PNNL

Abstract

In this paper, we present a physics-constrained deep neural network (DNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium redox flow battery (VRFB) that we refer to as PCDNN. In this approach, we use DNNs to approximate the model parameters as functions of the operating conditions. It allows the integration of the VRFB computational models as the physical constraints in the parameter learning process, leading to the enhanced accuracy of parameter estimation and the cell voltage prediction. Using an experimental data set, we demonstrate that the PCDNN method can estimate model parameters for a range of operating conditions and improve the 0D model prediction of voltage compared to the 0D model prediction with constant operation-condition-independent parameters estimated with traditional inverse methods. We also demonstrate that the PCDNN approach has an improved generalization ability for estimating parameters values for operating conditions not used in the DNN training.