Sensitivity of Ship Voyage Optimizations to Various Energy Cost Models
Paper in proceeding, 2024

A ship’s energy performance model describing the relationship between ship speed and energy consumption is an essential component in her voyage optimization system, since it is required to evaluate the energy costs associated with different voyage plannings. For energy-efficient voyage planning, such a ship model estimates the corresponding energy consumption of each feasible route/sub-route based on her sailing speeds and encountering environmental conditions. Thus, the reliability of a ship’s energy performance model is expected to have a great influence on the ship’s voyage optimization results. Various approaches have been widely researched to construct the ship performance model, such as empirical white-box models based on experimental tests and physical knowledge, data-driven black-box models using machine learning methods, and gray-box models combining the above two approaches, etc. In addition, various energy cost functions are used for the ship voyage optimizations, such as the total power or fuel consumption, etc. The objective of this study is to investigate the sensitivity of ship voyage optimizations due to different energy cost functions from different modeling techniques. A chemical tanker with full-scale measurement is used in the case study to study the sensitivity of voyage optimizations in terms of energy efficiency. Some insights into employing different energy cost functions and models are discussed in detail to provide good recommendation practices for optimal voyage planning.

voyage optimization

energy efficiency

machine learning

ship performance model

Author

Yuhan Chen

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Wengang Mao

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

International Conference on Offshore Mechanics and Arctic Engineering

1523-651X (ISSN)

Vol. 5A: Ocean Engineering OMAE2024-127986, V05AT06A050
978-0-7918-8782-0 (ISBN)

ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2024
Singapore, Singapore,

AI-enhanced energy efficiency measures for optimal ship operations to reduce GHG emissions

VINNOVA (2021-02768), 2021-10-15 -- 2024-06-30.

AUTOBarge - European training and research network on Autonomous Barges for Smart Inland Shipping

European Commission (EC) (EC/H2020/955768), 2021-10-01 -- 2025-09-30.

Areas of Advance

Transport

Energy

Subject Categories

Energy Engineering

Marine Engineering

DOI

10.1115/OMAE2024-127986

More information

Latest update

12/13/2024