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arxiv: 2606.12664 · v1 · pith:4X4R2U52new · submitted 2026-06-10 · 📡 eess.SY · cs.SY

Modeling and Estimation of Solid Electrolyte Interphase during Formation in Battery Manufacturing

Zhiwen Wan , Hamidreza Movahedi , Wenxue Liu , Jingchen Ma , Jason B. Siegel , Andrew Weng , Anna Stefanopoulou This is my paper

Pith reviewed 2026-06-27 08:16 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords solid electrolyte interphaseSEI estimationbattery formationunscented Kalman filterlithium-ion batteriesin-operando measurementcell manufacturingsemi-empirical model
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The pith

A semi-empirical model with unscented Kalman filter estimates SEI thickness growth in real time from battery voltage and expansion data during formation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper develops a control-oriented semi-empirical model that tracks the thickness of the solid electrolyte interphase created on lithium-ion battery electrodes during the cell formation step of manufacturing. The model ingests terminal voltage and cell expansion signals collected in-operando through a low-cost integrated-sensing fixture and employs an unscented Kalman filter to generate ongoing thickness estimates. Model parameters are fitted directly to formation-cycle data from physical cells. If the estimates are accurate, the approach opens the door to adjusting formation current or voltage profiles on the fly rather than relying on fixed empirical schedules.

Core claim

The paper claims that a control-oriented semi-empirical model of SEI growth, calibrated on cell formation data and combined with an unscented Kalman filter, can estimate the evolving SEI film thickness solely from in-operando terminal voltage and cell expansion measurements.

What carries the argument

Semi-empirical SEI growth model paired with unscented Kalman filter that fuses voltage and expansion measurements into real-time thickness estimates.

If this is right

  • Real-time SEI thickness estimates become available during manufacturing without specialized direct-measurement hardware.
  • Closed-loop adjustment of formation protocols based on estimated SEI growth rate becomes feasible.
  • Formation times may be shortened while maintaining or improving SEI quality that affects subsequent battery lifetime.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The sensing fixture and filter could be retrofitted onto existing formation equipment with modest capital cost.
  • The same measurement pair might later support online monitoring of SEI evolution in operating packs after they leave the factory.
  • Extending the model to different electrode chemistries or formation temperatures would test how broadly the calibrated parameters apply.

Load-bearing premise

The semi-empirical model structure and its calibration on formation data are sufficient for the unscented Kalman filter to produce accurate real-time estimates of SEI thickness solely from terminal voltage and cell expansion, without requiring direct SEI measurements or additional unmodeled dynamics.

What would settle it

Post-formation comparison of the filter's estimated SEI thickness against independent direct measurements such as electrochemical impedance spectroscopy or cross-section microscopy on disassembled cells.

Figures

Figures reproduced from arXiv: 2606.12664 by Andrew Weng, Anna Stefanopoulou, Hamidreza Movahedi, Jason B. Siegel, Jingchen Ma, Wenxue Liu, Zhiwen Wan.

Figure 1
Figure 1. Figure 1: Overview of the proposed framework for control-oriented modeling and estimation of SEI growth during formation. (a) Battery formation in manufacturing: empirical protocols are time-consuming, yet decisive for lifetime performance. (b) Formation experiment and modeling result: measured current, voltage, and in-situ cell expansion, with modeled voltage and expansion. (c) Control-oriented formation model: cou… view at source ↗
Figure 3
Figure 3. Figure 3: Simulation of SEI growth under higher C-rate. (a) Cell and side-reaction currents, highlighting the increase in ISEI and the transition between kinetic- and diffusion-limited regimes. (b) Voltage decomposition, showing stronger polarization at high current, which drives the anode potential into the SEI reaction window earlier. (c) Expansion contributions, comparing SEI thickening, electrode swelling, and a… view at source ↗
Figure 4
Figure 4. Figure 4: State estimation with biased initialization. The UKF was initialized with a 20% error in both anode and cathode stoichiometries to em￾ulate uncertainty from unknown prelithiation. Estimated states (solid lines) are shown together with their ±2σ confidence envelopes and simulated trajectories (dashed lines) for reference. Results indicate rapid correction of the anode stoichiometry within the first minute, … view at source ↗
Figure 5
Figure 5. Figure 5: UKF predicted outputs versus measurements. Comparison of UKF predictions with experimental data for terminal voltage (top) and cell expansion (bottom). Once the internal states converge, the UKF reproduces both outputs with high fidelity, yielding root-mean-square errors of 55.3 mV for voltage and 2.65 µm for expansion. in expansion. Implemented within a UKF, the framework enabled reliable real-time state … view at source ↗
read the original abstract

