Digital twins tackle performance by uncovering battery blind spots

Operational challenges in scaling BESS projects, such as fragmented system visibility and battery degradation are ongoing concerns for the industry.

A recent white paper by 3E, “Mastering storage performance: a digital twin approach for real-world results,” highlights that operators frequently grapple with siloed data from multiple OEMs, hindering performance analysis. The paper notes that, unlike PV assets, BESS degradation is acutely linked to operational strategies which can creates unusual tension when markets reward aggressive cycling for services like frequency regulation; directly impacting long-term battery health.

The 3E white paper advocates for physics-based digital twins as a strategic solution. These systems, as detailed in the report, provide a granular understanding of asset health and performance in real-time by integrating OEM data, live operational data, and market inputs with detailed electrical, chemical, and thermal battery models.

The paper presents a case study from a 21.6 MWh BESS facility in Zuidbroek, Netherlands, operated by ProfiNRG for IPP Sunvest, which was given a physics-based digital twin model to track it. The battery was monitored as it balanced grid services against long-term asset integrity in a volatile market. According to the study, real-time tracking of key metrics were available down to the cell level, including state of charge.

The digital twin was said to identify a 4% state of health (SoH) discrepancy between its own estimation and the value provided by the BESS’s onboard battery management system (BMS). This, the report states, without revealing full details of subsequent operational changes, allowed for an accurate diagnosis of how specific system operations were affecting battery degradation. Still the 4% figure is significant: that could reveal the BMS overestimating the battery’s true health, or underreporting the state of health, which may mean missed opportunities for gaining revenue.

By facilitating such independent analytics, as suggested in the white paper, operators can transition from reactive to proactive operational strategies. This allows for direct changes to be made, for example, to cycling profiles, plus more accurate maintenance or replacement cost forecasting.