{
  "abstract": "Existing fault detection methods for lead-acid batteries in commercial vehicles are subject to several critical limitations, such as low detection accuracy and a shortage of labeled fault data. To address these issues, this paper presents an unsupervised deep learning framework for anomaly detection. In the framework, a hybrid LSTM-Autoencoder architecture is used to integrate the temporal feature extraction capability of long short-term memory networks with the reconstruction mechanism of Autoencoders, thus markedly improving the temporal modeling of battery data. Since the detection performance is highly dependent on the selection of reconstruction error metrics, multiple such metrics are employed in the model, and are systematically evaluated and compared to determine the most suitable metric for battery anomaly detection. The model is trained separately for static and driving conditions based on corresponding subsets of the battery datasets. With a fixed model structure and experimental conditions, model performance is evaluated using various reconstruction error metrics, and Bayesian optimization is used to identify the optimal detection threshold for each metric. Experimental results show that detection performance changes with different reconstruction error metrics and cosine similarity yields the best performance. This study offers a practical solution for online battery diagnosis and helps lower vehicle maintenance costs.",
  "abstract_shown": true,
  "affiliations": [
    "School of Intelligent Connected Vehicle, Hubei University of Automotive Technology, Shiyan, China",
    "Shiyan Key Laboratory of Air-Ground Crowd Cooperation Technology and Application (Hubei University of Automotive Technology), Hubei University of Automotive Technology, Shiyan, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Le Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Hongsheng Xu"
    },
    {
      "name": "Hai Tang"
    },
    {
      "name": "Jianing Cui"
    },
    {
      "name": "Li Feng"
    },
    {
      "name": "Ke Deng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8nrt78qggek7h.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357237",
  "fulltext_url": null,
  "id": "8nrt78qggek7h",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0357237",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2203|Advanced Battery Technologies Research"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognition/Memory/Short term memory"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Learning"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Learning and memory/Learning"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Learning and memory/Memory/Short term memory"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Engineering and technology/Electrical engineering/Electrical faults"
    ],
    [
      "plos",
      "/Engineering and technology/Measurement/Distance measurement"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Similarity measures/Cosine similarity"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Damage mechanics"
    ],
    [
      "plos",
      "/Research and analysis methods/Database and informatics methods/Bioinformatics/Sequence analysis/Sequence alignment"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Learning"
    ]
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  "openalex": "W7213231916",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357237&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/8nrt78qggek7h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.1371/journal.pone.0357237",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0357237"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Research on a lead-acid battery fault detection method based on LSTM-AE-Cosine",
  "title_text": "Research on a lead-acid battery fault detection method based on LSTM-AE-Cosine",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/8nrt78qggek7h",
  "venue": "PLOS One",
  "versions": []
}