IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v290y2024ics0360544223035405.html
   My bibliography  Save this article

Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information

Author

Listed:
  • Jia, Chunchun
  • Zhou, Jiaming
  • He, Hongwen
  • Li, Jianwei
  • Wei, Zhongbao
  • Li, Kunang

Abstract

The escalating level of vehicle electrification and intelligence makes higher requirements for the energy management strategy (EMS) of fuel cell vehicles. Environmental and road conditions can significantly influence the power demand of the load, thereby affecting the lifespan and efficiency of vehicular energy systems. To ensure that the vehicle is always in optimal working condition, this study innovatively proposes a health-conscious EMS framework based on twin delayed deep deterministic policy gradient (TD3) algorithm for fuel cell hybrid electric bus (FCHEB). First, the environment and look-ahead road information obtained through vehicle sensors, GPS and Geographic Information System is used to establish the energy management problem formulation. Secondly, a TD3-based data-driven EMS is developed with the objective of optimizing hydrogen fuel economy, fuel cell durability and battery thermal health status. Finally, the strategy validation is performed in a developed validation environment that contains terrain information, ambient temperature, and real-world collected driving conditions. The validation results indicate that compared to the state-of-the-art TD3-based EMS, the proposed EMS can improve battery life by 28.02 % and overall vehicle economy by 8.92 %.

Suggested Citation

  • Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information," Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223035405
    DOI: 10.1016/j.energy.2023.130146
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223035405
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.130146?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. Maroto Estrada, Pedro & de Lima, Daniela & Bauer, Peter H. & Mammetti, Marco & Bruno, Joan Carles, 2023. "Deep learning in the development of energy Management strategies of hybrid electric Vehicles: A hybrid modeling approach," Applied Energy, Elsevier, vol. 329(C).
    2. Khayyam, Hamid & Bab-Hadiashar, Alireza, 2014. "Adaptive intelligent energy management system of plug-in hybrid electric vehicle," Energy, Elsevier, vol. 69(C), pages 319-335.
    3. Lin, Xinyou & Xu, Xinhao & Wang, Zhaorui, 2022. "Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle," Applied Energy, Elsevier, vol. 321(C).
    4. Zhang, Kaixuan & Ruan, Jiageng & Li, Tongyang & Cui, Hanghang & Wu, Changcheng, 2023. "The effects investigation of data-driven fitting cycle and deep deterministic policy gradient algorithm on energy management strategy of dual-motor electric bus," Energy, Elsevier, vol. 269(C).
    5. 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).
    6. Ma, Suhui & Qin, Yanzhou & Liu, Yuwen & Sun, Liancheng & Guo, Qiaoyu & Yin, Yan, 2022. "Delamination evolution of PEM fuel cell membrane/CL interface under asymmetric RH cycling and CL crack location," Applied Energy, Elsevier, vol. 310(C).
    7. 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).
    8. 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).
    9. Ajanovic, A. & Glatt, A. & Haas, R., 2021. "Prospects and impediments for hydrogen fuel cell buses," Energy, Elsevier, vol. 235(C).
    10. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wojciech Lewicki & Milena Bera & Monika Śpiewak-Szyjka, 2024. "The Correlation of the Smart City Concept with the Costs of Toxic Exhaust Gas Emissions Based on the Analysis of a Selected Population of Motor Vehicles in Urban Traffic," Energies, MDPI, vol. 17(21), pages 1-19, October.
    2. Na Yeon An & Jung Hyun Yang & Eunyong Song & Sung-Ho Hwang & Hyung-Gi Byun & Sanguk Park, 2024. "Digital Twin-Based Hydrogen Refueling Station (HRS) Safety Model: CNN-Based Decision-Making and 3D Simulation," Sustainability, MDPI, vol. 16(21), pages 1-26, October.

