Co-Optimization of the Hardware Configuration and Energy Management Parameters of Ship Hybrid Power Systems Based on the Hybrid Ivy-SA Algorithm
A ship’s diesel–electric hybrid power system is complex, with hardware configuration and energy management parameters being crucial to its economic performance. However, existing optimization methods typically involve designing and optimizing the hardware configuration on the basis of typical operat...
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MDPI AG
2025-04-01
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| Series: | Journal of Marine Science and Engineering |
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| author | Qian Guo Zhihang Fu Xingming Zhang |
| author_facet | Qian Guo Zhihang Fu Xingming Zhang |
| author_sort | Qian Guo |
| collection | DOAJ |
| description | A ship’s diesel–electric hybrid power system is complex, with hardware configuration and energy management parameters being crucial to its economic performance. However, existing optimization methods typically involve designing and optimizing the hardware configuration on the basis of typical operating conditions, followed by the design and optimization of the energy management parameters, which makes it difficult to achieve optimal system performance. Moreover, when co-optimizing hardware configurations and energy management parameters, the parameter relationships and complex constraints often lead conventional optimization algorithms to converge slowly and become trapped in local optima. To address this issue, a hybrid Ivy-SA algorithm is developed for the co-optimization of both the hardware configuration and energy management parameters. First, the main engine and hybrid ship models are established on the basis of the hardware configuration, and the accuracy of the models is validated. An energy management strategy based on the equivalent fuel consumption minimization strategy (ECMS) is then formulated, and energy management parameters are designed. A sensitivity analysis is conducted on the basis of both the hardware configuration and energy management parameters to evaluate their impacts under various conditions, enabling the selection of key optimization parameters, such as diesel engine parameters, battery configuration, and charge/discharge factors. The Ivy-SA algorithm, which integrates the advantages of both the Ivy algorithm (IVYA) and the simulated annealing algorithm (SA), is developed for the co-optimization. The algorithm is tested with the CEC2017 benchmark functions and outperforms 11 other algorithms. Furthermore, when the top five performing algorithms are applied for the co-optimization, the results show that the Ivy-SA algorithm outperforms the other four algorithms with a 14.49% increase in economic efficiency and successfully escapes local optima. |
| format | Article |
| id | doaj-art-accac34a32df4d42a0d66293d533ac4f |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-accac34a32df4d42a0d66293d533ac4f2025-08-20T02:18:09ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-04-0113473110.3390/jmse13040731Co-Optimization of the Hardware Configuration and Energy Management Parameters of Ship Hybrid Power Systems Based on the Hybrid Ivy-SA AlgorithmQian Guo0Zhihang Fu1Xingming Zhang2School of Ocean Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, ChinaSchool of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, ChinaSchool of Ocean Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, ChinaA ship’s diesel–electric hybrid power system is complex, with hardware configuration and energy management parameters being crucial to its economic performance. However, existing optimization methods typically involve designing and optimizing the hardware configuration on the basis of typical operating conditions, followed by the design and optimization of the energy management parameters, which makes it difficult to achieve optimal system performance. Moreover, when co-optimizing hardware configurations and energy management parameters, the parameter relationships and complex constraints often lead conventional optimization algorithms to converge slowly and become trapped in local optima. To address this issue, a hybrid Ivy-SA algorithm is developed for the co-optimization of both the hardware configuration and energy management parameters. First, the main engine and hybrid ship models are established on the basis of the hardware configuration, and the accuracy of the models is validated. An energy management strategy based on the equivalent fuel consumption minimization strategy (ECMS) is then formulated, and energy management parameters are designed. A sensitivity analysis is conducted on the basis of both the hardware configuration and energy management parameters to evaluate their impacts under various conditions, enabling the selection of key optimization parameters, such as diesel engine parameters, battery configuration, and charge/discharge factors. The Ivy-SA algorithm, which integrates the advantages of both the Ivy algorithm (IVYA) and the simulated annealing algorithm (SA), is developed for the co-optimization. The algorithm is tested with the CEC2017 benchmark functions and outperforms 11 other algorithms. Furthermore, when the top five performing algorithms are applied for the co-optimization, the results show that the Ivy-SA algorithm outperforms the other four algorithms with a 14.49% increase in economic efficiency and successfully escapes local optima.https://www.mdpi.com/2077-1312/13/4/731hybrid power systemco-optimizationsensitivity analysishybrid Ivy-SA algorithm |
| spellingShingle | Qian Guo Zhihang Fu Xingming Zhang Co-Optimization of the Hardware Configuration and Energy Management Parameters of Ship Hybrid Power Systems Based on the Hybrid Ivy-SA Algorithm Journal of Marine Science and Engineering hybrid power system co-optimization sensitivity analysis hybrid Ivy-SA algorithm |
| title | Co-Optimization of the Hardware Configuration and Energy Management Parameters of Ship Hybrid Power Systems Based on the Hybrid Ivy-SA Algorithm |
| title_full | Co-Optimization of the Hardware Configuration and Energy Management Parameters of Ship Hybrid Power Systems Based on the Hybrid Ivy-SA Algorithm |
| title_fullStr | Co-Optimization of the Hardware Configuration and Energy Management Parameters of Ship Hybrid Power Systems Based on the Hybrid Ivy-SA Algorithm |
| title_full_unstemmed | Co-Optimization of the Hardware Configuration and Energy Management Parameters of Ship Hybrid Power Systems Based on the Hybrid Ivy-SA Algorithm |
| title_short | Co-Optimization of the Hardware Configuration and Energy Management Parameters of Ship Hybrid Power Systems Based on the Hybrid Ivy-SA Algorithm |
| title_sort | co optimization of the hardware configuration and energy management parameters of ship hybrid power systems based on the hybrid ivy sa algorithm |
| topic | hybrid power system co-optimization sensitivity analysis hybrid Ivy-SA algorithm |
| url | https://www.mdpi.com/2077-1312/13/4/731 |
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