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arXiv · 2610.08702

Robust Scenario-Based Data-Enabled Predictive Control of a Battery Energy Storage System: An Experimental Study

Sebastian Zieglmeier·Nikolas Recke·Mathias Hudoba de Badyn
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Abstract

Battery energy storage systems must respect strict state-of-charge (SOC) limits to prevent overcharge and deep discharge, a task complicated by measurement noise and costly-to-model nonlinear dynamics. Data-enabled predictive control (DeePC) resolves the costly modeling issue by predicting future behavior directly from data. However, its regularization robustifies only the prediction and not the constraints. Scenario-based DeePC (Scenario-DeePC) extends DeePC with the scenario approach, building constraint robustness fully data-driven from observed prediction errors rather than an assumed disturbance distribution. This paper presents the first real-world deployment of Scenario-DeePC, on a grid-connected battery system at the NEST research facility, whose SOC estimate exhibits abrupt, irregular recalibration jumps in addition to ordinary noise. Compared to standard DeePC, Scenario-DeePC achieves comparable tracking performance with substantially fewer constraint violations. Its adaptive scenario buffer further tightens constraint handling automatically, improving robustness to unpredictable recalibration events.

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Sebastian Zieglmeier, Nikolas Recke, Mathias Hudoba de Badyn. 2026-10-06. Robust Scenario-Based Data-Enabled Predictive Control of a Battery Energy Storage System: An Experimental Study. https://arxiv.org/abs/2610.08702

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