Navigational path optimization methodology for wing-diesel hybrid ship based on improved Double Deep Q-Network
Wing-diesel hybrid ships can demonstrate quantifiable fuel efficiency gains by using wing-sails’ thrust to replace part of the fuel propulsion during navigation. But ships can be affected by factors such as wind, waves, currents and shore wall effects during navigation, thus necessitating precise pa...
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| Published in: | Ocean engineering Vol. 341; p. 122543 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.12.2025
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| ISSN: | 0029-8018 |
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| Abstract | Wing-diesel hybrid ships can demonstrate quantifiable fuel efficiency gains by using wing-sails’ thrust to replace part of the fuel propulsion during navigation. But ships can be affected by factors such as wind, waves, currents and shore wall effects during navigation, thus necessitating precise path planning methodologies to ensure both economic efficiency and operational safety. Therefore, this study develops an improved Double Deep Q Network (DDQN) algorithm for optimizing the navigation path planning of the wing-diesel hybrid ship. First, a ship fuel consumption prediction model based on XGBoost algorithm and a ship motion model are established. Then, the reward function is improved by incorporating fuel consumption, heading, distance, time, and collisions as rewards or penalties. Afterward, DDQN is introduced to learn the action-reward model, and the learning results are used to control the ship's movement. By pursuing higher reward values, the ship can autonomously find the optimal low-fuel consumption path. Experimental verification was conducted on the target voyage of the “New Aden” in the Arabian Sea. The results demonstrate that the proposed method effectively enhances the energy efficiency of wing-diesel hybrid ships, reducing fuel consumption and the Energy Efficiency Operational Indicator (EEOI) by approximately 6.35 % and 7.98 %, respectively.
•A cooperative optimization method for the wind-assisted hybrid ship is proposed.•Improved Double Deep Q-Network is designed.•XGBoost algorithm is used for predicting ship fuel consumption.•Reward function is improved to optimize operational efficiency.•Interpolation method is adopted to align meteorological data with ship data. |
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| AbstractList | Wing-diesel hybrid ships can demonstrate quantifiable fuel efficiency gains by using wing-sails’ thrust to replace part of the fuel propulsion during navigation. But ships can be affected by factors such as wind, waves, currents and shore wall effects during navigation, thus necessitating precise path planning methodologies to ensure both economic efficiency and operational safety. Therefore, this study develops an improved Double Deep Q Network (DDQN) algorithm for optimizing the navigation path planning of the wing-diesel hybrid ship. First, a ship fuel consumption prediction model based on XGBoost algorithm and a ship motion model are established. Then, the reward function is improved by incorporating fuel consumption, heading, distance, time, and collisions as rewards or penalties. Afterward, DDQN is introduced to learn the action-reward model, and the learning results are used to control the ship's movement. By pursuing higher reward values, the ship can autonomously find the optimal low-fuel consumption path. Experimental verification was conducted on the target voyage of the “New Aden” in the Arabian Sea. The results demonstrate that the proposed method effectively enhances the energy efficiency of wing-diesel hybrid ships, reducing fuel consumption and the Energy Efficiency Operational Indicator (EEOI) by approximately 6.35 % and 7.98 %, respectively.
•A cooperative optimization method for the wind-assisted hybrid ship is proposed.•Improved Double Deep Q-Network is designed.•XGBoost algorithm is used for predicting ship fuel consumption.•Reward function is improved to optimize operational efficiency.•Interpolation method is adopted to align meteorological data with ship data. |
| ArticleNumber | 122543 |
| Author | Wang, Cong Sheng, Jinlu Ma, Ranqi Cao, JianLin Ruan, Zhang Zhang, Rui Wang, Kai Huang, Lianzhong |
| Author_xml | – sequence: 1 givenname: Cong orcidid: 0009-0008-9965-4974 surname: Wang fullname: Wang, Cong organization: Marine Engineering College, Dalian Maritime University, Dalian, 116026, Liaoning, China – sequence: 2 givenname: Lianzhong orcidid: 0009-0006-3886-4447 surname: Huang fullname: Huang, Lianzhong email: huanglz@dlmu.edu.cn organization: Marine Engineering College, Dalian Maritime University, Dalian, 116026, Liaoning, China – sequence: 3 givenname: Ranqi surname: Ma fullname: Ma, Ranqi organization: Marine Engineering College, Dalian Maritime University, Dalian, 116026, Liaoning, China – sequence: 4 givenname: Kai surname: Wang fullname: Wang, Kai organization: Marine Engineering College, Dalian Maritime University, Dalian, 116026, Liaoning, China – sequence: 5 givenname: Jinlu surname: Sheng fullname: Sheng, Jinlu organization: School of Shipping and Naval Architecture, Chongqing Jiaotong University, 400074, China – sequence: 6 givenname: Zhang surname: Ruan fullname: Ruan, Zhang organization: Marine Engineering College, Dalian Maritime University, Dalian, 116026, Liaoning, China – sequence: 7 givenname: Rui surname: Zhang fullname: Zhang, Rui organization: Marine Engineering College, Dalian Maritime University, Dalian, 116026, Liaoning, China – sequence: 8 givenname: JianLin surname: Cao fullname: Cao, JianLin organization: Marine Engineering College, Dalian Maritime University, Dalian, 116026, Liaoning, China |
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| Cites_doi | 10.3390/jmse12112100 10.3390/jmse9040357 10.2112/SI97-022.1 10.1109/TCOMM.2019.2947918 10.1016/j.marpol.2016.10.007 10.1109/ACCESS.2019.2953326 10.1016/j.oceaneng.2019.106299 10.3390/en17153833 10.1080/20464177.2024.2434331 10.3390/jmse12091645 10.1016/j.oceaneng.2022.111010 10.3390/jmse11040789 10.1016/j.oceaneng.2023.115750 10.1016/j.energy.2025.135720 10.1016/j.oceaneng.2024.117669 10.1016/j.apor.2021.102759 10.1016/j.energy.2024.130318 10.1016/j.apenergy.2024.123132 10.1007/s00773-024-00993-6 10.1016/j.oceaneng.2024.117668 10.1016/j.oceaneng.2021.109603 10.1109/ACCESS.2023.3307480 10.1016/j.oceaneng.2019.01.026 10.3390/en18040897 |
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| Keywords | Double Deep Q Network algorithm Fuel Consumption Prediction Model Route planning Reward Function Wing-diesel hybrid ship |
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| Title | Navigational path optimization methodology for wing-diesel hybrid ship based on improved Double Deep Q-Network |
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