Optimal estimation of the Proton Exchange Membrane Fuel Cell model parameters based on extended version of Crow Search Algorithm

Lu X., Kanghong D., Guo L., Wang P., YILDIZBAŞI A.

Journal of Cleaner Production, vol.272, 2020 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 272
  • Publication Date: 2020
  • Doi Number: 10.1016/j.jclepro.2020.122640
  • Journal Name: Journal of Cleaner Production
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Chimica, Communication Abstracts, Compendex, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Ankara Yıldırım Beyazıt University Affiliated: Yes

Abstract

© 2020 Elsevier LtdThis paper presents a new method for optimal model parameters estimation of the Proton Exchange Membrane Fuel Cell (PEMFC). The purpose is to find the optimal parameters such that the integral of the absolute error (IAE) between the output voltage of the real stack and the achieved data has been minimized. Different metaheuristics can be used for the minimization. Most of them have their shortcomings. Therefore, in this study, a new improved version of the Crow Search Algorithm (ICSA) is suggested to increase the algorithm diversity and to prevent trapping in the local minimum. The study has been then applied to two practical case studies, Nexa PEMFC and NedSstack PS6 PEMFC and the results are compared with some state of art methods, COA, (N + λ) - ES algorithm and basic CSA to show the better fitting of the suggested algorithm toward the others. The results show that IAE value for the Nexa PEMFC in ICSA, CSA, COA, and EA are 14, 16, 15, and 23, respectively and the IAE value for the NedStack PS6 PEMFC in ICSA, CSA, COA, and EA are 1.52, 1.76, 1.63, and 2.08, respectively that indicate the higher performance of the suggested algorithm when applying to both case studies.