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

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

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.

Suggested Citation

  • Wang, Nan & Wang, Dongxuan & Xing, Yazhou & Shao, Limin & Afzal, Sadegh, 2020. "Application of co-evolution RNA genetic algorithm for obtaining optimal parameters of SOFC model," Renewable Energy, Elsevier, vol. 150(C), pages 221-233.
    • Handle: RePEc:eee:renene:v:150:y:2020:i:c:p:221-233
    DOI: 10.1016/j.renene.2019.12.105
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    5. Wang, Erlei & Xia, Jiangying & Li, Jia & Sun, Xianke & Li, Hao, 2022. "Parameters exploration of SOFC for dynamic simulation using adaptive chaotic grey wolf optimization algorithm," Energy, Elsevier, vol. 261(PA).
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    7. Fathy, Ahmed & Babu, Thanikanti Sudhakar & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Yousri, Dalia, 2022. "Recent approach based heterogeneous comprehensive learning Archimedes optimization algorithm for identifying the optimal parameters of different fuel cells," Energy, Elsevier, vol. 248(C).
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