{
  "abstract": "Accurate state-of-health (SOH) estimation across nonlinear battery aging is challenged by condition-dependent feature relevance. This study combines aging data up to a defined 80% SOH operational end-of-life threshold with an adaptive SHapley Additive exPlanations (SHAP)-guided dual-attention long short-term memory (LSTM) network. Current, voltage, state of charge (SOC), and a pulse-derived short-time resistance indicator from hybrid pulse power characterization (HPPC) are used as inputs. Training-set SHAP importance supplies a weak global feature prior, whose influence is adjusted for each input sequence by an adaptive gate. Residual feature attention recalibrates the inputs, while temporal attention aggregates representations from a shared LSTM encoder. Stage windows and labels are permuted synchronously during training to avoid fixed-position shortcuts. Under held-out SOC conditions, the complete model achieves MAE = 0.3418 percentage points (pp), RMSE = 0.5652 pp, MAPE = 0.3825%, and R2 = 0.9945, reducing MAE and RMSE by 14.1% and 12.4% relative to the Plain Shared LSTM. The results support the coordinated architecture within the tested SOC-holdout protocol; cross-cell performance is assessed separately under a different UDDS setting.",
  "abstract_shown": true,
  "affiliations": [
    "School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China",
    "School of Mechanical Engineering, Ningxia Institute of Science and Technology, Shizuishan 753000, China",
    "School of Materials Science and Engineering, Northeastern University, Shenyang 110819, China",
    "Shandong Key Laboratory of Intelligent Manufacturing Technology for Advanced Power Equipment, Weifang University, Weifang 261061, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Pengxiang Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Zixi Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Nan Zhou"
    },
    {
      "affs": [
        3
      ],
      "name": "Shanshan Guo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/40c689fhc8swr.bib",
  "cited_by": 0,
  "doi": "10.3390/batteries12110458",
  "fulltext_url": null,
  "id": "40c689fhc8swr",
  "issns": [
    "2313-0105"
  ],
  "landing_url": "https://www.mdpi.com/2313-0105/12/11/458",
  "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": "2026-11-03",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/40c689fhc8swr.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:24:21Z",
      "native_id": "10.3390/batteries12110458",
      "source": "crossref",
      "url": "https://www.mdpi.com/2313-0105/12/11/458"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2313-0105",
      "subfield": "electrochemistry-energy"
    }
  ],
  "title": "Multi-Stage State-of-Health Estimation for Lithium-Ion Batteries Based on an Adaptive SHAP-Guided Dual-Attention LSTM",
  "title_text": "Multi-Stage State-of-Health Estimation for Lithium-Ion Batteries Based on an Adaptive SHAP-Guided Dual-Attention LSTM",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T00:20:54Z",
  "url": "https://research.draconitic.com/paper/40c689fhc8swr",
  "venue": "Batteries",
  "versions": []
}