Notes from the Field: How AI helps energy storage operators increase revenues

As more battery energy storage systems (BESS) are deployed worldwide, leading operators are turning to predictive battery analytics software to improve battery system safety, performance, and lifetimes.

BESS assets already achieving performance optimization today clearly showcase how innovations in predictive analytics support operations teams. One key area of focus is asset availability, which ties directly to revenue opportunities. Operators using predictive capabilities are increasing system availability by minimizing unplanned maintenance and mitigating safety issues.

Cloud-based analytics software rapidly processes large quantities of field data with approaches based on software engineering, data science, and physics-based battery modeling, including AI. Together, these areas represent the foundation for a new category of software solutions delivering value to BESS owners and operators.

The following case studies frame industry challenges and offer perspective into how leaders are putting solutions into practice, unlocking the value from BESS field data.  

1) Battery Management Systems (BMS) have limited data analytics capabilities, which can result in lost revenues and safety risks.

At a BESS site in the United States this year, predictive analytics software found a weak battery cell in a 90+ MWh asset that was reducing system performance, and could have led to a safety incident if left unchecked. The BMS never flagged the issue. A key challenge for batteries is that the performance of the system is often determined by the weakest cell, which was certainly the case with this asset. After the issue was identified with independent cloud-based analytics, the asset owner notified the system’s hardware integrator, and the module containing the faulty cell was quickly replaced. By replacing the defective cell, the asset returned to full performance.

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