A novel dynamical collaborative optimization method of ship energy consumption based on a spatial and temporal distribution analysis of voyage data

It is of significant importance to optimize the energy consumption of ships in order to improve economy and reduce CO₂ emissions. However, the energy use of ships is affected by a series of navigational environmental parameters, which have certain spatial and temporal differences and variability. Therefore, the dynamic collaborative optimization method of sailing route and speed, which fully considers the spatial and temporal distribution characteristics of those factors, is of great importance. In this paper, the spatial and temporal distribution characteristics of the environmental factors and their related ship energy consumption profiles are first analyzed. Subsequently, a ship energy consumption model considering various environmental factors is established to realize the prediction of energy use of ships within the navigation region. Then, a novel dynamic collaborative optimization algorithm, which adopts the Model Predictive Control (MPC) strategy and swarm intelligence algorithm, is proposed, to further improve the ship’s energy consumption optimization. Finally, a case study is conducted to demonstrate the effectiveness of the proposed method. The results show that the newly developed dynamic collaborative optimization method, which fully considers the continuously time-varying characteristics of environmental and operational parameters, could effectively reduce the energy consumption in comparison to the original operational mode. In addition, the adoption of the MPC strategy produces better performance results compared to the optimization method without the MPC strategy.

• Record URL:
  https://doi.org/10.1016/j.apor.2021.102657
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0141118721001346
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01411187
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wang, Kai
  • 0000-0002-2941-4579
  • Xu, Hao
  • Li, Jiayuan
  • Huang, Lianzhong
  • Ma, Ranqi
  • Jiang, Xiaoli
  • Yuan, Yupeng
  • Mwero, Ngome A
  • Sun, Peiting
  • 0000-0003-1968-9728
  • Negenborn, Rudy R
  • 0000-0001-9784-1225
  • Yan, Xinping
• Publication Date: 2021-7

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Maps; References; Tables;
• Pagination: 102657
• Serial:
  • Applied Ocean Research
  • Volume: 112
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0141-1187
  • Serial URL: https://www.sciencedirect.com/journal/applied-ocean-research

Subject/Index Terms

• TRT Terms: Case studies; Energy consumption; Operating speed; Optimization; Predictive models; Routes and routing; Ships; Spatial analysis; Time domain analysis
• Identifier Terms: Model Predictive Control
• Subject Areas: Energy; Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01777725
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 26 2021 3:47PM