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Multi-Objective Optimization of Energy Storage Configuration and Dispatch in Diesel-Electric Propulsion Ships

Furkejuvvon:
Publikašuvnnas:Journal of Marine Science and Engineering vol. 13, no. 9 (2025), p. 1808-1835
Váldodahkki: Sun Fupeng
Eará dahkkit: Liu, Yanlin, Gan Huibing, Zang Shaokang, Lei Zhibo
Almmustuhtton:
MDPI AG
Fáttát:
Load
Fuel cells
Lower bounds
Energy management
Particle swarm optimization
Diesel engines
Shipping
Mathematical models
Greenhouse gases
Batteries
Modules
Energy storage
Multiple objective analysis
Engines
Energy resources
Energy consumption
Lithium
Configurations
Scheduling
Genetic algorithms
Berthing
Energy
Algorithms
Search algorithms
Emissions
Alternative energy sources
Cost control
Optimization algorithms
Fuel economy
Ship handling
Shipping industry
Dung
Fuel consumption
Ships
Diesel generators
Life cycle costs
Diesel
Emissions control
Carbon dioxide
State of charge
Energy efficiency
Emission standards
Electric propulsion
Economic
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Abstrákta:This study investigates the configuration of an energy storage system (ESS) and the optimization of energy management strategies for diesel-electric hybrid ships, with the goal of enhancing fuel economy and reducing emissions. An integrated mathematical model of the diesel generator set and the battery-based ESS is established. A rule-based energy management strategy (EMS) is proposed, in which the ship operating conditions are classified into berthing, maneuvering, and cruising modes. This classification enables coordinated power allocation between the diesel generator set and the ESS, while ensuring that the diesel engine operates within its high-efficiency region. The optimization framework considers the number of battery modules in series and the upper and lower bounds of the state of charge (SOC) as design variables. The dual objectives are set as lifecycle cost (LCC) and greenhouse gas (GHG) emissions, optimized using the Multi-Objective Coati Optimization Algorithm (MOCOA). The algorithm achieves a balance between global exploration and local exploitation. Numerical simulations indicate that, under the LCC-optimal solution, fuel consumption and GHG emissions are reduced by 16.12% and 13.18%, respectively, while under the GHG-minimization solution, reductions of 37.84% in fuel consumption and 35.02% in emissions are achieved. Compared with conventional algorithms, including Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Dung Beetle Optimizer (NSDBO), and Multi-Objective Sparrow Search Algorithm (MOSSA), MOCOA exhibits superior convergence and solution diversity. The findings provide valuable engineering insights into the optimal configuration of ESS and EMS for hybrid ships, thereby contributing to the advancement of green shipping.
ISSN:2077-1312
DOI:10.3390/jmse13091808
Gáldu:Engineering Database