Abstract

The catalyst layer (CL) is a pivotal component of Proton Exchange Membrane (PEM) fuel cells, exerting a significant impact on both performance and durability. Its ink composition can be succinctly characterized by platinum (Pt) loading, Pt/carbon ratio, and ionomer/carbon ratio. The amount of each substance within the CL must be meticulously balanced to achieve optimal operation. In this work, we apply an Artificial Neural Network (ANN) model to forecast the performance and durability of a PEM fuel cell based on its cathode CL composition. The model is trained and validated based on experimental data measured at our laboratories, which consist of data from 49 fuel cells, detailing their cathode CL composition, operating conditions, accelerated stress test conditions, polarization curves and ECSA measurements throughout their lifespan. The presented ANN model demonstrates exceptional reliability in predicting PEM fuel cell behavior for both beginning and end of life. This allows for a deeper understanding of the influence of each input on performance and durability. Furthermore, the model can be effectively applied to optimize the CL composition. This paper demonstrates the immense potential of AI, combined with a high-quality database, to advance fuel cell research.

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 call

Disclaimer: 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.