IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v376y2024ipas0306261924015666.html
   My bibliography  Save this article

Comparative analysis and test bench validation of energy management methods for a hybrid marine propulsion system powered by batteries and solid oxide fuel cells

Author

Listed:
  • Ünlübayir, Cem
  • Youssfi, Hiba
  • Khan, Rehan Ahmad
  • Ventura, Santiago Salas
  • Fortunati, Daniele
  • Rinner, Jonas
  • Börner, Martin Florian
  • Quade, Katharina Lilith
  • Ringbeck, Florian
  • Sauer, Dirk Uwe

Abstract

Climate protection goals and the transformation of the mobility sector are pushing the shipping industry to develop new propulsion systems emitting fewer or no greenhouse gases. One promising approach to eliminate greenhouse gas emissions from ships is a hybrid propulsion system powered by fuel cells and batteries. A high-temperature solid oxide fuel cell (SOFC) can supply heat and the electrical power demand in combination with a battery. Due to the low dynamic performance of the SOFC when faced with sudden load changes, a battery is responsible for providing the power for the dynamic load components. To ensure the resource-efficient operation of the propulsion components, intelligent energy management methods are required for power distribution control. Implementing a machine-learning-based energy management method based on twin-delayed deep deterministic policy gradient (TD3) improves the overall system efficiency, lifetime, and fuel economy compared to conventional energy management methods. To verify the technical feasibility of the propulsion system including its controls, the system is tested in a hardware-in-the-loop (HIL) environment. By implementing the TD3-based algorithm within the energy management used on the test bench, hydrogen consumption was reduced by approximately 10% and the remaining battery capacity after five years was 6% higher in comparison to conventional energy management methods.

