrentino & Enrico Busto, 2023. "Influence of the Reward Function on the Selection of Reinforcement Learning Agents for Hybrid Electric Vehicles Real-Time Control," Energies, MDPI, vol. 16(6), pages 1-22, March.
    4. Youssef Amry & Elhoussin Elbouchikhi & Franck Le Gall & Mounir Ghogho & Soumia El Hani, 2022. "Electric Vehicle Traction Drives and Charging Station Power Electronics: Current Status and Challenges," Energies, MDPI, vol. 15(16), pages 1-30, August.
    5. Ma, Yan & Hu, Fuyuan & Hu, Yunfeng, 2023. "Energy efficiency improvement of intelligent fuel cell/battery hybrid vehicles through an integrated management strategy," Energy, Elsevier, vol. 263(PE).
    6. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
    7. Georgios Varvoutis & Athanasios Lampropoulos & Evridiki Mandela & Michalis Konsolakis & George E. Marnellos, 2022. "Recent Advances on CO 2 Mitigation Technologies: On the Role of Hydrogenation Route via Green H 2," Energies, MDPI, vol. 15(13), pages 1-38, June.
    8. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).
    9. Pang, Kexin & Zhou, Jian & Tsianikas, Stamatis & Coit, David W. & Ma, Yizhong, 2024. "Long-term microgrid expansion planning with resilience and environmental benefits using deep reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    10. Hu, Dong & Huang, Chao & Yin, Guodong & Li, Yangmin & Huang, Yue & Huang, Hailong & Wu, Jingda & Li, Wenfei & Xie, Hui, 2024. "A transfer-based reinforcement learning collaborative energy management strategy for extended-range electric buses with cabin temperature comfort consideration," Energy, Elsevier, vol. 290(C).
    11. Zhou, Hongxu & Yu, Zhongwei & Wu, Xiaohua & Fan, Zhanfeng & Yin, Xiaofeng & Zhou, Lingxue, 2023. "Dynamic programming improved online fuzzy power distribution in a demonstration fuel cell hybrid bus," Energy, Elsevier, vol. 284(C).
    12. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    13. Zhao, Wenbin & Mi, Shijie & Wu, Haoqing & Zhang, Yaoyuan & He, Zhuoyao & Qian, Yong & Lu, Xingcai, 2022. "Towards a comprehensive understanding of mode transition between biodiesel-biobutanol dual-fuel ICCI low temperature combustion and conventional CI combustion - Part ΙΙ: A system optimization at low l," Energy, Elsevier, vol. 241(C).
    14. Tang, Wenbin & Wang, Yaqian & Jiao, Xiaohong & Ren, Lina, 2023. "Hierarchical energy management strategy based on adaptive dynamic programming for hybrid electric vehicles in car-following scenarios," Energy, Elsevier, vol. 265(C).
    15. Wu, Jingda & Huang, Chao & He, Hongwen & Huang, Hailong, 2024. "Confidence-aware reinforcement learning for energy management of electrified vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    16. Fiammetta Rita Bianchi & Barbara Bosio, 2021. "Operating Principles, Performance and Technology Readiness Level of Reversible Solid Oxide Cells," Sustainability, MDPI, vol. 13(9), pages 1-23, April.
    17. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    18. Zou, Weitao & Li, Jianwei & Yang, Qingqing & Wan, Xinming & He, Yuntang & Lan, Hao, 2023. "A real-time energy management approach with fuel cell and battery competition-synergy control for the fuel cell vehicle," Applied Energy, Elsevier, vol. 334(C).
    19. Hao, Xinyang & Salhi, Issam & Laghrouche, Salah & Ait Amirat, Youcef & Djerdir, Abdesslem, 2023. "Multiple inputs multi-phase interleaved boost converter for fuel cell systems applications," Renewable Energy, Elsevier, vol. 204(C), pages 521-531.
    20. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:306:y:2024:i:c:s0360544224023053. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.