{
  "abstract": "Accurate health assessment is essential for the reliable and cost-effective operation of lithium-ion battery energy storage systems. This study presents a physics-informed explainable machine-learning framework for hybrid State-of-Health (SoH) estimation and Remaining Useful Life (RUL) prediction under heterogeneous simulated operating and degradation conditions. A multi-cell degradation model represents capacity fade, resistance growth, thermal and operational stress, and nonlinear aging acceleration. Hybrid SoH combines capacity- and resistance-related health information, while end of life (EOL) and RUL are defined exclusively by an 80% capacity-retention criterion. Generalization is evaluated using cell-wise and leave-one-cell-out validation, with right-censored cells excluded from exact RUL error calculation. In the primary evaluation, Gradient Boosting achieves a SoH RMSE of 0.340 percentage points and R2 = 0.9979, while Random Forest provides the lower RUL RMSE of 239.5 cycles with R2 = 0.9278. Whole-cell bootstrap analysis, simulated distribution-shift testing, and expanded feature ablation further assess uncertainty and robustness. Explainability analysis indicates strong model associations with cumulative electrothermal stress, charging-stage behavior, and pulse resistance. The results demonstrate strong simulation-based predictive capability while revealing substantial inter-cell variability in RUL generalization and uncertainty. Independent experimental validation remains necessary to establish transferability to physical battery systems.",
  "abstract_shown": true,
  "affiliations": [
    "CoE, “National Center of Mechatronics and Clean Technologies”, 1000 Sofia, Bulgaria",
    "Department of Intelligent Technology in Industry, Faculty of Computer Systems and Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria",
    "Department of Computer Systems, Faculty of Computer Systems and Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Plamen Stanchev",
      "orcid": "0000-0001-6564-6206"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Nikolay Hinov",
      "orcid": "0000-0002-0308-4858"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fncr52hg754e5.bib",
  "cited_by": 0,
  "doi": "10.3390/batteries12100401",
  "fulltext_url": null,
  "id": "fncr52hg754e5",
  "issns": [
    "2313-0105"
  ],
  "landing_url": "https://www.mdpi.com/2313-0105/12/10/401",
  "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": "W7220665073",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/fncr52hg754e5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:47Z",
      "native_id": "10.3390/batteries12100401",
      "source": "crossref",
      "url": "https://www.mdpi.com/2313-0105/12/10/401"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2313-0105",
      "subfield": "electrochemistry-energy"
    }
  ],
  "title": "Physics-Informed Explainable Machine Learning for Hybrid State of Health and Remaining Useful Life Estimation of Lithium-Ion Batteries",
  "title_text": "Physics-Informed Explainable Machine Learning for Hybrid State of Health and Remaining Useful Life Estimation of Lithium-Ion Batteries",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:47Z",
  "url": "https://research.draconitic.com/paper/fncr52hg754e5",
  "venue": "Batteries",
  "versions": []
}