| 船舶航速优化综述 |
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| 引用本文： | 袁裕鹏,王康豫,尹奇志,严新平.船舶航速优化综述[J].交通运输工程学报,2020,20(6):18-34. |
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| 作者姓名： | 袁裕鹏 王康豫 尹奇志 严新平 |
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| 作者单位： | 武汉理工大学 能源与动力工程学院, 湖北 武汉 430063 |
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| 摘 要： | 从航速优化模型、油耗预测模型、航速优化模型求解方法与船舶能效管理系统方面, 分析了国内外航速优化研究现状, 探讨了航速优化存在的问题, 并针对这些问题提出了建议。研究结果表明: 在航运市场持续萎靡的情况下, 经济航行将被更广泛应用, 针对航速优化的研究仍然具有重要的意义; 在航速优化模型方面, 目前多集中在以碳排放政策、不确定因素的影响、排放控制区政策、船队调度等为单一优化目标建立航速优化模型, 优化目标主要为成本最小化和利润最大化, 未来应将航速与航线、纵倾、船队部署联合优化, 考虑多种不确定因素、多种优化目标建立航速优化模型; 在油耗预测模型方面, 预测模型主要分为白盒模型、黑盒模型和灰盒模型, 白盒模型具有更好的可解释性, 黑盒模型的预测性能更好, 灰盒模型弥补了白盒模型和黑盒模型的缺点, 将成为未来的研究重点, 未来应基于精确的船舶数据和先进的人工智能算法进行数据学习, 提升油耗预测模型预测准确性; 在优化算法方面, 由于航速优化模型的复杂性, 大多采用启发式算法进行优化求解, 这种算法可以减少优化求解时间和提高求解质量, 未来需要探索更加精确高效的求解算法; 在优化策略方面, 采用大数据分析可以识别天气对航行的影响, 动态优化策略可以补偿环境因素引起的扰动, 能够进一步提升船舶能效水平; 在船舶能效管理系统方面, 船舶能效管理系统主要包括航行数据采集、数据传输、数据储存、数据分析与智能决策等功能, 由于其成本高昂, 目前尚未在船舶上大规模运用。
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| 关 键 词： | 船舶工程 航速优化 油耗预测 优化算法 能效管理 综述 |
| 收稿时间： | 2020-07-26 |
Review on ship speed optimization |
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| YUAN Yu-peng,WANG Kang-yu,YIN Qi-zhi,YAN Xin-ping.Review on ship speed optimization[J].Journal of Traffic and Transportation Engineering,2020,20(6):18-34. |
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| Authors: | YUAN Yu-peng WANG Kang-yu YIN Qi-zhi YAN Xin-ping |
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| Affiliation: | School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, Hubei, China |
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| Abstract: | The status of ship speed optimization research in domestic and overseas, including ship speed optimization models, fuel consumption prediction methods, solutions of ship speed optimization models, and ship energy efficiency management systems, were summarized and analyzed. The existing problems in speed optimization research were discussed, and suggestions were made to solve these problems. Analysis result shows that, under the condition in which the shipping market continues to be depressed, economic navigation will be used more widely, and research on speed optimization will remain of great significance.In terms of speed optimization models, most speed optimization models are established with carbon emission policy, influence of uncertain factors, emission control area(ECA) policy and fleet scheduling as a single optimization objective. The main optimization objectives of speed optimization models are to minimize the cost and maximize the profit. Speed should be combined with route, trim and fleet deployment optimization, and a model of speed optimization should be established considering various uncertain factors and optimization objectives in the future.In terms of fuel consumption prediction model, prediction models are mainly divided into white box, black box, and gray box models. The white box model is better in terms of model interpretability, the black box model offers better prediction performance, and the gray box model compensates for the disadvantages of the white box model and the black box model and will become the focus of future research.Data learning should be based on accurate ship data and advanced artificial intelligence algorithms to improve the prediction accuracy of fuel consumption prediction model.In terms of optimization algorithm, due to the complexity of speed optimization model, heuristic algorithm is mostly used for optimization solution. This algorithm can reduce the optimization solution time and improve the solution quality. More accurate and efficient solution algorithms need to be explored in the future.In terms of optimization strategy, the use of big data analysis can identify the influence of weather on navigation, and the use of dynamic optimization strategies can compensate for disturbances caused by environmental factors, enabling further improvement in the energy efficiency of ships.In terms of ship energy efficiency management system, the ship energy efficiency management system mainly includes navigation data acquisition, data transmission, data storage, data analysis and intelligent decision making, it has not been applied on a large scale on ships as its high cost. |
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