{
  "abstract": "Data-driven battery state estimation has been widely studied in recent years to enable safe, reliable, and lifetime-aware battery management in e-mobility applications, where accurate estimation is essential under highly dynamic operating conditions. With several recent reviews focusing on model architectures and methodological developments, a critical assessment of practical feasibility, real-world deployability, and application-specific challenges in e-mobility is needed. This review therefore provides a challenge-oriented assessment of recent data-driven methods for battery state estimation, with particular emphasis on practical deployment in e-mobility systems. Beyond summarising existing approaches, it assesses how recent studies address key deployment barriers. These barriers include data dependency, limited ground-truth health labels, and fragmented datasets. Further challenges concern feature complexity and interpretability, robustness under domain shifts, embedded-BMS constraints, and cell-to-pack scalability.Recentresearch increasingly focuses on battery health analytics, reflecting the importance of ageing-aware battery management in e-mobility. Based on the gaps identified, future research directions are outlined to support the development of safe, reliable, interpretable, and deployment-ready battery state estimation for next-generation e-mobility systems.",
  "abstract_shown": true,
  "affiliations": [
    "Centre for Industrial Electronics, Institute of Mechanical and Electrical Engineering, University of Southern Denmark, 6400 Sønderborg, Denmark",
    "Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, 67100 L’Aquila, Italy",
    "MEAtec Mühendislik Ltd., 06800 Ankara, Turkey"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Aaruththiran Manoharan",
      "orcid": "0000-0002-8112-5491"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Fazal Ur Rehman",
      "orcid": "0009-0006-2625-8546"
    },
    {
      "affs": [
        2
      ],
      "name": "Aydin Emre Utku"
    },
    {
      "affs": [
        2
      ],
      "name": "Mehmet Emre Aslakci"
    },
    {
      "affs": [
        0
      ],
      "name": "Kun Qian",
      "orcid": "0000-0002-6765-9446"
    },
    {
      "affs": [
        0
      ],
      "name": "Kasper Mayntz Paasch"
    },
    {
      "affs": [
        0
      ],
      "name": "Thomas Ebel",
      "orcid": "0000-0001-8473-4471"
    },
    {
      "affs": [
        0
      ],
      "name": "Henrik Andersen",
      "orcid": "0000-0002-7400-4114"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/09kgybscjp6m6.bib",
  "cited_by": 0,
  "doi": "10.3390/batteries12110475",
  "fulltext_url": null,
  "id": "09kgybscjp6m6",
  "issns": [
    "2313-0105"
  ],
  "landing_url": "https://www.mdpi.com/2313-0105/12/11/475",
  "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/09kgybscjp6m6.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T00:21:23Z",
      "native_id": "10.3390/batteries12110475",
      "source": "crossref",
      "url": "https://www.mdpi.com/2313-0105/12/11/475"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2313-0105",
      "subfield": "electrochemistry-energy"
    }
  ],
  "title": "Data-Driven Battery State Estimation for E-Mobility Applications: Challenges, Recent Advances, and Future Directions",
  "title_text": "Data-Driven Battery State Estimation for E-Mobility Applications: Challenges, Recent Advances, and Future Directions",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T00:21:23Z",
  "url": "https://research.draconitic.com/paper/09kgybscjp6m6",
  "venue": "Batteries",
  "versions": []
}