Abstract
This investigation explored the performance of PEMFC for varying ambient conditions with the aid of an adaptive neuro-fuzzy inference system. The experimental data obtained from the laboratory were initially trained using both the input and output parameters. The model that was trained was then evaluated using an independent variable. The training and testing of the model were then utilized in the prediction of the cell-characteristic performance. The model exhibited a perfect correlation between the predicted and experimental data, and this stipulates that ANFIS can predict characteristic behavior of fuel cell performance with very high accuracy.
Highlights
As the world continues to strive for alternative energy generation media in order to fight climate change, energy generation sources for electricity production must be critically reviewed [1,2,3]
This investigation explored the application of an adaptive neuro-fuzzy inference system as a prediction technique for fuel cell experimental data obtained under laboratory conditions
This research was aimed at predicting current and voltage based on experimental data from proton exchange membrane fuel cell at different operational conditions
Summary
As the world continues to strive for alternative energy generation media in order to fight climate change, energy generation sources for electricity production must be critically reviewed [1,2,3]. Fossil product over the last few years has been the major source of energy generation worldwide [4,5,6,7]. The world today considers it a key contributor to carbon emissions into the atmosphere the urgent need to consider an alternative [8,9,10]. Renewable sources are perceived as the best replacement for these fossil products [11,12]. Fossil product prices are unstable, and the worst part is their harmful effect on the environment. Several research activities today are geared towards the optimization of operational conditions of fuel cells [13]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
