Designing AI‐Aided Analysis and Prediction Models for Nonprecious Metal Electrocatalyst‐Based Proton‐Exchange Membrane Fuel Cells
Year: 2020
Type: article
Abstract: Traditionally, a larger number of experiments are needed to optimize the performance of the membrane electrode assembly (MEA) in proton-exchange membrane fuel cells (PEMFCs) since it involves complex electrochemical, thermodynamic, and hydrodynamic processes. Herein, we introduce artificial intelligence (AI)-aided models for the first time to determine key parameters for nonprecious metal electrocatalyst-based PEMFCs, thus avoiding unnecessary experiments during MEA development. Among 16 competing algorithms widely applied in the AI field, decision tree and XGBoost showed good accuracy (86.7 % and 91.4 %) in determining key factors for high-performance MEA. Artificial neural network (ANN) shows the best accuracy (R2=0.9621) in terms of predictions of the maximum power density and a decent reproducibility (R2>0.99) on uncharted I-V polarization curves with 26 input features. Hence, machine learning is shown to be an excellent method for improving the efficiency of MEA design and experiments.
Authors: Rui Ding, Ran Wang, Yiqin Ding, Wenjuan Yin, Yide Liu, Jia Li, Jianguo Liu
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Awards: G2394253809, G3713997240, G496579919
OpenAlex ID: W3041568745