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

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  • 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
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    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. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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).
    8. 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.
    9. 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).
    10. 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).
    11. 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).
    12. 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).
    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).
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