Prediction of polarization curves of PEMFC membrane electrode assembly using artificial intelligence technics

Authors : Haydar Bayar, Erkan Dursun

Pages : 1538-1544

Doi:10.28948/ngumuh.1536258

View : 60 | Download : 83

Publication Date : 2024-10-15

Article Type : Research Paper

Abstract :Proton exchange membrane fuel cells (PEMFCs) are used commercially in automobiles, buses, uninterruptible power supplies, and combined heat power systems, holding a significant place in the fuel cell market. Fuel cell performance is characterized by polarization curves in design and manufacturing processes. This study predicts a PEMFC’s polarization curves using comparative artificial intelligence (AI) models trained and tested under different operational conditions. The AI model inputs are cell temperature, humidity, anode-cathode flow, and membrane resistance. The outputs are cell voltage and current density. The model outputs are compared with experimental values for 50°C, 100% humidity using MATLAB software. The average Root Mean Square Error (RMSE) for the ANFIS prediction is 0.056112, while for the ANN prediction it is 0.011919. These results indicate that the Artificial Neural Network (ANN) method outperforms the Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the behavior of the PEMFC’s Membrane Electrode Assembly (MEA). The models showed promising results with high accuracy.
Keywords : Yapay zeka, Elektrokimyasal cihazlar, Enerji Dönüşümü, Yakıt hücreleri