Sizing and Control of a Hybrid Ship Propulsion System Using Multi-Objective Double-Layer Optimization

Xuezhou Wang, Udai Shipurkar, Ali Haseltalab, Henk Polinder, Frans Claeys, Rudy R. Negenborn

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)
135 Downloads (Pure)

Abstract

Ship hybridization has received some interests recently in order to achieve the emission target by 2050. However, designing and optimizing a hybrid propulsion system is a complicated problem. Sizing components and optimizing energy management control are coupled with each other. This paper applies a nested double-layer optimization architecture to optimize the sizing and energy management of a hybrid offshore support vessel. Three different power sources, namely diesel engines, batteries and fuel cells, are considered which increases the complexity of the optimization problem. The optimal sizing of the components and their corresponding energy management strategies are illustrated. The effects of the operational profiles and the emission reduction targets on the hybridization design are studied for this particular type of vessel. The results prove that a small emission reduction target of about 10% can be achieved by improving the diesel engine efficiency using the batteries only while the achievement of a larger emission reduction target mainly depends on the amount of the hydrogen and/or on-shore charging electricity consumed. Some design guidelines for hybridization are derived for this particular ship which could be also valid for other vessels with similar operational profiles.

Original languageEnglish
Pages (from-to)72587-72601
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • Batteries
  • control
  • Energy management
  • energy management
  • Fuel cells
  • Hybrid
  • Hydrogen
  • Marine vehicles
  • offshore support vessel
  • Optimization
  • Propulsion
  • sizing

Fingerprint

Dive into the research topics of 'Sizing and Control of a Hybrid Ship Propulsion System Using Multi-Objective Double-Layer Optimization'. Together they form a unique fingerprint.

Cite this