{
  "abstract": "Extreme fast charging of lithium-ion batteries is critically limited by internal lithium plating at the negative electrode-separator interface, driving rapid degradation and thermal hazards. Real-time mitigation is fundamentally impeded because the interfacial plating overpotential (ηplating) is inaccessible to non-invasive sensors, leaving conventional protocols blind to metallic lithium deposition. While high-fidelity Doyle–Fuller–Newman (DFN) models resolve these dynamics, their computational cost (∼ 8 s) precludes embedded closed-loop control, whereas empirical equivalent circuit models lack the spatial resolution to detect plating. To resolve this trade-off, we develop a physics-informed 1D Fourier Neural Operator (FNO-1D) surrogate incorporating an explicit cumulative charge integral Q(t) = ∫ I dt to preserve mass conservation. Trained on 7,771 continuum DFN trajectories, the surrogate achieves a 4, 349× speedup (1.84 ms inference) with high fidelity (V RMSE = 5.40 mV, η plating , RMSE = 2.24 mV). Coupled with an Ensemble Smoother with Multiple Data Assimilation (ESMDA), the framework filters sensor noise (±10 mV to 5.94 mV) and reconstructs unobservable plating dynamics (RMSE = 4.12 mV, 95% CI = ±3.22 mV), establishing continuous closed-loop observability. A Barrier-Riding Nonlinear Model Predictive Control (NMPC) policy then maximizes charging current along the degradation boundary. Across a 10% - 80% SOC window, it cuts charging time by 21.7% (33.7 min vs. 43.0 min CC-CV)while strictly preventing lithium plating (η plating ≥ +14.81 mV > +5.0 mV), limiting peak temperature to 38.5◦C, and decreasing SEI growth by 21.5%. Finally, transferability is experimentally validated across two phys ical cycler benchmarks: cross-chemistry transfer on Oxford pouch cells, and chemistry-matched validation on commercial RWTH Aachen Sanyo 18650 NMCcells, achieving a terminal voltage tracking RMSE of 33.02 mV (86.0% error reduction over zero-shot transfer) providing an experimentally grounded paradigm for degradation-free fast charging.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Chemistry",
    "Islamia University Bahawalpur"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Hussnain Tariq"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/95js3qjgqz7ch.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010874/v1",
  "fulltext_url": null,
  "id": "95js3qjgqz7ch",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010874/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2203|Advanced Battery Technologies Research"
    ]
  ],
  "openalex": "W7223178791",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010874/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/95js3qjgqz7ch.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv.15010874",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010874/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "A Physics-Informed Fourier Neural Operator Framework for Zero-Plating Fast Charging of Lithium-Ion Batteries via Real-Time Electrochemical State Estimation and Barrier-Riding Optimal Control",
  "title_text": "A Physics-Informed Fourier Neural Operator Framework for Zero-Plating Fast Charging of Lithium-Ion Batteries via Real-Time Electrochemical State Estimation and Barrier-Riding Optimal Control",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/95js3qjgqz7ch",
  "venue": "ChemRxiv",
  "versions": []
}