The solid electrolyte interphase (SEI) - a critical passivation layer that governs the longevity, safety, and efficiency of lithium-ion batteries - is created during the last step in cell manufacturing called cell formation. Conventional cell formation protocols are largely empirical, resulting in long processing times and limited control over the SEI growth rate that influences SEI quality and lifetime performance. This paper develops a control-oriented, semi-empirical model to estimate SEI thickness growth from terminal voltage and cell expansion measurements acquired in-operando during manufacturing using low-cost micrometer-precision integrated-sensing fixture. Model parameters are calibrated against cell formation data, and an unscented Kalman filter is employed to estimate the SEI film growth. The results lay the foundation for future closed-loop control of SEI growth, enabling high-quality and more efficient formation processes.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript develops a control-oriented, semi-empirical model to estimate solid electrolyte interphase (SEI) thickness growth during lithium-ion battery cell formation. It uses in-operando terminal voltage and cell expansion measurements from a low-cost micrometer-precision integrated-sensing fixture. Model parameters are calibrated on cell formation data, and an unscented Kalman filter (UKF) is employed to produce real-time SEI estimates. The work positions this as a foundation for future closed-loop control of SEI growth to improve manufacturing efficiency and SEI quality.

Significance. If the central claims hold after proper validation, the approach could enable non-destructive, real-time monitoring of SEI formation using readily available signals, supporting more efficient and controlled battery manufacturing protocols. The combination of semi-empirical modeling with UKF and low-cost sensing hardware represents a practical step toward control-oriented battery process engineering.

major comments (1)
  1. [Abstract and model-validation sections] Abstract and model-validation sections: The central claim that the UKF yields accurate real-time SEI thickness estimates from voltage and expansion alone requires that the semi-empirical model isolates SEI growth from other contributors (electrolyte consumption, gas evolution, mechanical effects). No independent ground-truth SEI thickness measurements (post-formation disassembly, XPS, EIS, or similar) are described for either parameter calibration or hold-out validation of the UKF estimates. Without such data, it is impossible to determine whether reported estimation performance reflects true SEI prediction or simply reproduces the calibration signals.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback. We respond to the major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract and model-validation sections] Abstract and model-validation sections: The central claim that the UKF yields accurate real-time SEI thickness estimates from voltage and expansion alone requires that the semi-empirical model isolates SEI growth from other contributors (electrolyte consumption, gas evolution, mechanical effects). No independent ground-truth SEI thickness measurements (post-formation disassembly, XPS, EIS, or similar) are described for either parameter calibration or hold-out validation of the UKF estimates. Without such data, it is impossible to determine whether reported estimation performance reflects true SEI prediction or simply reproduces the calibration signals.

    Authors: We agree that the lack of independent ground-truth SEI thickness measurements (e.g., via XPS, EIS, or post-formation disassembly) is a genuine limitation. Our semi-empirical model parameters are calibrated directly to the observed in-operando voltage and expansion signals, and the UKF produces estimates consistent with that calibrated model. We do not have separate validation data to confirm that SEI growth has been isolated from other effects or that the estimates reflect true SEI thickness rather than the calibration signals. We will revise the abstract and model-validation sections to explicitly acknowledge this limitation, clarify the nature of the reported performance, and position the work as a proof-of-concept for the sensing and estimation framework rather than a fully validated SEI measurement method. Future work will incorporate direct SEI characterization. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The abstract and description outline a standard semi-empirical modeling workflow: parameters are calibrated to formation data, then an unscented Kalman filter is applied to estimate SEI growth from voltage and expansion signals. No equations, self-citations, or derivation steps are shown that reduce the claimed estimates to the inputs by construction. The model structure is presented as control-oriented and semi-empirical without evidence of self-definitional loops, fitted inputs renamed as predictions, or load-bearing self-citations. This is a normal, non-circular empirical modeling paper whose central claim rests on external data calibration and filter application rather than tautological reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; full manuscript would be required to enumerate calibration constants, modeling assumptions, or new physical constructs.

pith-pipeline@v0.9.1-grok · 5687 in / 1277 out tokens · 22351 ms · 2026-06-27T08:16:55.398076+00:00 · methodology

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Reference graph

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