IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i22p5721-d1521731.html
   My bibliography  Save this article

Durability Oriented Fuel Cell Electric Vehicle Energy Management Strategies Based on Vehicle Drive Cycles

Author

Listed:
  • Xin Fu

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361000, China)

  • Zengbin Fan

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361000, China)

  • Shangfeng Jiang

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361000, China)

  • Ashley Fly

    (Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK)

  • Rui Chen

    (School of Mechanical Engineering, Tianjin University, Tianjin 300072, China)

  • Yong Han

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361000, China)

  • An Xie

    (School of Materials Science and Engineering, Xiamen University of Technology, Xiamen 361000, China)

Abstract

With the increasing severity of environmental problems and energy scarcity, fuel cell electric vehicles (FCEVs), as a sustainable and efficient means of transportation, are attracting more attention. The ageing of fuel cells (FCs) has become an urgent problem with the development of FCEV. In order to prolong the lifetime of FCs, this paper builds a model of a vehicle driven by two power sources, FC and lithium battery (Lib) using AVL Cruise. A rule-based energy management strategy (EMS) is developed in Simulink to explore the optimal control strategy for the vehicle in terms of the durability of the FC. An FC ageing model is used to quantify the degradation voltage of different duty cycles. The results show that the FC engagement levels, OCV operations, and start/stop operations can affect the lifetime of the FC significantly. By optimising the EMS, the lifetime of the FC is improved by 9.47%.

Suggested Citation

  • Xin Fu & Zengbin Fan & Shangfeng Jiang & Ashley Fly & Rui Chen & Yong Han & An Xie, 2024. "Durability Oriented Fuel Cell Electric Vehicle Energy Management Strategies Based on Vehicle Drive Cycles," Energies, MDPI, vol. 17(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5721-:d:1521731
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/22/5721/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/22/5721/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Kui & Laghrouche, Salah & Djerdir, Abdesslem, 2019. "Degradation model of proton exchange membrane fuel cell based on a novel hybrid method," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    2. Tang, Xingwang & Zhang, Yujia & Xu, Sichuan, 2023. "Experimental study of PEM fuel cell temperature characteristic and corresponding automated optimal temperature calibration model," Energy, Elsevier, vol. 283(C).
    3. Zhuang Tian & Zheng Wei & Jinhui Wang & Yinxiang Wang & Yuwei Lei & Ping Hu & S. M. Muyeen & Daming Zhou, 2023. "Research Progress on Aging Prediction Methods for Fuel Cells: Mechanism, Methods, and Evaluation Criteria," Energies, MDPI, vol. 16(23), pages 1-21, November.
    4. Qiu, Diankai & Peng, Linfa & Liang, Peng & Yi, Peiyun & Lai, Xinmin, 2018. "Mechanical degradation of proton exchange membrane along the MEA frame in proton exchange membrane fuel cells," Energy, Elsevier, vol. 165(PB), pages 210-222.
    5. 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).
    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. Bin Huang & Wenbin Yu & Minrui Ma & Xiaoxu Wei & Guangya Wang, 2025. "Artificial-Intelligence-Based Energy Management Strategies for Hybrid Electric Vehicles: A Comprehensive Review," Energies, MDPI, vol. 18(14), pages 1-42, July.