    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. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2023. "A novel health-aware deep reinforcement learning energy management for fuel cell bus incorporating offline high-quality experience," Energy, Elsevier, vol. 282(C).
    2. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Learning-based model predictive energy management for fuel cell hybrid electric bus with health-aware control," Applied Energy, Elsevier, vol. 355(C).
    3. Jia, Chunchun & Li, Kunang & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao, 2023. "Health-aware energy management strategy for fuel cell hybrid bus considering air-conditioning control based on TD3 algorithm," Energy, Elsevier, vol. 283(C).
    4. 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.
    5. Li, Yanfei & Taghizadeh-Hesary, Farhad, 2022. "The economic feasibility of green hydrogen and fuel cell electric vehicles for road transport in China," Energy Policy, Elsevier, vol. 160(C).
    6. Ku, Donggyun & Choi, Minje & Yoo, Nakyoung & Shin, Seungheon & Lee, Seungjae, 2021. "A new algorithm for eco-friendly path guidance focused on electric vehicles," Energy, Elsevier, vol. 233(C).
    7. 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).
    8. Angel Recalde & Ricardo Cajo & Washington Velasquez & Manuel S. Alvarez-Alvarado, 2024. "Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review," Energies, MDPI, vol. 17(13), pages 1-39, June.
    9. Hegazy, Omar & Barrero, Ricardo & Van den Bossche, Peter & El Baghdadi, Mohamed & Smekens, Jelle & Van Mierlo, Joeri & Vriens, Wouter & Bogaerts, Bruno, 2016. "Modeling, analysis and feasibility study of new drivetrain architectures for off-highway vehicles," Energy, Elsevier, vol. 109(C), pages 1056-1074.
    10. Du, Jiuyu & Chen, Jingfu & Song, Ziyou & Gao, Mingming & Ouyang, Minggao, 2017. "Design method of a power management strategy for variable battery capacities range-extended electric vehicles to improve energy efficiency and cost-effectiveness," Energy, Elsevier, vol. 121(C), pages 32-42.
    11. Ding, Peishan & Zheng, Xiaotao & Chen, Haofeng & Tu, Shantung, 2024. "Ratcheting assessment of the catalyst layer in polymer electrolyte membrane fuel cells considering thermal-mechanical-humidity cycling," Applied Energy, Elsevier, vol. 357(C).
    12. Peng Xu & Qixing Liu & Yuhu Wu, 2023. "Energy Saving-Oriented Multi-Depot Vehicle Routing Problem with Time Windows in Disaster Relief," Energies, MDPI, vol. 16(4), pages 1-15, February.
    13. Zhao, Lei & Yuan, Hao & Xie, Jiaping & Jiang, Shangfeng & Wei, Xuezhe & Tang, Wei & Ming, Pingwen & Dai, Haifeng, 2023. "Inconsistency evaluation of vehicle-oriented fuel cell stacks based on electrochemical impedance under dynamic operating conditions," Energy, Elsevier, vol. 265(C).
    14. Zhang, Nan & Lu, Yiji & Kadam, Sambhaji & Yu, Zhibin, 2023. "A fuel cell range extender integrating with heat pump for cabin heat and power generation," Applied Energy, Elsevier, vol. 348(C).
    15. Nithin Isaac & Akshay Kumar Saha, 2023. "Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling," Sustainability, MDPI, vol. 15(18), pages 1-16, September.
    16. Ruoxi Pan & Yiping Liang & Yifei Li & Kai Zhou & Jiarui Miao, 2023. "Environmental and Health Benefits of Promoting New Energy Vehicles: A Case Study Based on Chongqing City," Sustainability, MDPI, vol. 15(12), pages 1-16, June.
    17. Zeyu Chen & Jiahuan Lu & Bo Liu & Nan Zhou & Shijie Li, 2020. "Optimal Energy Management of Plug-In Hybrid Electric Vehicles Concerning the Entire Lifespan of Lithium-Ion Batteries," Energies, MDPI, vol. 13(10), pages 1-15, May.
    18. Ramesh Kumar Chidambaram & Dipankar Chatterjee & Barnali Barman & Partha Pratim Das & Dawid Taler & Jan Taler & Tomasz Sobota, 2023. "Effect of Regenerative Braking on Battery Life," Energies, MDPI, vol. 16(14), pages 1-24, July.
    19. Farzaneh, Alireza & Farjah, Ebrahim, 2018. "Analysis of Road Curvature’s Effects on Electric Motorcycle Energy Consumption," Energy, Elsevier, vol. 151(C), pages 160-166.
    20. Chen, Fujun & Wang, Bowen & Ni, Meng & Gong, Zhichao & Jiao, Kui, 2024. "Online energy management strategy for ammonia-hydrogen hybrid electric vehicles harnessing deep reinforcement learning," Energy, Elsevier, vol. 301(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:290:y:2024:i:c:s0360544223035405. 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.