Pith. sign in

REVIEW 4 major objections 4 minor 1 cited by

Physics-based battery model parametrisation from impedance data

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that electrochemical impedance spectra can be used to parametrise physics-based battery models directly, because exact linearisation via automatic differentiation makes model impedance fast to compute, removing the need…

desk verdict A solid AD-based impedance tool and simulated-data parametrisation study; the measured-data electrode-assignment claim is overstated. read the letter →

arxiv 2412.10896 v4 pith:RVTE65EW submitted 2024-12-14 eess.SY cond-mat.mtrl-scics.SY

Noël Hallemans , Nicola E. Courtier , Colin P. Please , Brady Planden , Rishit Dhoot , Robert Timms , S. Jon chapman , David Howey
show 1 more author
Stephen R. Duncan
This is my paper · ORCID
classification eess.SYcond-mat.mtrl-scics.SY
keywords electrochemicalimpedancespectroscopysingleparticlemodelwithelectrolyteSPMeparametrisationautomaticdifferentiationlithium-ionbatteryparameteridentifiabilityPyBaMM
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper establishes that battery-model parametrisation from electrochemical impedance spectroscopy (EIS) need not start from a hand-derived impedance formula. By discretising the model and linearising it exactly at an operating point with automatic differentiation, the impedance of any model in the open-source PyBaMM framework can be computed quickly and accurately. The authors show this on the single-particle model, the single-particle model with electrolyte (SPMe), and the Doyle-Fuller-Newman model, then identify the SPMe as a parsimonious choice that captures the measured impedance features. They reduce the SPMe to 18 grouped parameters and estimate them from both simulated and measured EIS data, arguing that simultaneous fitting across many states of charge is essential for attributing parameters to the correct electrode. If this is right, routine EIS measurements become a practical route to calibrating physics-based battery models without deriving closed-form impedance expressions.

What carries the argument

The load-bearing object is the numerical frequency-domain impedance formula: after discretising the model into a system of differential-algebraic equations, the impedance at frequency $\omega$ is $Z_m(\omega,\theta) = [(j\omega M_{\theta,m}-J_{\theta,m})^{-1}B]_{N_x+1}$, where $M_{\theta,m}$ is the mass matrix evaluated at the operating point, $J_{\theta,m}$ is the Jacobian of the model's right-hand side, and the selected entry is the voltage. Automatic differentiation provides the Jacobian exactly and avoids the cost and error of finite-difference linearisation. The second supporting mechanism is parameter grouping: by non-dimensionalising the SPMe and keeping only the dimensions of time, current, and voltage, the paper reduces the model to 22 grouped parameters, and with known cell capacity and relative electrode thicknesses, 18 of these need to be estimated from impedance data.

What would settle it

Measure the second-harmonic distortion ratio across the entire frequency range used for fitting, not just at 400 µHz as in the paper; if the signal-to-nonlinear-distortion ratio drops below roughly 80 at any fitting frequency, the spectra are not linear and the reported parameter values and electrode assignment would change.

Watch

Extended reading notes

Core claim

The central claim is that the impedance of any physics-based battery model implemented in PyBaMM can be computed numerically by exact linearisation followed by a frequency-domain solve. After spatial discretisation the model becomes a DAE system, $M_{\theta,m}\frac{d\tilde{x}}{dt}=J_{\theta,m}\tilde{x}+Bi(t)$, and its impedance is the voltage entry of $(j\omega M_{\theta,m}-J_{\theta,m})^{-1}B$, where $J_{\theta,m}$ is the Jacobian of the right-hand side at the operating point, computed exactly by automatic differentiation. The paper validates this against brute-force time-domain simulation, finding relative errors below 0.4% while being orders of magnitude faster, which makes impedance-based fitting practical inside an optimisation loop. Applying the method to the SPMe, the paper groups parameters into 18 estimable quantities, shows how each affects distinct impedance features, and demonstrates that fitting simultaneously over a wide range of SOCs recovers the parameters that directly influence the response and assigns them to the correct electrode. The method is released as open-source software that works with any PyBaMM model at any operating point.

Load-bearing premise

The recorded impedance spectra are small-signal, stationary responses, and the half-cell open-circuit-potential curves used in the fits are the cell's true equilibrium voltages; the paper checks these only thinly and blames their failure for the poor low-frequency fits.