    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. Krystof Foniok & Lubomira Drozdova & Lukas Prokop & Filip Krupa & Pavel Kedron & Vojtech Blazek, 2025. "Mechanisms and Modelling of Effects on the Degradation Processes of a Proton Exchange Membrane (PEM) Fuel Cell: A Comprehensive Review," Energies, MDPI, vol. 18(8), pages 1-64, April.
    2. Deng, Zhihua & Chan, Siew Hwa & Chen, Qihong & Liu, Hao & Zhang, Liyan & Zhou, Keliang & Tong, Sirui & Fu, Zhichao, 2023. "Efficient degradation prediction of PEMFCs using ELM-AE based on fuzzy extension broad learning system," Applied Energy, Elsevier, vol. 331(C).
    3. Cha, Dowon & Yang, Wonseok & Kim, Yongchan, 2019. "Performance improvement of self-humidifying PEM fuel cells using water injection at various start-up conditions," Energy, Elsevier, vol. 183(C), pages 514-524.
    4. Lei, Nuo & Zhang, Hao & Hu, Jingjing & Hu, Zunyan & Wang, Zhi, 2025. "Sim-to-real design and development of reinforcement learning-based energy management strategies for fuel cell electric vehicles," Applied Energy, Elsevier, vol. 393(C).
    5. Olaf Dybiński & Tomasz Kurkus & Lukasz Szablowski & Arkadiusz Szczęśniak & Jaroslaw Milewski & Aliaksandr Martsinchyk & Pavel Shuhayeu, 2025. "Artificial Neural Network-Based Mathematical Model of Methanol Steam Reforming on the Anode of Molten Carbonate Fuel Cell Based on Experimental Research," Energies, MDPI, vol. 18(11), pages 1-17, June.
    6. Yousefi Tehrani, Mehran & Mirfarsi, Seyed Hesam & Rowshanzamir, Soosan, 2022. "Mechanical stress and strain investigation of sulfonated Poly(ether ether ketone) proton exchange membrane in fuel cells: A numerical study," Renewable Energy, Elsevier, vol. 184(C), pages 182-200.
    7. Guarino, Antonio & Trinchero, Riccardo & Canavero, Flavio & Spagnuolo, Giovanni, 2022. "A fast fuel cell parametric identification approach based on machine learning inverse models," Energy, Elsevier, vol. 239(PC).
    8. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    9. 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).
    10. Kofler, Sandro & Jakubek, Stefan & Hametner, Christoph, 2025. "Predictive energy management strategy with optimal stack start/stop control for fuel cell vehicles," Applied Energy, Elsevier, vol. 377(PB).
    11. Shi, Ting & Wang, Huaiyu & Yang, Wenming & Peng, Xueyuan, 2024. "Mathematical modeling and optimization of gas foil bearings-rotor system in hydrogen fuel cell vehicles," Energy, Elsevier, vol. 290(C).
    12. Huang, Ruchen & He, Hongwen & Su, Qicong & Härtl, Martin & Jaensch, Malte, 2025. "Type- and task-crossing energy management for fuel cell vehicles with longevity consideration: A heterogeneous deep transfer reinforcement learning framework," Applied Energy, Elsevier, vol. 377(PC).
    13. Heuts, Y.J.J. & Wouters, J.J.F. & Hulsebos, O.F. & Donkers, M.C.F., 2025. "Modeling, implementation and experimental verification of eco-driving on a battery-electric heavy-duty vehicle," Applied Energy, Elsevier, vol. 390(C).
    14. Yu, Yulong & Zheng, Qiang & Zhang, Tianyi & Li, Zhengyan & Chen, Lei & Tao, Wen-Quan, 2025. "Forecasting the output performance of PEMFCs via a novel deep learning framework considering varying operating conditions and time scales," Applied Energy, Elsevier, vol. 389(C).
    15. Abdeldjalil Djouahi & Belkhir Negrou & Boubakeur Rouabah & Abdelbasset Mahboub & Mohamed Mahmoud Samy, 2023. "Optimal Sizing of Battery and Super-Capacitor Based on the MOPSO Technique via a New FC-HEV Application," Energies, MDPI, vol. 16(9), pages 1-18, May.
    16. Badji, Abderrezak & Abdeslam, Djaffar Ould & Chabane, Djafar & Benamrouche, Nacereddine, 2022. "Real-time implementation of improved power frequency approach based energy management of fuel cell electric vehicle considering storage limitations," Energy, Elsevier, vol. 249(C).
    17. Pan, Mingzhang & Pan, Chengjie & Li, Chao & Zhao, Jian, 2021. "A review of membranes in proton exchange membrane fuel cells: Transport phenomena, performance and durability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    18. Guorong Li & Chen Song & Guang Han & Zihao Chen & Jiwei Huang, 2024. "Optimization of Hydrogen Production System Performance Using Photovoltaic/Thermal-Coupled PEM," Energies, MDPI, vol. 17(21), pages 1-22, October.
    19. Tao, Zihan & Zhang, Chu & Xiong, Jinlin & Hu, Haowen & Ji, Jie & Peng, Tian & Nazir, Muhammad Shahzad, 2023. "Evolutionary gate recurrent unit coupling convolutional neural network and improved manta ray foraging optimization algorithm for performance degradation prediction of PEMFC," Applied Energy, Elsevier, vol. 336(C).
    20. Kandidayeni, M. & Macias, A. & Boulon, L. & Kelouwani, S., 2020. "Investigating the impact of ageing and thermal management of a fuel cell system on energy management strategies," Applied Energy, Elsevier, vol. 274(C).

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jeners:v:17:y:2024:i:22:p:5721-:d:1521731. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.