Journal is indexed in following databases:
- SCOPUS
- Web of Science Core Collection - Journal Citation Reports
- EBSCOhost
- Directory of Open Access Journals
- TRID Database - Transportation Research Board
- Index Copernicus Journals Master List
- BazTech
- Google Scholar
2022 Journal Impact Factor - 0.6
2022 CiteScore - 1.7
ISSN 2083-6473
ISSN 2083-6481 (electronic version)
Editor-in-Chief
Associate Editor
Prof. Tomasz Neumann
Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
e-mail transnav@umg.edu.pl
Optimization of Hybrid Propulsion Systems
1 MI-SE@MALTA, MARSEC-XL Foundation, Senglea, Malta
ABSTRACT: Powertrain hybridization permits the benefits of more than one power source to be integrated and exploited for a beneficial effect on an objective, such as reduction of fuel consumption or emissions. Due to their operating profiles however, marine hybrid vessels do not exhibit much opportunity for free energy re-cuperation. Fuel savings can be realized by bettering component operating points, yet this requires correct siz-ing matched to the expected usage. In this paper, a multi-objective genetic algorithm is used to optimally size propulsion components in order to minimize fuel consumption as well as installation weight for a hybrid mo-toryacht operating on a day cruise scenario.
KEYWORDS: Multi-Objective Genetic Algorithm (MOGA), Hybrid Propulsions, Process of Optimization, Ship Propulsion, Propulsion System, Hybrid Propulsion Systems, Fuel Efficiency, Powertrain Hybridization
REFERENCES
Barabino, G., Carpaneto, M., Comacchio, L., Marchesoni, M. & Novella, G. 2009. A new energy storage and conversion system for boat propulsion in protected marine areas. Clean Electrical Power, 2009 International Conference on: 363-369.
Deb, K. 2001. Multi-Objective Optimization using Evolutionary Algorithms. New York: Wiley.
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, Transactions on, vol. 6, no. 2: 182-197.
Desai, C. & Williamson, S.S. 2009. Optimal design of a parallel Hybrid Electric Vehicle using multi-objective genetic algorithms. Vehicle Power and Propulsion Conference, 2009: 871-876.
Ehsani, M., Gao, Y. & Emadi, A. 2010. Modern electric, hybrid electric and fuel cell vehicles. CRC Press.
Grech, A. 2009. A day cruise scenario as an underlying foundation for a hybrid propulsion system optimization. Tech. Rep. MARSEC09-432. Senglea: MARSEC-XL.
Hasanzadeh, A., Asaei, B. & Emadi, A. 2005. Optimum Design of Series Hybrid Electric Buses by Genetic Algorithm. Industrial Electronics, 2005. ISIE 2005. Proceedings of the IEEE International Symposium on, vol. 4: 1465-1470.
Herrera, F., Lozano, M. & Verdegay, J. 1998. Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artificial Intelligence Review, vol. 12: 265-319.
Jain, M., Desai, C. & Williamson, S.S. 2009. Genetic algorithm based optimal powertrain component sizing and control strategy design for a fuel cell hybrid electric bus. Vehicle Power and Propulsion Conference, 2009: 980-985.
Lukic, S., Cao, J., Bansal, R., Rodriguez, F. & Emadi, A. 2008. Energy storage systems for automotive applications. Industrial electronics, IEEE Transactions on, vol. 55, no. 6: 2258-2267.
Schofield, N., Yap, H. & Bingham, C. 2005. Hybrid energy sources for electric and fuel cell vehicle propulsion. Vehicle power and propulsion, 2005 IEEE Conference: 522-529.
Sciberras, E. & Norman R. 2010. Sizing of hybrid propulsion systems. Transactions in Evolutionary Computation: Unpubl.
Woud, H.K. & Stapersma, D. 2002. Design of propulsion and electric power generation systems: 115-120. London: IMarEST.
Citation note:
Sciberras E., Grech A.: Optimization of Hybrid Propulsion Systems. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 6, No. 4, pp. 539-546, 2012
File downloaded 1766 times
