LAPSE:2023.3634
Published Article
LAPSE:2023.3634
Energy Management Optimization of Fuel Cell Hybrid Ship Based on Particle Swarm Optimization Algorithm
Xin Peng, Hui Chen, Cong Guan
February 22, 2023
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.
Keywords
energy management strategy, fuel cell, hybrid ship, Particle Swarm Optimization
Suggested Citation
Peng X, Chen H, Guan C. Energy Management Optimization of Fuel Cell Hybrid Ship Based on Particle Swarm Optimization Algorithm. (2023). LAPSE:2023.3634
Author Affiliations
Peng X: 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
Chen H: 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 [ORCID]
Guan C: 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
Journal Name
Energies
Volume
16
Issue
3
First Page
1373
Year
2023
Publication Date
2023-01-29
Published Version
ISSN
1996-1073
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PII: en16031373, Publication Type: Journal Article
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LAPSE:2023.3634
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doi:10.3390/en16031373
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