PNNL

Abstract

Numerical modeling and simulation have become indispensable tools to advance a comprehensive understanding of the underlying mechanisms and cost-effective process optimization and control of flow batteries. In this study, we propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high accuracy voltage predictions in the vanadium redox flow batteries (VRFB). The idea of the PCDNN approach is to enforce the physics-based zero-dimensional (0D) VRFB model in the neural network to ensure the model generalization for various battery operation conditions. Limited by the simplifications of the 0D model, the PCDNN can not capture sharp voltage changes in the extreme SOC regions. To improve the accuracy of voltage prediction at extreme ranges, we introduce a second (enhanced) DNN to mitigate the prediction errors carried from the 0D model itself, and call the resulting approach enhanced PCDNN (ePCDNN). By comparing the model prediction with experimental data, we demonstrate that the ePCDNN approach can accurately capture the voltage response throughout charge-discharge cycle, including the tail region of the voltage discharge curve. Compared to the standard PCDNN, the prediction accuracy of the ePCDNN is significantly improved. The loss function for the training of the ePCDNN is designed to be flexible by adjusting the weights of the physics-constrained DNN and the enhanced DNN. This allows the ePCDNN framework to be transferable to battery systems with variable physical model fidelity.