Robust Scenario-Based Data-Enabled Predictive Control of a Battery Energy Storage System: An Experimental Study
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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BibTeX
@misc{zieglmeier2026robust,
title = {{Robust Scenario-Based Data-Enabled Predictive Control of a Battery Energy Storage System: An Experimental Study}},
author = {Sebastian Zieglmeier and Nikolas Recke and Mathias Hudoba de Badyn},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.08702},
eprint = {2610.08702},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.08702},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Robust Scenario-Based Data-Enabled Predictive Control of a Battery Energy Storage System: An Experimental Study
AU - Sebastian Zieglmeier
AU - Nikolas Recke
AU - Mathias Hudoba de Badyn
PY - 2026
DA - 2026/10/06
PB - arXiv
DO - 10.48550/arxiv.2610.08702
UR - https://arxiv.org/abs/2610.08702
AB - 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.
ER -