Interval Extension of Neural Network Models for the Electrochemical Behavior of High-Temperature Fuel Cells

Abstract

In various research projects, it has been demonstrated that feedforward neural network models (possibly extended toward dynamic representations) are efficient means for identifying numerous dependencies of the electrochemical behavior of high-temperature fuel cells. These dependencies include external inputs such as gas mass flows, gas inlet temperatures, and the electric current as well as internal fuel cell states such as the temperature. Typically, the research on using neural networks in this context is focused only on point-valued training data. As a result, the neural network provides solely point-valued estimates for such quantities as the stack voltage and instantaneous fuel cell power. Although advantageous, for example, for robust control synthesis, quantifying the reliability of neural network models in terms of interval bounds for the network’s output has not yet received wide attention. In practice, however, such information is essential for optimizing the utilization of the supplied fuel. An additional goal is to make sure that the maximum power point is not exceeded since that would lead to accelerated stack degradation. To solve the data-driven modeling task with the focus on reliability assessment, a novel offline and online parameterization strategy for interval extensions of neural network models is presented in this paper. Its functionality is demonstrated using real-life measured data for a solid oxide fuel cell stack that is operated with temporally varying electric currents and fuel gas mass flows.