Editorial extensions

If this is right

  • Any model implemented in PyBaMM, including user-defined ones, can be given an impedance response at any operating point without deriving an analytic impedance expression, opening EIS-based calibration to a broad class of physics-based models.
  • The comparison of SPM, SPMe, and DFN shows that electrolyte dynamics add a diffusion 'bump' seen in measured data, and that the SPMe is the parsimonious model that captures the relevant impedance features while the DFN is similar but more expensive.
  • The 18 grouped SPMe parameters are identifiable when fitted jointly to impedance data across several SOCs, and the parameters that directly affect the response can be assigned to the correct electrode.
  • Fitting from impedance data is more informative than time-domain voltage data for short-timescale parameters, while long-timescale diffusion parameters are recovered with similar accuracy from both.
  • For the LG M50LT cell, the SPMe fitted to measured EIS data predicts a drive-cycle voltage with a root-mean-square error of 5.3 mV and a maximum error near 15 mV, despite poor low-frequency impedance fits.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the negative-electrode diffusion timescale is nearly invisible at 50% SOC where the graphite OCP is flat, an implicit design rule follows: choose SOC operating points where each electrode's open-circuit-potential slope is large to make that electrode's diffusion parameters identifiable; the paper's multi-SOC protocol implements this rule without stating it.
  • The same exact-linearisation machinery could be extended to predict higher harmonics by retaining second-order Taylor terms, turning the linear impedance calculator into a nonlinear-EIS diagnostic that would strengthen the paper's single-frequency linearity check.
  • If the poor low-frequency fits are indeed caused by OCP hysteresis, as the paper suggests, then fitting the model to impedance spectra acquired with the SOC approached from both charge and discharge directions would separate hysteresis from genuine model deficiency; the reported measurements approach every operating point from discharge only.
  • Because impedance computation is cheap for any PyBaMM model, the method turns model selection into an automated loop: candidate extensions such as state-dependent diffusivity, particle-size distributions, or multi-particle models can be compared purely by their ability to fit measured impedance, without hand-deriving any impedance.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This paper presents PyBaMM-EIS, a numerical frequency-domain impedance computation method for battery models that uses automatic differentiation to linearize the discretized DAE system about an operating point. The method is validated against brute-force time-domain simulation for the SPM and SPMe with relative errors below 0.4%, and it is shown to be much faster than brute-force simulation. The paper then uses the method to compare SPM, SPMe, and DFN impedance, selects the SPMe as a parsimonious model, derives a grouped-parameter SPMe, and performs an impedance sensitivity analysis. Parameter estimation is carried out with PyBOP on simulated impedance data (18 grouped parameters) and on measured LG M50LT impedance data. The simulated-data fits recover most large-feature parameters, while the measured-data fit has poor low-frequency agreement, several parameters at optimisation bounds, and excludes the 10% and 90% SOC spectra. A drive-cycle validation yields a 5.3 mV RMSE.

Significance. If the numerical impedance method is accepted, it is a valuable open-source contribution: it removes the need for analytical impedance derivations for arbitrary PyBaMM models and may substantially speed up EIS-based parametrisation. The grouped-parameter SPMe and the SOC-dependent sensitivity analysis are also useful. However, the central claim that measured SPMe parameters can be accurately determined and assigned to the correct electrode is only partially supported by the evidence, as detailed in the major comments. The paper is therefore significant but needs substantial revision to substantiate the parametrisation claims.

