Performance prediction of PEM fuel cell with wavy serpentine flow channel by using artificial neural network

SEYHAN M., AKANSU Y. E., Murat M., KORKMAZ Y., Akansu S. O.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, vol.42, no.40, pp.25619-25629, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 42 Issue: 40
  • Publication Date: 2017
  • Doi Number: 10.1016/j.ijhydene.2017.04.001
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.25619-25629
  • Keywords: PEM fuel cell, Wavy shaped serpentine, Artificial neural network, BIPOLAR PLATES, DESIGN, OPTIMIZATION, PARAMETERS, PARALLEL, SIMULATION, PRESSURE, SURFACE, FIELDS, LENGTH
  • Erciyes University Affiliated: Yes


Effects of serpentine flow channel having sinusoidal wave at the rib surface on performance of PEMFC having 25 cm(2) active area are investigated at different flow rates, three different amplitudes changing from 0.25 mm to 0.75 mm and three different cell operation temperatures. A proton exchange membrane fuel cell (PEMFC) is modeled for the prediction of the output current by using artificial neural network (ANN) that is utilized the aforementioned experimental parameters. Effect of hydrogen and air flow rate, the fuel cell temperature, amplitude of channel is tested. The results indicated that model C1 having lowest amplitude is enhanced maximum power output up to 20.15% as compared to indicated conventional serpentine channel (model C4) for 0.7 SLPM H-2 and 1.5 SLPM air and also model C1 has better performance than C2, C3 and C4 models. The maximum power output is augmented with increasing the cell temperature due to raising the fuel and oxidant diffusion ratio. Cell temperature, amplitude, H2 and air flow rate and input voltage is used as input variables in train and test of the developing ANN model. MAPE of training and testing is determined as 239 and 2.059, respectively. Prediction results of developed ANN model including two hidden layer shows similar trend with experimental results. Developed ANN model can be used to both decrease the number of required experiments and find the optimum operation condition within the range of input parameters. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.