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Energy Management Optimization of Fuel Cell Hybrid Ship Based on Particle Swarm Optimization Algorithm

Author

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  • Xin Peng

    (Shenzhen Research Institute, Wuhan University of Technology, Shenzhen 518000, China
    School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China)

  • Hui Chen

    (Shenzhen Research Institute, Wuhan University of Technology, Shenzhen 518000, China
    School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China)

  • Cong Guan

    (Shenzhen Research Institute, Wuhan University of Technology, Shenzhen 518000, China
    School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China)

Abstract

In order to optimize the energy management strategy and solve the problem of the power quality degradation of fuel cell hybrid electric ships, a particle swarm optimization algorithm based energy management strategy is proposed in this paper. Taking a fuel cell ship as the target ship, a system simulation model is built in Matlab/Simulink to verify the proposed energy management strategy. Through simulations and comparisons, the bus voltage curve of the optimized hybrid power system fluctuates more gently, and the voltage sag is smaller. The amplitude of the voltage fluctuation under maneuvering conditions is reduced by 55% compared with that of the original ship. The charging and discharging process of the composite energy storage system is optimized under maneuvering conditions, the power quality of the marine power grid is improved, and the use of the energy management strategy can extend the service life of the battery.

Suggested Citation

  • Xin Peng & Hui Chen & Cong Guan, 2023. "Energy Management Optimization of Fuel Cell Hybrid Ship Based on Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 16(3), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1373-:d:1050030
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    References listed on IDEAS

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    1. Yudong Zhang & Shuihua Wang & Genlin Ji, 2015. "A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-38, October.
    2. Fotis D. Kanellos & John Prousalidis & George J. Tsekouras, 2017. "Optimal Active Power Management in All Electric Ship Employing DC Grid Technology," Springer Proceedings in Business and Economics, in: Evangelos Grigoroudis & Michael Doumpos (ed.), Operational Research in Business and Economics, pages 271-284, Springer.
    3. Geertsma, R.D. & Negenborn, R.R. & Visser, K. & Hopman, J.J., 2017. "Design and control of hybrid power and propulsion systems for smart ships: A review of developments," Applied Energy, Elsevier, vol. 194(C), pages 30-54.
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    5. Du, Wei & Li, Yanjun & Shi, Jianxin & Sun, Baozhi & Wang, Chunhui & Zhu, Baitong, 2023. "Applying an improved particle swarm optimization algorithm to ship energy saving," Energy, Elsevier, vol. 263(PE).
    6. Chen, Hui & Zhang, Zehui & Guan, Cong & Gao, Haibo, 2020. "Optimization of sizing and frequency control in battery/supercapacitor hybrid energy storage system for fuel cell ship," Energy, Elsevier, vol. 197(C).
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    8. Wenqing Hu & Qianming Shang & Xiangrui Bian & Renjie Zhu, 2022. "Energy management strategy of hybrid energy storage system based on fuzzy control for ships [State-of-charge balancing of lithium-ion batteries with state-of-health awareness capability]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 673-684.
    9. Diju Gao & Xuyang Wang & Tianzhen Wang & Yide Wang & Xiaobin Xu, 2018. "An Energy Optimization Strategy for Hybrid Power Ships under Load Uncertainty Based on Load Power Prediction and Improved NSGA-II Algorithm," Energies, MDPI, vol. 11(7), pages 1-14, July.
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    Cited by:

    1. Rudravaram Venkatasatish & Dhanamjayulu Chittathuru, 2023. "Coyote Optimization Algorithm-Based Energy Management Strategy for Fuel Cell Hybrid Power Systems," Sustainability, MDPI, vol. 15(12), pages 1-21, June.

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