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Fuel cell power management using genetic expression programming in all-electric ships

Abkenar, AT, Nazari, A, Jayasinghe Arachchillage, SDG ORCID: 0000-0002-3304-9455, Kapoor, A and Negnevitsky, M ORCID: 0000-0002-5130-419X 2017 , 'Fuel cell power management using genetic expression programming in all-electric ships' , IEEE Transactions on Energy Conversion, vol. 32, no. 2 , pp. 779-787 , doi: 10.1109/TEC.2017.2693275.

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Abstract

All-electric ships (AES) are considered as an effective solution for reducing greenhouse gas emissions as they provide a better platform to use alternative clean energy sources such as fuel cells (FC) in place of fossil fuel. Even though FCs are promising alternative, their response is not fast enough to meet load transients that can occur in ships at sea. Therefore, high-density rechargeable battery storage systems are required to achieve stable operation under such transients. Generally, in such hybrid systems, dc/dc converters are used to interface the FC and battery into the dc link. This paper presents an intelligent FC power management strategy to improve FC performance at various operating points without employing dc/dc interfacing converters. A hybrid AES driveline model using genetic programming is utilized using Simulink and GeneXProTools4 to formulate operating FC voltage based on the load current, FC air, and fuel flow rates. Genetic algorithm is used to adjust air and fuel flow rates to keep the FC within the safe operating range at different power demands. The proposed method maintains FC performance as well as reduces fuel consumption, and, thereby, ensures the optimal power sharing between the FC and the lithium-ion battery in AES application.

Item Type: Article
Authors/Creators:Abkenar, AT and Nazari, A and Jayasinghe Arachchillage, SDG and Kapoor, A and Negnevitsky, M
Keywords: all-electric ships, FC control strategy, FC power Management, fuel cells, genetic algorithms
Journal or Publication Title: IEEE Transactions on Energy Conversion
Publisher: IEEE
ISSN: 0885-8969
DOI / ID Number: 10.1109/TEC.2017.2693275
Copyright Information:

© 2017 IEEE.

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