Application of the improved chaotic grey wolf optimization algorithm as a novel and efficient method for parameter estimation of solid oxide fuel cells model
In this paper, important functional parameters of solid oxide fuel cells are identified by introducing a novel high-speed optimization method, namely adaptive chaotic grey wolf optimization algorithm. The suggested optimization method is obtained by combining the adaptive grey wolf optimization and...
Gespeichert in:
| Veröffentlicht in: | International journal of hydrogen energy Jg. 46; H. 73; S. 36454 - 36465 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Ltd
22.10.2021
|
| Schlagworte: | |
| ISSN: | 0360-3199, 1879-3487 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Zusammenfassung: | In this paper, important functional parameters of solid oxide fuel cells are identified by introducing a novel high-speed optimization method, namely adaptive chaotic grey wolf optimization algorithm. The suggested optimization method is obtained by combining the adaptive grey wolf optimization and chaotic grey wolf optimization algorithms. The chaotic algorithm is applied to the basic grey wolf optimization to achieve higher convergence speed, keep the population's diversity, and provide an initial population with uniform distribution. Besides, a nonlinear convergence factor is defined for balancing the global and local exploration abilities. Employing the improved convergence factor resulted in a new version of the grey wolf optimization algorithm, namely adaptive grey wolf optimization algorithm. Adaptive chaotic grey wolf optimization algorithm adopts the advantages of both chaotic grey wolf optimization and adaptive grey wolf optimization methods simultaneously. The adaptive grey wolf optimization algorithm is applied to a 5 kW dynamic tubular stack. The results of the simulation report the lowest values of mean squared error, higher accuracy, higher robustness, and high convergence speed for the adaptive grey wolf optimization algorithm compared to some well-known optimization methods. Besides, the proposed method shows a good agreement with experimental results with lower computational difficulty.
[Display omitted]
•An adaptive chaotic grey wolf optimization for parameter identification of solid oxide fuel cells is developed.•The proposed ACGWO algorithm is applied on a 5 kW tubular SOFC stack.•Various evaluation criteria and other parameter estimation techniques are discussed.•A comprehensive comparison analysis of different identification approaches is presented.•ACGWO is a competitive method in terms of accuracy, robustness, convergence and statistics. |
|---|---|
| ISSN: | 0360-3199 1879-3487 |
| DOI: | 10.1016/j.ijhydene.2021.08.174 |