{
  "abstract": "Accurate state-of-charge (SOC) estimation is essential for lithium-ion battery management, but low temperatures, dynamic loads, and measurement disturbances challenge both model-based and data-driven estimators. This study proposes a physics-gated multi-scale temporal convolutional network (PG-MS-TCN) for SOC estimation from −20 to 40 °C. Within an established second-order RC modeling framework, an adaptive forgetting-factor recursive least-squares scheme adjusts parameter updates according to voltage innovation and current increments. The resulting circuit-derived features are smoothed using a dual-threshold residual-adaptive exponentially weighted moving average filter, and a temperature-aware two-dimensional open-circuit-voltage-to-SOC mapping provides the physical baseline. To learn the nonlinear residual between this baseline and the reference SOC, PG-MS-TCN employs a cross-domain gating mechanism in which dynamic physical states modulate external measurement features. This mechanism is designed to attenuate transient voltage anomalies during severe polarization, while multi-scale causal convolutions capture temporal signatures of multi-frequency electrochemical dynamics. Experiments on the LG HG2 battery dataset yield an overall root mean square error (RMSE) of 1.18% under interference-free conditions and 1.38% under the specified current disturbances. The average processing time is 0.021 ms per sample. These results demonstrate SOC estimation accuracy and computational efficiency under the evaluated conditions.",
  "abstract_shown": true,
  "affiliations": [
    "School of Automation, Guangxi University of Science and Technology, 2 Wenchang Road, Chengzhong District, Liuzhou 545006, China",
    "School of Electronic Engineering, Guangxi University of Science and Technology, 2 Wenchang Road, Chengzhong District, Liuzhou 545006, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yafan Duan"
    },
    {
      "affs": [
        0
      ],
      "name": "Shengyong Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Anqiong Tang"
    },
    {
      "affs": [
        0
      ],
      "name": "Shenglong Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Qipei Gan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cpbh5mpr0m3qr.bib",
  "cited_by": 0,
  "doi": "10.3390/batteries12110476",
  "fulltext_url": null,
  "id": "cpbh5mpr0m3qr",
  "issns": [
    "2313-0105"
  ],
  "landing_url": "https://www.mdpi.com/2313-0105/12/11/476",
  "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": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/cpbh5mpr0m3qr.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T00:21:23Z",
      "native_id": "10.3390/batteries12110476",
      "source": "crossref",
      "url": "https://www.mdpi.com/2313-0105/12/11/476"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2313-0105",
      "subfield": "electrochemistry-energy"
    }
  ],
  "title": "Hybrid Physical and Data-Driven State of Charge Estimation for Lithium-Ion Batteries Under Low-Temperature and Noisy Conditions",
  "title_text": "Hybrid Physical and Data-Driven State of Charge Estimation for Lithium-Ion Batteries Under Low-Temperature and Noisy Conditions",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T00:21:23Z",
  "url": "https://research.draconitic.com/paper/cpbh5mpr0m3qr",
  "venue": "Batteries",
  "versions": []
}