{
  "abstract": "Detecting internal short-circuit (ISC) conditions in lithium-ion batteries is difficult because fault-induced voltage deviations are often masked by load transients, state-of-charge variation, model mismatch, and measurement noise. This study proposes a noise-aware calibrated multi-scale voltage-residual method for ISC detection. A healthy-voltage predictor is first trained using normal operating data to construct a reference trajectory, and measured-minus-predicted voltage residuals are then calibrated with train-normal statistics. Multi-scale and current-coupled residual descriptors are extracted to characterize both local deviations and persistent voltage mismatch. The experimental protocol uses experiment-record/source-file-grouped training, validation, and locked test partitions; all threshold selection and decision refinement are performed using validation data only. On the clean locked test set under this protocol, the final detector achieved F1 = 0.8100, ROC-AUC = 0.8902, PR-AUC = 0.9346, and balanced accuracy = 0.8026. Its recall was 0.7393, corresponding to 3005 missed ISC windows among 11,525 ISC-labeled test windows, i.e., a false-negative (miss) rate of 0.2607 (26.07%). Relative to the calibrated multi-scale baseline (F1 = 0.8291, specificity = 0.5504, FAR = 0.4496), the final robust rule trades some F1 for substantially higher specificity (0.8659) and a lower FAR (0.1341); its principal incremental benefit is therefore false-alarm control rather than uniform superiority across metrics. Under the fixed-rule artificial Gaussian voltage-noise stress test, ROC-AUC is 0.8432 with FAR = 0.1149 at the 0.10% nominal perturbation level, but ROC-AUC decreases to 0.7021 at 0.50% and to 0.6249 at 2.00%, with FAR increasing to 0.5235 and 0.7469, respectively. The noise evidence therefore supports low-noise false-alarm mitigation under the specified Gaussian protocol, not broad robustness to substantial or realistic sensor noise. These results support experiment-record/source-file-grouped offline evaluation of the proposed residual formulation. The supervised endpoint is binary normal-versus-resistor-emulated ISC classification; short-circuit resistance Rsc is not a prediction target. The results do not by themselves establish cell-level transferability, realistic sensor robustness, quantitative Rsc estimation, or online deployment. Because the public release does not provide a verified pointwise ISC-onset annotation, detection delay, pre-onset false-alarm rate, warning lead time, and horizon-dependent performance cannot be computed; accordingly, this study does not claim early detection.",
  "abstract_shown": true,
  "affiliations": [
    "College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Jiangjun Road Campus, No. 29 Jiangjun Avenue, Nanjing 211106, China",
    "CRRC Nanjing Puzhen Co., Ltd., Nanjing 210031, China",
    "School of Automation, Central South University, Changsha 410083, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Jun Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Huiying Zhu"
    },
    {
      "affs": [
        2
      ],
      "name": "Zhuoyang Liu"
    },
    {
      "affs": [
        2
      ],
      "name": "Ziming Liu"
    },
    {
      "affs": [
        2
      ],
      "name": "Liqing Liao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b26k45vmk3r4s.bib",
  "cited_by": 0,
  "doi": "10.3390/batteries12100425",
  "fulltext_url": null,
  "id": "b26k45vmk3r4s",
  "issns": [
    "2313-0105"
  ],
  "landing_url": "https://www.mdpi.com/2313-0105/12/10/425",
  "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",
      "2203|Advanced Battery Technologies Research"
    ]
  ],
  "openalex": "W7221793292",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/b26k45vmk3r4s.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:47Z",
      "native_id": "10.3390/batteries12100425",
      "source": "crossref",
      "url": "https://www.mdpi.com/2313-0105/12/10/425"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2313-0105",
      "subfield": "electrochemistry-energy"
    }
  ],
  "title": "A Noise-Aware Calibrated Multi-Scale Voltage-Residual Method for Internal Short-Circuit Detection in Lithium-Ion Batteries",
  "title_text": "A Noise-Aware Calibrated Multi-Scale Voltage-Residual Method for Internal Short-Circuit Detection in Lithium-Ion Batteries",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:47Z",
  "url": "https://research.draconitic.com/paper/b26k45vmk3r4s",
  "venue": "Batteries",
  "versions": []
}