Modeling the SOFC by BP neural network algorithm

Solid oxide fuel cells (SOFCs) are complex systems in which electrochemistry, thermophysics and ion conduction occur simultaneously. The coupling of the multi-physics field makes the performance of the SOFC hard to predict. Meanwhile, the high operation temperature prevents a fully aware test of the internal conditions of the SOFC. Artificial intelligence (AI) technology requires a small amount of experimental data to build an accurate model of SOFC. In this study, 30 pieces SOFC stack is fabricated and experimentally tested in different furnace temperatures and the BP neural network, support vector machine (SVM)and random forest (RF) are both used to predict the stack performance. Multiple evaluation criteria such as R 2 , RMSE, MAE, training and testing time are used. The results show that fitting errors of the three algorithms are all within 5%. By analyzing the evaluation criteria, the prediction accuracy, generalization ability and testing time of BP neural network are optimal. The untested data is predicted in order to verify the interpolation of the BP neural network. • The stack of 30 layers of single cells 858 pieces of data are collected through experiments. • Analyzing the performance of stack under four different operating conditions. • A variety of evaluation criteria were selected such as R2、RMSR、MAE、training time and testing time. • The prediction error of BP neural network algorithm is only 1%, better than that of SVM (4%) and RF (6%). • The shortest training time and testing time of BP neural network algorithm are 0.664s and 0.769s.