Characterization tests for hybrid storage systems – Li-ion and Va-na dium Redox Flow Batteries (HyStorization)

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 sys
tems. 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 auto
mate 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 measure
ments 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 integra
tion. 
However, due to a malfunction, the VRFB could not be tested as planned. Instead, the project
relied 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 opera
tional 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 line
arization 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 de
coupled 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.