Optimization of solid oxide fuel cell power generation voltage prediction based on improved neural network

This paper proposes a method for predicting the generation voltage of a solid oxide fuel cell based on the data results of a stand-alone solid oxide fuel single cell (SOFC) simulation model under ideal conditions, with the aim of improving the generation efficiency and extending the service life of the solid oxide fuel cell. In this paper, a modified BP neural network algorithm is used to improve the prediction accuracy of the solid oxide fuel cell generation voltage by using the whale algorithm to optimize the BP neural network model to improve its convergence and achieve the effect of improving the prediction accuracy. Firstly, the characteristics of the independent solid oxide fuel cell are introduced and simulated. Second, the long short-term memory network model (LSTM), linear regression network model and BP neural network are simulated and compared, and the results show that the BP neural network prediction model is more accurate and can be optimized and improved. Finally, the BP neural network is optimized and simulated using the whale algorithm, and the simulation results show that the method has better convergence and higher prediction accuracy than the traditional BP neural network prediction model.