Application of co-evolution RNA genetic algorithm for obtaining optimal parameters of SOFC model

Nan Wang, Dongxuan Wang, Yazhou Xing, Limin Shao and Sadegh Afzal

Renewable Energy, 2020, vol. 150, issue C, 221-233

Abstract: The operational and structural designing of Solid Oxide Fuel Cells are in high importance to be appropriately optimized to obtain the best efficiency. While the efficiency of the SOFCs is strictly associated with the critical parameters related to the system’s internal physical and electrochemical processes, explicit recognition of the parameters is crucial to model the SOFC characteristic curve (V−I). The main contribution of the given study is the utilization of the co-evolution RNA genetic algorithm (coRNA-GA) for the identification of the parameter precise values. The objective function is considered to be the Mean Squared Errors of the experimental and modeling output voltage that is intended to minimize it. The coRNA-GA method has been illustrated to be a competitor algorithm with other well-known schemes. The coRNA-GA method which is inspired by the biological Ribonucleic Acid encodes the chromosomes using RNA nucleotide and some operators. The efficiency of the proposed coRNA-GA is evaluated through experimental data and some well-known functions. The laboratory data obtained from a 5kW Solid Oxide Fuel Cell stack demonstrates that the coRNA-GA is capable of obtaining a desiring compromise between the exploitation and exploration comparing to different methods. Furthermore, the coRNA-GA sensitivity to changes in the population size is experimentally studied.

Keywords: SOFC; coRNA-GA; Parameter identification; Comparative study (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148119319779
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:150:y:2020:i:c:p:221-233

DOI: 10.1016/j.renene.2019.12.105

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().