{
  "abstract": "Accurate state-of-health (SOH) estimation is essential for the safe, reliable, and cost-effective operation of lithium-ion battery systems. However, practical SOH modeling remains challenging because battery degradation data are often limited, degradation trajectories vary substantially across temperatures, and high-capacity deep sequence models typically require large, labeled datasets and considerable computational resources. To address these limitations, this study proposes a data-efficient GAN-Informer framework for lithium-ion battery SOH estimation. In the proposed method, a generative adversarial network is employed to augment the feature-space degradation distribution and improve the effective coverage of limited training samples, while an optimized Informer network with sparse multi-head attention learns long-range dependencies between aging-sensitive features and SOH trajectories. Laboratory cycling experiments were conducted under three representative temperature conditions: 20 °C, −20 °C, and 40 °C. The results show that the proposed framework achieves accurate capacity-tracking performance under both within-temperature and cross-temperature prediction settings. Compared with the Box–Cox-LSTM baseline, GAN-Informer consistently reduces MAE and RMSE across all tested datasets; for example, the RMSE decreases from 0.2235 to 0.0847 on CX1 with 50% training data and from 0.2385 to 0.1187 in the CX1-to-CX3 transfer scenario. These findings demonstrate that adversarial data augmentation combined with sparse-attention sequence modeling provides a promising route for robust, small-sample, and cross-temperature battery health estimation.",
  "abstract_shown": true,
  "affiliations": [
    "College of Operational Support, Rocket Force University of Engineering, Xi’an 710025, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Haoyu Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaojing Yuan"
    },
    {
      "affs": [
        0
      ],
      "name": "Weipeng Luo"
    },
    {
      "affs": [
        0
      ],
      "name": "Min Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Ze Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Huidong Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Xinyi Yin"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiantao Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/24vrgv669vvb4.bib",
  "cited_by": 0,
  "doi": "10.3390/batteries12100405",
  "fulltext_url": null,
  "id": "24vrgv669vvb4",
  "issns": [
    "2313-0105"
  ],
  "landing_url": "https://www.mdpi.com/2313-0105/12/10/405",
  "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": "W7221170639",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/24vrgv669vvb4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:47Z",
      "native_id": "10.3390/batteries12100405",
      "source": "crossref",
      "url": "https://www.mdpi.com/2313-0105/12/10/405"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2313-0105",
      "subfield": "electrochemistry-energy"
    }
  ],
  "title": "Generative Data Augmentation and Sparse-Attention Informer for Cross-Temperature SOH Estimation of Lithium-Ion Batteries",
  "title_text": "Generative Data Augmentation and Sparse-Attention Informer for Cross-Temperature SOH Estimation of Lithium-Ion Batteries",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:47Z",
  "url": "https://research.draconitic.com/paper/24vrgv669vvb4",
  "venue": "Batteries",
  "versions": []
}