Optimisation of hybrid energy systems for maritime vessels. Issue 17 (26th April 2019)

• Record Type: Journal Article
• Authors: Tang, Wenshuo; Dickie, Ross; Roman, Darius; Robu, Valentin; Flynn, David
• Is Part Of: Journal of engineering. Volume 2019:Issue 17(2019)
• Page Start: 4516
• Page End: 4521
• Publication Date: 2019-04-26
• Subjects: exhaust systems -- diesel engines -- hybrid electric vehicles -- battery management systems -- asset management -- energy conservation -- energy management systems -- nitrogen compounds -- energy storage -- transportation -- lead acid batteries -- air pollution control -- condition monitoring -- learning (artificial intelligence) -- ships -- power engineering computing; hybrid energy systems -- maritime vessels -- decarbonisation agenda -- maritime transport -- asset owners -- greener technologies -- primary drivers -- efficient energy performance -- asset management -- technical risks -- financial risks -- utilising energy -- environmental monitoring -- energy system optimisation architecture -- hybrid fusion energy management system -- key performance indicators -- diesel engine nitrogen oxide -- energy storage technologies -- art machine‐learning techniques -- lead acid batteries -- vessel operator -- increased vessel journey use cases -- environmental metrics -- HyFES -- energy performance -- particulate matter -- prognostic state of health assessment -- on‐board lithium‐ion batteries; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.8232
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : The decarbonisation agenda in maritime transport requires that asset owners and operators adopt greener technologies within their existing and new vessels. The primary drivers within this agenda relate to improved environmental metrics, efficient energy performance, and improved asset management. However, the integration of new technologies always presents technical and financial risks. Here, utilising energy and environmental monitoring from real vessels, the authors propose an energy system optimisation architecture, hybrid fusion energy management system (HyFES), that optimises the key performance indicators of energy performance, reduction of diesel engine nitrogen oxide (NOx), and particulate matter (PM), and prognostic state of health assessment of energy storage technologies. Using state of the art machine‐learning techniques, the authors are able to determine the on‐board lithium‐ion and lead acid batteries' state of health with accuracy > 8 and 4%, respectively. Dependent on the mode of operation, optimisation of energy performance indicates fuel saving of between 70 and 80% for the vessel operator. Future research will focus on the integration of more assets into the optimisation architecture and increased vessel journey use cases.