A digital twin technology for BESS from the FULLEST project, now integrated into 3E’s SynaptiQ, sets new standards in accuracy and asset management.
How digital twin technology for BESS strengthens performance and reliability
3E, together with academic and industrial partners, has achieved a major milestone with the FULLEST (Flexible Utility-Scale Energy Storage) project, introducing a digital twin technology for battery energy storage systems (BESS). Now integrated into 3E’s SynaptiQ platform, this innovation marks a significant step forward in performance management for large-scale storage assets.
Tackling Europe’s energy storage challenges
The FULLEST project addresses critical hurdles in the fast-growing European energy storage market. Belgium alone has contracted more than 1.1 GW of capacity through its Capacity Remuneration Mechanism and hosts large-scale projects led by companies such as ENGIE and TotalEnergies.
Technical accuracy grounded in physics
The digital twin developed under FULLEST delivers superior accuracy compared to traditional battery management methods thanks to its physics-based modeling. It estimates state of health (SoH) with minimal deviation from physical systems, outperforming conventional battery management systems.
Key technical elements
- Advanced electrochemical modeling: The digital twin applies PyBAMM (Python Battery Mathematical Modeling) with a single particle model (SPM), simulating SEI (solid electrolyte interphase) growth that drives lithium consumption and resistance increase.
- Real-time state estimation: The system processes voltage, current, and temperature data from the PCS level, normalizing them at cell level to boost precision and ensure continuous synchronization with the physical system.
- Computational efficiency: Optimized models deliver tenfold speed improvements versus full-order models while maintaining accuracy. The SPM can simulate one day of PCS operation in 0.5 seconds, capturing diffusion, charge transfer, and aging reactions.
- Validated results: VUB calibrated the degradation model using aging tests on Porsche Taycan cells provided by Revolta. The tests covered variable load conditions, simulating grid services such as frequency and voltage control. Results showed that the model error was below 1% of the cell’s nominal capacity over a full year of testing.
A battery energy storage system (BESS) using second-life Taycan cells is currently monitored in SynaptiQ. The implementation of the digital twin provides a validated method to calculate state of health (SoH), replacing the static 100% values from the BMS that offered no actionable insights.
Industry-academia collaboration driving BESS management
FULLEST brought together complementary expertise from five partners:
- 3E led commercial integration, embedding the digital twin into SynaptiQ and designing data architecture and preprocessing modules.
- Vrije Universiteit Brussel (VUB) developed electrochemical models, SEI degradation simulations, and validation protocols.
- Sirris contributed machine learning algorithms for anomaly detection and reduced-order modeling.
- Revolta provided real-world operational data from BESS assets for field validation.
- Eneco guided market-driven applications, linking the twin to grid services and profitability optimization.
Three strategic use cases for digital twin in BESS
- Performance optimization: Operators can maximize energy yield while managing degradation risk by modeling SEI growth relative to state of charge.
- Anomaly detection: Continuous monitoring of thermal and operational patterns identifies early signs of degradation using clustering algorithms.
- Predictive maintenance: Accurate degradation modeling allows reliable estimation of remaining useful life, enabling cost-effective maintenance and extending asset lifespan.
Market evolution and FULLEST’s positioning
The battery storage industry is shifting rapidly toward advanced Asset Performance Management (APM). Emerging trends include:
- Physics-based models overtaking equivalent circuit models for higher accuracy of electrochemical and thermal processes.
- Real-time digital twin implementations, with cloud and edge variations showing deviations below 2% from real-world systems.
- More advanced anomaly detection methods, such as time series decomposition, isolation forests, and convolutional autoencoders.
- Predictive maintenance practices already reducing operational costs by anticipating defective modules.
The success of the FULLEST project positions 3E and its partners at a competitive level in the field of asset performance management for battery energy storage systems (BESS). With battery costs falling and installations rapidly scaling, demand for advanced digital solutions will continue to grow. The technology developed through FULLEST provides a strategic advantage and highlights the value of collaboration between academia and industry in tackling complex technological challenges.
For more information on the FULLEST project and SynaptiQ’s BESS asset management capabilities, contact the SynaptiQ team.
