Analyzing Experimental Design and Input Data Variation of a Vanadium Redox Flow Battery Model

Vanadium redox flow batteries (VRFB) are a fertile energy storage technology especially for customized storage applications with special energy and power requirements. The dimensioning and control of these storages is mostly calculated beforehand using battery models in embedded simulation structures. To cover various stack designs, chemistries, application strategies and system architectures, battery simulation models should be validated with different experimental input data and thus show universal functionality. In this study the functionality of a grey box VRFB model using current, voltage and state of charge (SOC) of a 10 kW/100 kWh VRFB as input data are validated for an adapted input data set using of a 5 kW/10 kWh VRFB. This model is designed for stationary applications of VRFB only. The contribution of this study is (i) to apply a suitable SOC conversion method to the raw data from the used 5 kW VRFB system, (ii) to adapt the modeling code for broader use and integration of the SOC conversion, (iii) to validate the functionality and (iv) to investigate the influence of constant current and constant voltage phases in the raw data on the accuracy of the model. A comparison of experimental data between different redox flow batteries shows that most VRFB measure the open circuit voltage (OCV) to calculate the SOC of the battery. Using the calculated SOC as an input data the proposed simulation model need to be adapted and a method is applied to use OCV input data for model validation. Although simulation models in general often assume linearity between SOC and OCV, the study showed sufficient accuracy using polynomic fitting of second order. Applying a parametrization process the results of the simulation model are compared to the raw data and the scope of application of the grey box VRFB model is defined. While using the dominant constant current phase for the charging and discharging cycle, the grey box simulation model has been sufficiently parametrized and validated for adapted input data.