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

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

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Electrical Systems in Transportation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.


    1. 1)
    2. 2)
      • 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.
    3. 3)
      • H.K. Woud , D. Stapersma . (2002) Power plant concepts, in: ‘Design of propulsion and electric power generation systems.
    4. 4)
    5. 5)
      • K. Deb . (2001) Multi-objective optimization using evolutionary algorithms.
    6. 6)
      • D.E. Goldberg . (1989) Genetic algorithms in search, optimization, and machine learning. Artificial Intelligence.
    7. 7)
    8. 8)
    9. 9)
      • 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.
    10. 10)
      • 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.
    11. 11)
      • Hasanzadeh, A., Asaei, B., Emadi, A.: `Optimum design of series hybrid electric buses by genetic algorithm', Proc. IEEE Int. Symp. on Industrial Electronics, 2005 ISIE, 20–23 June 2005, 4, p. 1465–1470.
    12. 12)
    13. 13)

Related content

This is a required field
Please enter a valid email address