Temporal dilated convolution and nonlinear autoregressive network for predicting solid oxide fuel cell performance

Solid oxide fuel cells (SOFCs) are promising clean energy technology to produce electricity with zero emission, fuel diversity, and high electrical efficiency. The dynamic behavior of SOFC systems is affected by exogenous inputs and complex internal multi-physics phenomena. This paper introduce a hybrid neural network to build a comprehensive predictive model for SOFC performance. The proposed algorithm includes residual blocks of temporal dilated convolution that are designed to hierarchically extract a high-level hidden abstraction of the temporal dependencies from a sequence of historic output, which contains paramount information about the SOFC dynamics. The influence of the exogenous inputs is extracted through a shallow neural network. The fusion of learned features is then fed to a fully connected network for performance prediction, representing a nonlinear autoregressive exogenous scheme. To train and evaluate the model's performance, experimental data from lab-scale SOFCs under various dynamic operations was collected. Having been validated over diverse operating conditions, the prediction performance of the proposed framework is benchmarked. The results demonstrate our model can more precisely capture the SOFC dynamical behavior and is computationally more efficient than the baseline models. The parallelism of the model holds the potential for real-time control, diagnostics, and optimization of SOFC systems.