Volume 68

An artificial neural network-based framework for performance prediction and optimization of PEMFCs Tian Zhang, Zhengmeng Hou, Qianjun Chen, Qichen Wang, Yilin Guo, Liangchao Huang, Lin Wu, Shengyou Zhang, Nan Cai

Abstract

Predicting and optimizing the performance of proton exchange membrane fuel cells (PEMFCs) poses significant challenges because of the large number of design variables and their intricate interdependencies over the full operating voltage range. In this study, with the power of an artificial neural network (ANN), an ANN-based framework is proposed to perform prediction and optimization of PEMFC performance efficiently. A comprehensive dataset is produced using a validated multiphysics model, enabling robust training of ML models. An ANN achieves high predictive accuracy (R² > 0.999). Results show that with this framework, predictions agree well with experimental data, with predictive error below 8.1%. Furthermore, the framework enables quick identification of optimal design variables for multi-objective optimization. Overall, this work highlights an ANN-based research paradigm for designing efficient, application-tailored fuel cell systems, thereby reducing experiments and shortening design cycles.

Keywords proton exchange membrane fuel cells, performance prediction, multi-objective optimization, artificial neural network