Physics-constrained deep neural network method for estimating parameters in a redox flow battery
This work addresses parameter estimation for redox flow batteries, an incremental improvement in battery modeling for energy storage applications.
The paper tackles parameter estimation in a vanadium redox flow battery model by developing a physics-constrained deep neural network method, which improves voltage prediction accuracy and generalization compared to traditional inverse methods.
In this paper, we present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium redox flow battery (VRFB). In this approach, we use deep neural networks (DNNs) to approximate the model parameters as functions of the operating conditions. This method allows the integration of the VRFB computational models as the physical constraints in the parameter learning process, leading to enhanced accuracy of parameter estimation and cell voltage prediction. Using an experimental dataset, 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 parameter values for operating conditions not used in the DNN training.