major comments (4)
  1. [Sec. 6.1, Table 3] The simulated-data identifiability results show that several parameters are estimated at their optimisation bounds (τe+ = 200 s, ζ+ = 0.5, τe_sep = 200 s, Qe = 500 As) and several others have large relative standard deviations over the ten runs (τe− 20.9%, ζ− 41.9%, τct+ 26.5%, C+ 13.9%). This directly qualifies the Abstract's claim that parameters 'can be accurately determined'; the claim holds only for the parameters associated with large impedance features. The authors should either soften the claim or provide a formal identifiability analysis (e.g., profile likelihood or Fisher information) to delineate which parameters are identifiable.
  2. [Sec. 7.2, Table 5] In the measured-data fit, eight of the eighteen fitted parameters are at their optimisation bounds (τe+, τe−, τe_sep, ζ+, ζ−, Qe, t+, C+). This indicates that the optimiser's bounds, not the impedance data, are constraining these values. The paper notes these are related to small features, but this undermines the broader claim that parameters are accurately determined from measured data. The authors should assess whether wider bounds or different parameter scaling change the results, or explicitly state that these parameters are not identifiable from the available data.
  3. [Sec. 7.2, Eq. (15)] The paper attributes the poor low-frequency fits to OCP hysteresis, but the model treats the OCP curves as fixed, single-valued equilibrium inputs. Since Eq. (15) shows the diffusion-tail impedance is proportional to U′±(cm,±), any error in the OCP slope directly propagates into the estimated diffusion time-scales τd± and the stoichiometry bounds c0%± and c100%± — exactly the parameters claimed to be assigned to the correct electrode. Without quantifying the hysteresis-induced OCP uncertainty or validating the electrode-level parameters against independent measurements (e.g., half-cell data or post-mortem analysis), the electrode-assignment claim is not established.
  4. [Sec. 7.2] The measured-data fitting excludes the 10% and 90% SOC spectra because they were 'hard to fit'. This exclusion removes the operating points where the OCP slope is steepest and where the diffusion tail and stoichiometry information are most informative. The paper's claim that fitting must be done simultaneously across a wide SOC range is weakened by excluding exactly the extreme SOCs. The authors should report the fits at these SOCs, discuss why they fail, and quantify how their exclusion affects the remaining parameter estimates.
minor comments (4)
  1. [Sec. 3, Table 1] The DFN impedance is computed but not validated against brute-force simulation, as the brute-force DFN computation was prohibitively long. The paper should state this limitation explicitly in the validation section, rather than only in the table caption.
  2. [Sec. 7.1, Eq. (18)] The signal-to-nonlinear distortion ratio is defined as |V(ω)/V(2ω)| at the lowest frequency, but the text does not specify whether V denotes the voltage Fourier coefficient or the impedance. Please clarify the notation.
  3. [Appendix A, Eq. (A.8) and Appendix C, Eq. (C.7)] In Eqs. (A.8) and (C.7), the two logarithmic terms are written identically, which appears to be a typo; presumably one refers to the electrode-surface value and the other to the electrode-average value of the electrolyte concentration. Please correct the expressions.
  4. [Sec. 5.2] The time-scale ranges are described as approximate, but the overlap between particle and electrolyte diffusion time-scales (τd+ = 6812 s, τe+ = 409 s) is large; a brief discussion of how this overlap affects the interpretation of the diffusion 'bump' would improve the section.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the numerical impedance method is benchmarked against an independent brute-force simulation, and the measured-data validation uses a drive cycle not used in fitting.

full rationale

The paper's central derivation is self-contained and externally checked. The frequency-domain automatic-differentiation impedance is validated against brute-force time-domain simulation of the same model (Sec. 3), which is an independent numerical implementation of the measurement procedure rather than a circular target. The grouped-parameter SPMe is fully re-derived in Appendix C and verified to reproduce PyBaMM's native SPMe, so the citations for grouping and double-layer capacitance are corroboration, not load-bearing. The diffusion-tail dependence on OCP slope (Eq. 15) is also derived in Appendix D, not merely imported from the cited works. The simulation-to-simulation parameter estimation uses known true parameters, and the measured-data fitting is validated on a drive cycle (Sec. 7.3) that was not used in fitting. The paper's own limitation about poor low-frequency fits and OCP hysteresis (Sec. 7.2) identifies a data/model mismatch and identifiability risk, but it is not a circular reduction: the OCP curves are external inputs, and the fitted diffusion and stoichiometry parameters are not being used to define the target impedance. No fitted quantity is renamed as a prediction, and no self-citation carries the load of the central claims.

Assumptions & free parameters 18 free parameters · 5 assumptions · 0 invented entities

The central measured-data claims rest on 18 fitted grouped parameters, several of which clip at their bounds; on fixed relative electrode thicknesses; on the SPMe model structure; on the half-cell OCP data; and on the post-hoc decision to drop 10% and 90% SOC. Linearisation and Fourier transformation are standard small-signal mathematics.

