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In this paper, we present a physics-informed neural network (PINN) approachfor predicting the performance of an all-vanadium redox flow battery, with itsphysics constraints enforced by a two-dimensional (2D) mathematical model. The2D model, which includes 6 governing equations and 24 boundary conditions,provides a detailed representation of the electrochemical reactions, masstransport and hydrodynamics occurring inside the redox flow battery. To solvethe 2D model with the PINN approach, a composite neural network is employed toapproximate species concentration and potentials; the input and output arenormalized according to prior knowledge of the battery system; the governingequations and boundary conditions are first scaled to an order of magnitudearound 1, and then further balanced with a self-weighting method. Our numericalresults show that the PINN is able to predict cell voltage correctly, but theprediction of potentials shows a constant-like shift. To fix the shift, thePINN is enhanced by further constrains derived from the current collectorboundary. Finally, we show that the enhanced PINN can be even further improvedif a small number of labeled data is available.

Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model