Modeling and Simulation of Solid Oxide Fuel Cell Based on Neural Network

Abstract

In order to study the effects of hydrogen input molar flow, oxygen input molar flow, water vapor input molar flow and real-time temperature on the output voltage of solid oxide fuel cell (SOFC), this paper proposes a method to model SOFC using a BP neural network optimized by immunogenetic algorithm (IA). MATLAB simulation results show that the SOFC model established in this paper can more accurately reflect the actual SOFC input and output characteristics. And the model has a high fit to the real data.