Hydrogen fuel cell parameter estimation using an innovative hybrid estimation model based on deep learning and probability pooling
Yazarlar (2)
Prof. Dr. Cem EMEKSİZ Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Mustafa Tan Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | International Journal of Hydrogen Energy (Q1) | ||
| Dergi ISSN | 0360-3199 Dergi Bilgileri (2024) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2024 |
| Cilt / Sayı / Sayfa | 110 / 1 / 445–456 | DOI | 10.1016/j.ijhydene.2025.02.272 |
| Makale Linki | https://www.sciencedirect.com/science/article/pii/S0360319925008493 | ||
| UAK Araştırma Alanları |
Yenilenebilir Enerji Sistemleri
|
||
| Özet |
| Developing a suitable model for the reliable performance of Polymer Electrolyte Membrane Fuel Cells (PEMFC) is important. However, the nonlinearity, computational complexity of the mathematical models used and the electrochemical nature of the system make it difficult to model. One of these challenges is the determination of model parameters. Parameters are usually determined using experimental data or numerical simulations, but this process is time-consuming and costly. Therefore, the Probability pool method and Convolutional Neural Network were used in this study for the first time to determine the PEMFC parameters. This hybrid approach constitutes the unique and innovative aspect of the study. With the model created, a classification map was used to identify the PEMFC parameters more accurately and quickly. This study offers a new perspective on data-driven model structures in order to provide … |
| Anahtar Kelimeler |
| Convolutional neural network | Deep learning | Hydrogen production | Parameter estimation | Polymer electrolyte membrane fuel cells |
Science Direct
Hydrogen fuel cell parameter estimation using an innovative hybrid estimation model based on deep learning and probability pooling
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 3 |
| Scopus | 3 |
| Google Scholar | 4 |