{
  "abstract": "Residential battery energy management systems increasingly rely on model predictive control (MPC), yet most schemes assume a fixed horizon, a noise-free state and an uncalibrated forecaster. This paper proposes DT-AAMPC, an adaptive MPC that combines a Kalman-filtered battery digital twin with an attention-BiLSTM forecaster (TAB-Net) whose attention entropy yields a confidence signal that adapts the controller’s horizon, uncertainty penalty, and state-of-charge back-off online. We evaluate it using corrected code executed on the real dataset at a reduced but realistic validation scale (three seeds, ten training epochs; full protocol: five seeds, forty epochs), against a PID/droop controller, a naive-forecast MPC, and a fixed-horizon ablation (Fixed-DT-MPC) across six household stress-test scenarios. Averaged over eighteen matched blocks, DT-AAMPC lowered net cost to 104.83 currency units (against 149.29, 170.79 and 106.49 for PID, Classical MPC and Fixed-DT-MPC) and cut state-of-health loss to 0.69% (against 1.70%, 2.36% and 0.73%), significant against both classical baselines after Holm–Bonferroni correction (p<0.001); DT-AAMPC was not, however, significantly different from Fixed-DT-MPC on any headline metric (p>0.11), so online adaptation itself is not yet shown to drive the benefit, and self-sufficiency/comfort violation were not significant against either classical baseline. A previously unflagged bug, where ambient temperature was in Fahrenheit, silently treated as Celsius, was found and fixed here; the thermal interlock then never activates across 72 runs, and equivalent-cycle counts fall to a physically unremarkable 0.8, −4.1 per six-day window (against 45–90 previously). Calibration analysis shows the confidence signal is uncorrelated with realised forecast error (ρ=0.047, p=0.568), moderating the confidence-adaptive claims.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8SA, UK"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Muhammed Cavus",
      "orcid": "0000-0002-6231-6129"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cjr95pzapqm35.bib",
  "cited_by": 0,
  "doi": "10.3390/batteries12100410",
  "fulltext_url": null,
  "id": "cjr95pzapqm35",
  "issns": [
    "2313-0105"
  ],
  "landing_url": "https://www.mdpi.com/2313-0105/12/10/410",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2208|Smart Grid Energy Management"
    ]
  ],
  "openalex": "W7221141234",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/cjr95pzapqm35.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:47Z",
      "native_id": "10.3390/batteries12100410",
      "source": "crossref",
      "url": "https://www.mdpi.com/2313-0105/12/10/410"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2313-0105",
      "subfield": "electrochemistry-energy"
    }
  ],
  "title": "Digital-Twin Attention-Augmented Adaptive Model Predictive Control for Degradation-Aware Residential Battery Energy Management",
  "title_text": "Digital-Twin Attention-Augmented Adaptive Model Predictive Control for Degradation-Aware Residential Battery Energy Management",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:47Z",
  "url": "https://research.draconitic.com/paper/cjr95pzapqm35",
  "venue": "Batteries",
  "versions": []
}