Multi-Objective Optimization of Energy Storage Configuration and Dispatch in Diesel-Electric Propulsion Ships
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| 出版年: | Journal of Marine Science and Engineering vol. 13, no. 9 (2025), p. 1808-1835 |
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| 第一著者: | |
| その他の著者: | , , , |
| 出版事項: |
MDPI AG
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 022 | |a 2077-1312 | ||
| 024 | 7 | |a 10.3390/jmse13091808 |2 doi | |
| 035 | |a 3254562115 | ||
| 045 | 2 | |b d20250901 |b d20250930 | |
| 084 | |a 231479 |2 nlm | ||
| 100 | 1 | |a Sun Fupeng | |
| 245 | 1 | |a Multi-Objective Optimization of Energy Storage Configuration and Dispatch in Diesel-Electric Propulsion Ships | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Load | ||
| 653 | |a Fuel cells | ||
| 653 | |a Lower bounds | ||
| 653 | |a Energy management | ||
| 653 | |a Particle swarm optimization | ||
| 653 | |a Diesel engines | ||
| 653 | |a Shipping | ||
| 653 | |a Mathematical models | ||
| 653 | |a Greenhouse gases | ||
| 653 | |a Batteries | ||
| 653 | |a Modules | ||
| 653 | |a Energy storage | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Engines | ||
| 653 | |a Energy resources | ||
| 653 | |a Energy consumption | ||
| 653 | |a Lithium | ||
| 653 | |a Configurations | ||
| 653 | |a Scheduling | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Berthing | ||
| 653 | |a Energy | ||
| 653 | |a Algorithms | ||
| 653 | |a Search algorithms | ||
| 653 | |a Emissions | ||
| 653 | |a Alternative energy sources | ||
| 653 | |a Cost control | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Fuel economy | ||
| 653 | |a Ship handling | ||
| 653 | |a Shipping industry | ||
| 653 | |a Dung | ||
| 653 | |a Fuel consumption | ||
| 653 | |a Ships | ||
| 653 | |a Diesel generators | ||
| 653 | |a Life cycle costs | ||
| 653 | |a Diesel | ||
| 653 | |a Emissions control | ||
| 653 | |a Carbon dioxide | ||
| 653 | |a State of charge | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Emission standards | ||
| 653 | |a Electric propulsion | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Liu, Yanlin | |
| 700 | 1 | |a Gan Huibing | |
| 700 | 1 | |a Zang Shaokang | |
| 700 | 1 | |a Lei Zhibo | |
| 773 | 0 | |t Journal of Marine Science and Engineering |g vol. 13, no. 9 (2025), p. 1808-1835 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254562115/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3254562115/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254562115/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |