{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.08702",
  "author_total": 3,
  "authors": [
    {
      "name": "Sebastian Zieglmeier"
    },
    {
      "name": "Nikolas Recke"
    },
    {
      "name": "Mathias Hudoba de Badyn"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/31ne7cdy14m0a.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.08702",
  "fulltext_url": null,
  "id": "31ne7cdy14m0a",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.08702",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2207|Advanced Control Systems Optimization"
    ]
  ],
  "openalex": "W7220948114",
  "pdf_url": "https://arxiv.org/pdf/2610.08702",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/31ne7cdy14m0a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:26Z",
      "native_id": "2610.08702",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.08702"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Robust Scenario-Based Data-Enabled Predictive Control of a Battery Energy Storage System: An Experimental Study",
  "title_text": "Robust Scenario-Based Data-Enabled Predictive Control of a Battery Energy Storage System: An Experimental Study",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:26Z",
  "url": "https://research.draconitic.com/paper/31ne7cdy14m0a",
  "venue": "arXiv",
  "versions": []
}