free parameters (18)
  • tau_d+ (positive particle diffusion timescale) = 8953 s
    Target grouped parameter fitted to LG M50LT EIS; affects the low-frequency diffusion tail.
  • tau_d- (negative particle diffusion timescale) = 7974 s
    Target grouped parameter fitted to LG M50LT EIS; affects the low-frequency diffusion tail.
  • tau_e+ (positive electrolyte diffusion timescale) = 100 s (clipped at lower bound)
    Fitted grouped parameter; in the measured fit it clips at the optimisation bound.
  • tau_e- (negative electrolyte diffusion timescale) = 100 s (clipped at lower bound)
    Fitted grouped parameter; in the measured fit it clips at the optimisation bound.
  • tau_e_sep (separator electrolyte diffusion timescale) = 100 s (clipped at lower bound)
    Fitted grouped parameter; in the measured fit it clips at the optimisation bound.
  • zeta+ (positive relative porosity) = 1.70 (clipped at upper bound)
    Fitted grouped parameter; in the measured fit it clips at the optimisation bound.
  • zeta- (negative relative porosity) = 1.70 (clipped at upper bound)
    Fitted grouped parameter; in the measured fit it clips at the optimisation bound.
  • Qe (reference electrolyte capacity) = 2000 As (clipped at upper bound)
    Fitted grouped parameter; in the measured fit it clips at the optimisation bound.
  • tau_ct+ (positive charge transfer timescale) = 1087 s
    Fitted grouped parameter controlling the diameter of the positive electrode semi-circle.
  • tau_ct- (negative charge transfer timescale) = 1515 s
    Fitted grouped parameter controlling the diameter of the negative electrode semi-circle.
  • C+ (positive double-layer capacitance) = 2 F (clipped at upper bound)
    Fitted grouped parameter; in the measured fit it clips at the optimisation bound.
  • C- (negative double-layer capacitance) = 0.532 F
    Fitted grouped parameter affecting the frequency spread on the negative electrode semi-circle.
  • c0%+ (positive stoichiometry at 0% SOC) = 0.983
    Fitted grouped parameter determining the positive electrode state at low SOC.
  • c0%- (negative stoichiometry at 0% SOC) = 0.0015
    Fitted grouped parameter determining the negative electrode state at low SOC.
  • c100%+ (positive stoichiometry at 100% SOC) = 0.190
    Fitted grouped parameter determining the positive electrode state at high SOC.
  • c100%- (negative stoichiometry at 100% SOC) = 0.886
    Fitted grouped parameter determining the negative electrode state at high SOC.
  • t+ (cation transference number) = 0.70 (clipped at upper bound)
    Fitted grouped parameter; in the measured fit it clips at the optimisation bound.
  • R0 (series resistance) = 14.5 mOhm
    Fitted grouped parameter shifting the impedance horizontally along the real axis.
assumptions (5)
  • standard math Taylor linearization and Fourier transformation of the DAE system (B.9)-(B.10) are valid for small perturbations around a stationary operating point.
    This is the standard small-signal linearization used to define impedance; the paper checks linearity empirically through SNLDR.
  • domain assumption The SPMe with double-layer capacitance and Butler-Volmer kinetics, with OCPs from half-cell measurements, is a sufficient model for the LG M50LT cell.
    The measured-data parametrisation and electrode assignment rely on this model structure. The paper concedes low-frequency fits are poor, which suggests the model is not fully sufficient.
  • ad hoc to paper 10% and 90% SOC measured EIS are excluded from fitting because they were hard to fit.
    Sec. 7.2 states these SOCs were excluded after observing fitting difficulty, which shapes the reported measured-data fits and is a post hoc selection rule.
  • domain assumption Relative electrode thicknesses l+ and l- are assumed known and fixed during estimation.
    Sec. 6 explicitly says 'Assuming that the relative thicknesses l+/- are known'; these values enter the grouped model and are not estimated.
  • domain assumption The optimisation bounds and 1000 iterations of particle swarm optimisation are sufficient to recover the global optimum.
    Several parameters clip at their bounds in the measured fit, and the paper notes the cost function is highly non-convex, so the search or constraints may limit identifiability.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Physics-based battery model parametrisation from impedance data." pith.science (2026). https://pith.science/paper/RVTE65EW

@misc{pith2026241210896,
  author       = {Pith},
  title        = {Pith review of: Physics-based battery model parametrisation from impedance data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RVTE65EW}},
  note         = {Machine review of arXiv:2412.10896}
}
read the original abstract

Non-invasive parametrisation of physics-based battery models can be performed by fitting the model to electrochemical impedance spectroscopy (EIS) data containing features related to the different physical processes. However, this requires an impedance model to be derived, which may be complex to obtain analytically. We have developed the open-source software PyBaMM-EIS that provides a fast method to compute the impedance of any PyBaMM model at any operating point using automatic differentiation. Using PyBaMM-EIS, we investigate the impedance of the single particle model, single particle model with electrolyte (SPMe), and Doyle-Fuller-Newman model, and identify the SPMe as a parsimonious option that shows the typical features of measured lithium-ion cell impedance data. We provide a grouped parameter SPMe and analyse the features in the impedance related to each parameter. Using the open-source software PyBOP, we estimate 18 grouped parameters both from simulated impedance data and from measured impedance data from a LG M50LT lithium-ion battery. The parameters that directly affect the response of the SPMe can be accurately determined and assigned to the correct electrode. Crucially, parameter fitting must be done simultaneously to data across a wide range of states-of-charge. Overall, this work presents a practical way to find the parameters of physics-based models.

