A recent case study has introduced digital twins as a promising tool for identifying hidden battery degradation, after detecting a 4% discrepancy in the state of health (SoH) between its own calculations and those of the battery management system (BMS) at an energy storage site in the Netherlands.

Figure 1. Digital Twin Reveals.

Battery energy storage systems (BESS), which store electricity for later use, play a critical role in grid balancing and the integration of renewable energy sources. However, accurately monitoring their performance and lifespan remains a significant challenge, prompting engineers to explore more advanced diagnostic tools. Figure 1 shows Digital Twin Reveals.

The study, based on a 21.6 megawatt-hour (MWh) BESS located in Zuidbroek and operated by ProfiNRG for independent power producer Sunvest, suggests that digital twins—virtual models that replicate the behavior and condition of physical systems in real time—can uncover issues overlooked by conventional monitoring.

The Digital Twin Advantage

IT service company 3E, which published the accompanying white paper, investigated how physics-based digital twins can expose hidden signs of battery degradation and help improve system performance.

Unlike solar PV systems, where degradation tends to follow a linear, predictable pattern, battery wear depends heavily on usage and is far more complex to assess [1]. To address this, 3E developed a digital twin incorporating real-time operational data, equipment manufacturer specifications, and market signals, along with detailed thermal, electrical, and chemical modeling.

This virtual representation provides operators with a dynamic, cell-level view of battery health, enabling more accurate diagnostics and performance forecasting.

Transforming Energy Storage

At the Zuidbroek facility, this approach revealed a 4% mismatch in SoH between the digital twin and the BMS, highlighting a critical blind spot in traditional system monitoring.

“The Zuidbroek project showed that advanced storage operations can thrive—even in demanding, service-driven markets—when data is leveraged as a strategic asset rather than just a byproduct,” the study explained.

The digital twin's ability to detect this discrepancy enabled a more reliable and detailed understanding of how operational behavior influences battery degradation.

According to 3E, this case highlights the broader potential of digital twins in asset management—not only providing deeper visibility but also empowering operators to make smarter decisions that preserve asset value and ensure long-term profitability.

While the study didn’t specify what operational changes were made as a result, it stressed the importance of detecting such discrepancies. Although the analysis focused on a single site, the implications extend across the energy storage industry.

“Physics-based digital twins are leading the way in storage system optimization,” the white paper concluded. “Now is the time to embrace the change.”

Reference:

1. https://interestingengineering.com/innovation/digital-twin-battery-blind-spot-bess

Cite this article:

Keerthana S (2025), A Digital Twin Reveals Overlooked Battery Blind Spot in Energy Storage Systems, AnaTechMaz, pp.332