{
  "abstract": "Vehicle-level battery labels paired with many charging records create a weakly supervised rare-event problem. We evaluated a two-level multiple-instance framework on 191 vehicles (184 normal and seven source-coded abnormal) from the public EVBattery archive, using 38,200 charging snippets. Nine normal-reference anomaly detectors were combined with fixed or learned vehicle-level pooling under six repetitions of five-fold vehicle-disjoint internal cross-validation. All preprocessing and detector fitting used training vehicles only; score mapping used aggregation-training vehicles; decision thresholds used a separate 13-14-vehicle validation partition containing only one or two abnormal vehicles. PCA reconstruction with 95th-percentile pooling produced the highest numerical repeat-mean average precision (0.342) among 46 evaluated pipelines. A leave-one-repeat-out selection-sensitivity diagnostic selected this pipeline in five of six analyses and yielded mean held-repeat average precision 0.318 ± 0.078; because all repeats reuse the same 191 vehicles, this is not an independent test estimate. After averaging six out-of-fold probabilities per vehicle, average precision was 0.371 and AUROC 0.796. Threshold-based metrics are exploratory: the 30 validation-derived thresholds ranged from 0.267 to 0.999, and 17 equalled 0.500. No pairwise comparison survived Holm correction, uncertainty was wide, and calibration was poor. The results identify PCA plus upper-tail pooling only as the highest numerical internal benchmark in this cohort, not as an unbiased estimate of a selected final model, confirmed physical diagnosis, or deployment-ready probability estimation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2092, South Africa"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mavis Fortunate Makama"
    },
    {
      "affs": [
        0
      ],
      "name": "Bonginkosi A. Thango",
      "orcid": "0000-0003-3635-0988"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6e2dep33y2k0m.bib",
  "cited_by": 0,
  "doi": "10.3390/batteries12100431",
  "fulltext_url": null,
  "id": "6e2dep33y2k0m",
  "issns": [
    "2313-0105"
  ],
  "landing_url": "https://www.mdpi.com/2313-0105/12/10/431",
  "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",
      "1702|Anomaly Detection Techniques and Applications"
    ]
  ],
  "openalex": "W7222099786",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/6e2dep33y2k0m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:47Z",
      "native_id": "10.3390/batteries12100431",
      "source": "crossref",
      "url": "https://www.mdpi.com/2313-0105/12/10/431"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2313-0105",
      "subfield": "electrochemistry-energy"
    }
  ],
  "title": "Hierarchical Multiple-Instance Learning for Vehicle-Level Battery Anomaly Detection from Charging Data Streams",
  "title_text": "Hierarchical Multiple-Instance Learning for Vehicle-Level Battery Anomaly Detection from Charging Data Streams",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:47Z",
  "url": "https://research.draconitic.com/paper/6e2dep33y2k0m",
  "venue": "Batteries",
  "versions": []
}