Suggested Citation

  • Ünlübayir, Cem & Youssfi, Hiba & Khan, Rehan Ahmad & Ventura, Santiago Salas & Fortunati, Daniele & Rinner, Jonas & Börner, Martin Florian & Quade, Katharina Lilith & Ringbeck, Florian & Sauer, Dirk U, 2024. "Comparative analysis and test bench validation of energy management methods for a hybrid marine propulsion system powered by batteries and solid oxide fuel cells," Applied Energy, Elsevier, vol. 376(PA).
    • Handle: RePEc:eee:appene:v:376:y:2024:i:pa:s0306261924015666
    DOI: 10.1016/j.apenergy.2024.124183
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924015666
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124183?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. Jessica Kersey & Natalie D. Popovich & Amol A. Phadke, 2022. "Rapid battery cost declines accelerate the prospects of all-electric interregional container shipping," Nature Energy, Nature, vol. 7(7), pages 664-674, July.
    2. Wu, Peng & Partridge, Julius & Bucknall, Richard, 2020. "Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships," Applied Energy, Elsevier, vol. 275(C).
    3. Baldi, Francesco & Moret, Stefano & Tammi, Kari & Maréchal, François, 2020. "The role of solid oxide fuel cells in future ship energy systems," Energy, Elsevier, vol. 194(C).
    4. Inal, Omer Berkehan & Charpentier, Jean-Frédéric & Deniz, Cengiz, 2022. "Hybrid power and propulsion systems for ships: Current status and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    5. Huang, Yin & Kang, Zehao & Mao, Xuping & Hu, Haoqin & Tan, Jiaqi & Xuan, Dongji, 2023. "Deep reinforcement learning based energymanagement strategy considering running costs and energy source aging for fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 283(C).
    6. Balsamo, Flavio & Capasso, Clemente & Lauria, Davide & Veneri, Ottorino, 2020. "Optimal design and energy management of hybrid storage systems for marine propulsion applications," Applied Energy, Elsevier, vol. 278(C).
    7. Dimitrova, Zlatina & Nader, Wissam Bou, 2022. "PEM fuel cell as an auxiliary power unit for range extended hybrid electric vehicles," Energy, Elsevier, vol. 239(PA).
    8. Marcin Kolodziejski & Iwona Michalska-Pozoga, 2023. "Battery Energy Storage Systems in Ships’ Hybrid/Electric Propulsion Systems," Energies, MDPI, vol. 16(3), pages 1-24, January.
    9. Tang, Ruoli & Li, Xin & Lai, Jingang, 2018. "A novel optimal energy-management strategy for a maritime hybrid energy system based on large-scale global optimization," Applied Energy, Elsevier, vol. 228(C), pages 254-264.
    10. Zhu, Jianyun & Chen, Li & Wang, Bin & Xia, Lijuan, 2018. "Optimal design of a hybrid electric propulsive system for an anchor handling tug supply vessel," Applied Energy, Elsevier, vol. 226(C), pages 423-436.
    11. Hou, Jun & Sun, Jing & Hofmann, Heath, 2018. "Adaptive model predictive control with propulsion load estimation and prediction for all-electric ship energy management," Energy, Elsevier, vol. 150(C), pages 877-889.
    12. Zhou, Yujie & Huang, Yin & Mao, Xuping & Kang, Zehao & Huang, Xuejin & Xuan, Dongji, 2024. "Research on energy management strategy of fuel cell hybrid power via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 293(C).
    13. Zhou, Jianhao & Xue, Siwu & Xue, Yuan & Liao, Yuhui & Liu, Jun & Zhao, Wanzhong, 2021. "A novel energy management strategy of hybrid electric vehicle via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 224(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. Sun, Xiaojun & Yao, Chong & Song, Enzhe & Liu, Zhijiang & Ke, Yun & Ding, Shunliang, 2023. "Novel enhancement of energy distribution for marine hybrid propulsion systems by an advanced variable weight decision model predictive control," Energy, Elsevier, vol. 274(C).
    2. Sun, Xiaojun & Yao, Chong & Song, Enzhe & Yang, Qidong & Yang, Xuchang, 2022. "Optimal control of transient processes in marine hybrid propulsion systems: Modeling, optimization and performance enhancement," Applied Energy, Elsevier, vol. 321(C).
    3. 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.
    4. Hou, Jun & Song, Ziyou & Park, Hyeongjun & Hofmann, Heath & Sun, Jing, 2018. "Implementation and evaluation of real-time model predictive control for load fluctuations mitigation in all-electric ship propulsion systems," Applied Energy, Elsevier, vol. 230(C), pages 62-77.
    5. Si, Yupeng & Wang, Rongjie & Zhang, Shiqi & Zhou, Wenting & Lin, Anhui & Zeng, Guangmiao, 2022. "Configuration optimization and energy management of hybrid energy system for marine using quantum computing," Energy, Elsevier, vol. 253(C).
    6. Yuan, Yupeng & Wang, Jixiang & Yan, Xinping & Shen, Boyang & Long, Teng, 2020. "A review of multi-energy hybrid power system for ships," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    7. Xie, Peilin & Tan, Sen & Bazmohammadi, Najmeh & Guerrero, Josep. M. & Vasquez, Juan. C. & Alcala, Jose Matas & Carreño, Jorge El Mariachet, 2022. "A distributed real-time power management scheme for shipboard zonal multi-microgrid system," Applied Energy, Elsevier, vol. 317(C).
    8. Bagherabadi, Kamyar Maleki & Skjong, Stian & Bruinsma, Jogchum & Pedersen, Eilif, 2023. "Investigation of hybrid power plant configurations for an offshore vessel with co-simulation approach," Applied Energy, Elsevier, vol. 343(C).
    9. Salman Farrukh & Mingqiang Li & Georgios D. Kouris & Dawei Wu & Karl Dearn & Zacharias Yerasimou & Pavlos Diamantis & Kostas Andrianos, 2023. "Pathways to Decarbonization of Deep-Sea Shipping: An Aframax Case Study," Energies, MDPI, vol. 16(22), pages 1-26, November.
    10. Wang, Tingsong & Cheng, Peiyue & Zhen, Lu, 2023. "Green development of the maritime industry: Overview, perspectives, and future research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    11. Perčić, Maja & Frković, Lovro & Pukšec, Tomislav & Ćosić, Boris & Li, Oi Lun & Vladimir, Nikola, 2022. "Life-cycle assessment and life-cycle cost assessment of power batteries for all-electric vessels for short-sea navigation," Energy, Elsevier, vol. 251(C).
    12. Miretti, Federico & Misul, Daniela & Gennaro, Giulio & Ferrari, Antonio, 2022. "Hybridizing waterborne transport: Modeling and simulation of low-emissions hybrid waterbuses for the city of Venice," Energy, Elsevier, vol. 244(PB).
    13. Guo, Xinru & Guo, Yumin & Wang, Jiangfeng & Meng, Xin & Deng, Bohao & Wu, Weifeng & Zhao, Pan, 2023. "Thermodynamic analysis of a novel combined heating and power system based on low temperature solid oxide fuel cell (LT-SOFC) and high temperature proton exchange membrane fuel cell (HT-PEMFC)," Energy, Elsevier, vol. 284(C).
    14. Huang, Jiangfan & An, Qing & Zhou, Mingyu & Tang, Ruoli & Dong, Zhengcheng & Lai, Jingang & Li, Xin & Yang, Xiangguo, 2024. "A self-adaptive joint optimization framework for marine hybrid energy storage system design considering load fluctuation characteristics," Applied Energy, Elsevier, vol. 361(C).
    15. Liu, Yonggang & Wu, Yitao & Wang, Xiangyu & Li, Liang & Zhang, Yuanjian & Chen, Zheng, 2023. "Energy management for hybrid electric vehicles based on imitation reinforcement learning," Energy, Elsevier, vol. 263(PC).
    16. Wang, Guohui & Yang, Yanan & Wang, Shuxin & Zhang, Hongwei & Wang, Yanhui, 2019. "Efficiency analysis and experimental validation of the ocean thermal energy conversion with phase change material for underwater vehicle," Applied Energy, Elsevier, vol. 248(C), pages 475-488.
    17. Tang, Tianfeng & Peng, Qianlong & Shi, Qing & Peng, Qingguo & Zhao, Jin & Chen, Chaoyi & Wang, Guangwei, 2024. "Energy management of fuel cell hybrid electric bus in mountainous regions: A deep reinforcement learning approach considering terrain characteristics," Energy, Elsevier, vol. 311(C).
    18. Tang, Ruoli & An, Qing & Xu, Fan & Zhang, Xiaodi & Li, Xin & Lai, Jingang & Dong, Zhengcheng, 2020. "Optimal operation of hybrid energy system for intelligent ship: An ultrahigh-dimensional model and control method," Energy, Elsevier, vol. 211(C).
    19. Michail Cheliotis & Evangelos Boulougouris & Nikoletta L Trivyza & Gerasimos Theotokatos & George Livanos & George Mantalos & Athanasios Stubos & Emmanuel Stamatakis & Alexandros Venetsanos, 2021. "Review on the Safe Use of Ammonia Fuel Cells in the Maritime Industry," Energies, MDPI, vol. 14(11), pages 1-20, May.
    20. Ahmed, Shoaib & Li, Tie & Yi, Ping & Chen, Run, 2023. "Environmental impact assessment of green ammonia-powered very large tanker ship for decarbonized future shipping operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(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:appene:v:376:y:2024:i:pa:s0306261924015666. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.