Developed Coyote Optimization Algorithm and its application to optimal parameters estimation of PEMFC model

Artikel

In this paper, a new approach has been introduced for optimal parameter estimation of a proton exchange membrane fuel cell (PEMFC) model. The main purpose is to minimize the total error between the empirical data and the proposed method by optimal parameter selection of the model. The methodology is based on using a newly introduced developed version of the Coyote Optimization Algorithm (DCOA) for determining the value of the unknown parameters in the model. Two different PEMFC models including 2 kW Nexa FC and 6kW NedSstack PS6 FC are adopted for validation and the results are compared with the empirical data and some well-known methods including conventional COA, Seagull Optimization Algorithm, and (N + λ) - ES algorithm to show the proposed method's superiority toward the literature methods. The final results declared a satisfying agreement between the proposed DCOA and the empirical data. The results also declared the excellence of the presented method toward the other compared methods.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Energy Reports ; ISSN: 2352-4847 ; Volume: 6 ; Year: 2020 ; Pages: 1106-1117 ; Amsterdam: Elsevier

Classification and Topics

• Classification: Wirtschaft
• Subject: Coyote Optimization Algorithm ; Developed ; Electrical circuit equivalent ; Model parameter estimation ; Proton exchange membrane fuel

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Yuan, Zhi ; Wang, Weiqing ; Wang, Haiyun ; Yildizbasi, Abdullah
• Event: Veröffentlichung
• (who): Elsevier
• (where): Amsterdam
• (when): 2020

Further information

• DOI: doi:10.1016/j.egyr.2020.04.032
• Handle: http://hdl.handle.net/10419/244105
• Last update: 10.03.2025, 11:41 AM CET