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Multi-objective design of a hybrid propulsion system for marine vessels

Multi-objective design of a hybrid propulsion system for marine vessels

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Hybrid vehicles offer the advantages of both conventional and electric vehicles. Improved fuel consumption is generally the main driver for these systems with the option of silent, emission-free operation also being a big advantage. Marine hybrid vehicles however, do not experience significant recovery of energy via regeneration, because of their operating profiles. Fuel savings can be realised by optimal component operation rather than free-energy recovery, yet this requires correct component sizing considering the typical usage over a scenario. In this study, a model is built to calculate the fuel consumption of a hybrid motoryacht over a given day-cruise scenario. A genetic algorithm is then applied to optimise the component sizing of this hybrid system with respect to minimisation of fuel consumption and total installation weight.

Inspec keywords: electric propulsion; genetic algorithms; hybrid electric vehicles; marine vehicles; marine propulsion

Other keywords: hybrid motoryacht; emission-free operation; optimal component operation; electric vehicles; day-cruise scenario; marine vessels; multiobjective design; genetic algorithm; marine hybrid vehicles; hybrid propulsion system; correct component sizing; fuel savings; free-energy recovery; improved fuel consumption minimisation

Subjects: Transportation; Optimisation techniques

References

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      • Jain, M., Desai, C., Williamson, S.S.: `Genetic algorithm based optimal powertrain component sizing and control strategy design for a fuel cell hybrid electric bus', Vehicle Power and Propulsion Conf., 2009 VPPC’09 IEEE, September 2009, p. 980–985.
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      • Schofield, N., Yap, H.T., Bingham, C.M.: `Hybrid energy sources for electric and fuel cell vehicle propulsion', IEEE Conf. on Vehicle Power and Propulsion, 2005, p. 522–529.
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      • Desai, C., Williamson, S.S.: `Optimal design of a parallel Hybrid Electric Vehicle using multi-objective genetic algorithms', Vehicle Power and Propulsion Conf., 2009 VPPC’09 IEEE, September 2009, p. 871–876.
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