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Sizing of a combined series-parallel hybrid architecture for river ship application using genetic algorithm and optimal energy management

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 Apr 2019Publisher:Elsevier BVJournal:Mathematics and Computers in Simulation, volume 158, pages 248-263 (issn: 0378-4754,

Authors: Romain Derollepot; Emmanuel Vinot;

doi: 10.1016/j.matcom.2018.09.012

Sizing of a combined series-parallel hybrid architecture for river ship application using genetic algorithm and optimal energy management

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Abstract

Abstract A good way to reduce the fuel consumption of a commercial river ship can be the hybridization of the system. Sizing the system related to its actual use and energy management is then a key-point. In this paper a global sizing method based on Genetic Algorithm and dynamic programming is proposed. It allows the sizing of a complex series-parallel architecture on uses cycles which are developed using measurements on real ship. The study shows potential reduction of fuel consumption around 10%, and proposes some leads to reduce the pollutant emissions.

Related Organizations

French Institute of Science and Technology for Transport, Spatial Planning, Development and Networks
France

Keywords

HYBRID ARCHITECTURE, MOTEUR HYBRIDE, SIZING PROCESS, NAVIRE, [SPI]Engineering Sciences [physics], OPTIMAL CONTROL, CONSOMMATION DE CARBURANT, RIVER SHIP, TRANSPORT FLUVIAL

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	17
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%