In this paper, we present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery, with its physics constraints enforced by a two-dimensional (2D) mathematical model. The 2D model, which includes 6 governing equations and 24 boundary conditions, provides a detailed representation of the electrochemical reactions, mass transport and hydrodynamics occurring inside the redox flow battery. To solve the 2D model with the PINN approach, a composite neural network is employed to approximate species concentration and potentials; the input and output are normalized according to prior knowledge of the battery system; the governing equations and boundary conditions are first scaled to an order of magnitude around 1, and then further balanced with a self-weighting method. Our numerical results show that the PINN is able to predict cell voltage correctly, but the prediction of potentials shows a constant-like shift. To fix the shift, the PINN is enhanced by further constrains derived from the current collector boundary. Finally, we show that the enhanced PINN can be even further improved if a small number of labeled data is available.

翻译：本文提出了一种物理信息神经网络方法，用于预测全钒氧化还原液流电池的性能，其物理约束由二维数学模型强制执行。该二维模型包含6个控制方程和24个边界条件，详细描述了氧化还原液流电池内部发生的电化学反应、质量传输和流体动力学。为采用PINN方法求解二维模型，采用复合神经网络近似物质浓度和电势；根据电池系统的先验知识对输入和输出进行归一化；首先将控制方程和边界条件缩放至量级约为1，然后通过自加权方法进一步平衡。数值结果表明，PINN能够正确预测电池电压，但电势预测存在类常数偏移。为修正该偏移，通过增加基于集电极边界的额外约束来增强PINN。最后，我们证明，当少量标注数据可用时，增强型PINN可进一步提高性能。