The HyStorization project aims to advance the modelling and operational understanding of hybrid electrochemical energy storage systems, focusing on Lithium-ion (Li-ion) and Vanadium Redox Flow Batteries (VRFBs). These technologies are key enablers of flexible, reliable, and scalable grid-scale energy storage. While Li-ion batteries are well-established for high-power applications, VRFBs offer promising advantages for medium- to long-duration storage due to their durability and decoupled energy and power capacities. The primary objective is to develop linearized battery models for both technologies, derived from experimental data, that accurately capture efficiency and power limits as functions of the State of Charge (SoC). These models are intended for integration into Mixed-Integer Linear Programming (MILP) tools to optimize energy dispatch in hybrid storage systems. A comprehensive testing campaign was conducted on three BYD stationary Li-ion battery systems. Due to a malfunction in one unit, the remaining three—of similar age and usage—were treated as a single representative system. A Python-based controller was developed to automate cycling and collect high-resolution data (1-second intervals) via HTTP. The testing protocol included: • Constant power cycles for initial validation and degradation screening. • Constant current cycles for parameter extraction. Key findings include: • A slight but consistent improvement in SoC estimation accuracy using a linear model over a bucket model (~2% reduction in MAE and MSE). • Shorter resampling intervals (e.g., 1-minute vs. 15-minute) improved accuracy, but the most significant reduction in error came from refreshing the SoC with real measurements rather than relying on estimated values. • SoC limits, while useful for safety, were found to be overly restrictive and may not reflect the battery’s full operational flexibility. • Attempts to assess cyclic degradation were inconclusive due to the limited number of cycles and short observation window. The final linear model includes parameters for nominal charge/discharge voltages, inverter efficiencies, and dynamic SoC limits as functions of DC power. These were validated against real operational data and compared with manufacturer-based models. Concerning the VRFB, the project originally planned to conduct targeted tests on the VRFB to: • Evaluate energy efficiency across different SoC levels and operational ranges. • Determine maximum and minimum effective power ratings as functions of SoC. • Support the development of non-linear models that will be linearized for MILP integration. However, due to a malfunction, the VRFB could not be tested as planned. Instead, the projectrelied on previously collected characterization data, which did not fully cover the intended test scope. Despite these limitations, the available data was used to: • Analyse energy efficiency trends across selected states of charge (SoC) and operational conditions. • Estimate effective power ratings within the constraints of the existing dataset. • Support the preliminary development of non-linear models, with the aim of future linearization for MILP integration. While these efforts provided valuable insights, the absence of new experimental data limited the ability to fully capture the unique operational characteristics of VRFBs, such as their decoupled energy and power capabilities and their suitability for long-duration storage. The project is expected to deliver: • Validated, MILP-compatible models for both Li-ion and VRFB technologies. • Enhanced dispatch strategies for hybrid storage systems. • Improved integration of real-time SoC measurements to reduce estimation error. • Recommendations for longer-term testing to better assess degradation and refine model accuracy. In conclusion, the HyStorization project provides a foundational step toward more accurate, data-driven modelling of hybrid storage systems. It highlights the importance of real-time data, flexible modelling approaches, and the need for continued testing to support the evolving role of batteries in grid operations.