{
  "abstract": "Accurate capacity estimation of lithium-ion batteries (LIBs) in real-world electric vehicles (EVs) remains a critical challenge due to complex degradation mechanisms, sparse cloud-platform data, and the limitations of existing single-model approaches. To address these issues, this paper proposes a novel fusion estimation method that integrates a discretized Arrhenius empirical lifetime model with a first-order equivalent circuit model (ECM) identified via particle swarm optimization (PSO) and employs a dual extended Kalman filter (DEKF) to synergistically update model parameters and fuse capacity estimates. Then, the proposed method is validated using real-world charging data from 36 EVs across three vehicle brands. Experimental results demonstrate that the fusion model achieves capacity estimation errors within 2.974% maximum and delivers state-of-health (SOH) estimation with R2 values of 0.966, 0.983, and 0.981 for the three vehicle types, respectively. The method effectively overcomes data sparsity and noise issues inherent in cloud-platform data, and provides accurate, adaptive, and robust capacity tracking under diverse real-world operating conditions. This work offers a practical and scalable solution for cloud-based battery health management in next-generation EV applications.",
  "abstract_shown": true,
  "affiliations": [
    "School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China",
    "School of Automotive Engineering, Jiangxi Polytechnic University, Jiujiang 332007, China",
    "The China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China",
    "The China Electric Power Research Institute, Beijing 100192, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Dongdong Qiao",
      "orcid": "0000-0003-0866-850X"
    },
    {
      "affs": [
        1
      ],
      "name": "Zhicheng Zhu"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaowei Shen"
    },
    {
      "affs": [
        2
      ],
      "name": "Zhiwei Zhao"
    },
    {
      "affs": [
        3
      ],
      "name": "Xiangjun Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Xin Lai",
      "orcid": "0000-0003-2190-6023"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cw15p1va2pxkb.bib",
  "cited_by": 0,
  "doi": "10.3390/batteries12110455",
  "fulltext_url": null,
  "id": "cw15p1va2pxkb",
  "issns": [
    "2313-0105"
  ],
  "landing_url": "https://www.mdpi.com/2313-0105/12/11/455",
  "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": "2026-11-02",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/cw15p1va2pxkb.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:24:21Z",
      "native_id": "10.3390/batteries12110455",
      "source": "crossref",
      "url": "https://www.mdpi.com/2313-0105/12/11/455"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2313-0105",
      "subfield": "electrochemistry-energy"
    }
  ],
  "title": "Real Electric Vehicle Lithium-Ion Battery Capacity Estimation Method Based on the Fusion Model of Discrete Arrhenius and Double Extended Kalman Filter",
  "title_text": "Real Electric Vehicle Lithium-Ion Battery Capacity Estimation Method Based on the Fusion Model of Discrete Arrhenius and Double Extended Kalman Filter",
  "type": "peer-reviewed",
  "updated_at": "2026-11-04T12:21:10Z",
  "url": "https://research.draconitic.com/paper/cw15p1va2pxkb",
  "venue": "Batteries",
  "versions": []
}