Recently, the appeal of Hybrid Energy Storage Systems (HESSs) has been growing in multiple application fields, such as charging stations, grid services, and microgrids. HESSs consist of an integration of two or more single Energy Storage Systems (ESSs) to combine the benefits of each ESS and improve the overall system performance, e.g., efficiency and lifespan. Most recent studies on HESS mainly focus on power management and coupling between the different ESSs without a particular interest in a specific type of ESS. Over the last decades, Redox-Flow Batteries (RFBs) have received significant attention due to their attractive features, especially for stationary storage applications, and hybridization can improve certain characteristics with respect to short-term duration and peak power availability. Presented in this paper is a comprehensive overview of the main concepts of HESSs based on RFBs. Starting with a brief description and a specification of the Key Performance Indicators (KPIs) of common electrochemical storage technologies suitable for hybridization with RFBs, HESS are classified based on battery-oriented and application-oriented KPIs. Furthermore, an optimal coupling architecture of HESS comprising the combination of an RFB and a Supercapacitor (SC) is proposed and evaluated via numerical simulation. Finally, an in-depth study of Energy Management Systems (EMS) is conducted. The general structure of an EMS as well as possible application scenarios are provided to identify commonly used control and optimization parameters. Therefore, the differentiation in system-oriented and application-oriented parameters is applied to literature data. Afterwards, state-of-the-art EMS optimization techniques are discussed. As an optimal EMS is characterized by the prediction of the system’s future behavior and the use of the suitable control technique, a detailed analysis of the previous implemented EMS prediction algorithms and control techniques is carried out. The study summarizes the key aspects and challenges of the electrical hybridization of RFBs and thus gives future perspectives on newly needed optimization and control algorithms for management systems.

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.