Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/243676 
Year of Publication: 
2019
Citation: 
[Journal:] Energy Reports [ISSN:] 2352-4847 [Volume:] 5 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2019 [Pages:] 1365-1374
Publisher: 
Elsevier, Amsterdam
Abstract: 
Parameter identification of the proton exchange membrane fuel cell (PEMFC) is a good way of increasing their efficiency in the next designs. In this study, an optimized improved Elman neural network based on a new hybrid optimization algorithm is proposed for this purpose. The proposed algorithm is a hybrid algorithm based on a combination of two newly algorithms, the world cup optimization (WCO) and the fluid Search Optimization (FSO) algorithms. The proposed method is applied to improve the method efficiency for estimating the PEMFC model parameters. The method is then validated by four different operational conditions. The optimization algorithm efficiency is also analyzed by comparison with some popular algorithms. Simulation results showed that using the designed method gives higher accuracy forecast for the PEMFC model parameters.
Subjects: 
FSO
Improved Elman neural network
Optimization
Parameter identification
Proton exchange membrane fuel cell
WCO
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
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