Redox flow battery represents an economically viable energy storage technology that can integrate intermittent renewable energies from solar and wind power into existing electric grids. Besides the conventional all-liquid redox flow battery, there are two different architectures of flow batteries: hybrid and redox targeting flow batteries. Because the reactive solid phase is involved in the system, the energy storage density increases significantly. Therefore, they are considered as ones of the potential solutions for the future large-scale deployment of long-duration energy storage systems (LDES). A digital twin (DT) has been demonstrated as a promising numerical framework and platform to assist in the design, deployment, and operation of LDES. DTs usually require a fast-response model to provide an estimation of the flow battery's performance, which can be a fast-forward physics-based model or a machine learning-based regression model. Many pioneering works and our previous studies have introduced several fast-forward physics-based models for the conventional all-liquid redox flow battery in both inorganic and organic systems. In this study, a semi-analytical model, which can adapt to DTs, is proposed to provide an estimation of the performance of the hybrid and the redox targeting flow battery at different operating conditions.