A 3D macro-segment network model for vanadium redox flow battery with serpentine flow field

• A macro segment network model for VRFB with serpentine flow field is proposed. • The computational efficiency is greatly improved compared with FEM model. • The battery performance during single and multi-cycle is predicted. • The field distributions of key parameters are in good agreement with FEM model. This paper presents a 3D macro-segment network model for a vanadium redox flow battery with serpentine flow field. The proposed network model is coupled of electrolyte flow module, species transfer module, charge transfer module. In flow resistance network module, the characteristics of electrolyte flow in the serpentine flow channel and under-rib convection in the porous electrode are all considered. In addition, the electrode intrusion and the electrode non-uniform structure caused by compression are taken into account. In species transfer network module, the convection, diffusion and migration between adjacent segments and reversible electrochemical reactions and self-discharge reactions inside the segment are all analyzed in the ion conservation equation. In charge transfer network module, each battery segment, which is composed of the channel, electrode and membrane segment in series, is connected in parallel and has the same output voltage. In this paper, the battery performance including charge-discharge voltages and cell pressure drop under different electrode compression ratios are validated. In the following, the battery performance under single and multiple charge-discharge cycle are investigated using the proposed network model. Besides, the field distribution of key parameters in terms of velocity, pressure, ions concentration and current density are analyzed and validated with finite element method model data. The proposed 3D macro-segment network model is not only able to effectively consider the distribution difference inside the battery caused by the flow field, but also capable of reducing the computational resources, which renders the network model is suitable for the fast prediction of battery performance.