Figures

Figures reproduced from arXiv: 2412.10896 by the authors.

Figure 1
Figure 1. Illustration of a physics-based battery model with single [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Nyquist plot of measured EIS data of the LG M50LT at [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 5
Figure 5. Nyquist plot of the EIS spectra of the SPM, SPMe, and [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figures from the paper (9 more)
Figure 6
Figure 6. Figure 6: The different processes for the SPMe are the following: • τ < 1 ms (f > 1 kHz): At very short time-scales the SPMe acts as a series resistance R0. • 1 ms < τ < 1 s (1 Hz < f < 1 kHz): Here, the dominant process is charge transfer, represented by two semi-circles in the…
Figure 7
Figure 7. Figure 7: EIS spectra of grouped SPMe (Appendix C) at di [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Impedance of SPMe at 50% SOC, 25 ◦C, varying grouped parameters one-by-one. Parameters are perturbed with logarithmic spacing in the range [0.5θ, 2θ] (blue to red) with θ listed in [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 2
Figure 2. Figure 2: The dataset consists of 7 minutes resting at 90% [PITH_FULL_IMAGE:figures/full_fig_p011_2.png]
Figure 9
Figure 9. Figure 9: Measured OCV and OCP data for the LG M50LT. Dots [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: SPMe fits to impedance measurements LG M50LT battery. [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 11
Figure 11. Figure 11: Validation of the parametrised SPMe using a drive cy [PITH_FULL_IMAGE:figures/full_fig_p014_11.png]
Figure 13
Figure 13. Figure 13: Fits of simulated SPMe impedance data and relative error. [PITH_FULL_IMAGE:figures/full_fig_p019_13.png]
Figure 12
Figure 12. Figure 12: Sensitivity of the SPMe impedance to particle di [PITH_FULL_IMAGE:figures/full_fig_p019_12.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Primer on Bayesian Parameter Estimation and Model Selection for Battery Simulators

    stat.ME 2025-12 conditional novelty 4.0 of 10

    SOBER and BASQ, two previously published Bayesian algorithms, are adapted for battery simulators and demonstrated on six case studies, including impedance-based model selection.

Reference graph

Works this paper leans on

58 extracted references · 57 canonical work pages · cited by 1 Pith paper

  1. [1]

    G. L. Plett, Battery management systems, V olume II: Equivalent-circuit methods, Artech House, 2015

    work page 2015

  2. [2]

    X. Hu, S. Li, H. Peng, A comparative study of equivalent cir- cuit models for Li-ion batteries, Journal of Power Sources 198 (2012) 359–367. Figure 13: Fits of simulated SPMe impedance data and relative error. The estimated parameters are listed in Table 3

    work page 2012

  3. [3]

    Lagnoni, C

    M. Lagnoni, C. Scarpelli, G. Lutzemberger, A. Bertei, Crit- ical comparison of equivalent circuit and physics-based mod- els for lithium-ion batteries: A graphite /lithium-iron-phosphate case study, Journal of Energy Storage 94 (2024) 112326

    work page 2024

  4. [4]

    G. L. Plett, M. S. Trimboli, Battery management systems, V ol- ume III: Physics-Based Methods, Artech House, 2023

    work page 2023

  5. [5]

    A. Tian, K. Dong, X.-G. Yang, Y . Wang, L. He, Y . Gao, J. Jiang, Physics-based parameter identification of an electrochemical model for lithium-ion batteries with two-population optimiza- tion method, Applied Energy 378 (2025) 124748

    work page 2025

  6. [6]

    Doyle, T

    M. Doyle, T. F. Fuller, J. Newman, Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell, Jour- nal of the Electrochemical society 140 (6) (1993) 1526

    work page 1993

  7. [7]

    Doyle, J

    M. Doyle, J. Newman, A. S. Gozdz, C. N. Schmutz, J.-M. Taras- con, Comparison of modeling predictions with experimental data from plastic lithium ion cells, Journal of the Electrochemi- cal Society 143 (6) (1996) 1890. doi:10.1149/1.1836921

    work page doi:10.1149/1.1836921 1996

  8. [8]

    M. Guo, G. Sikha, R. E. White, Single-particle model for a lithium-ion cell: Thermal behavior, Journal of The Electrochem- ical Society 158 (2) (2010) A122

    work page 2010

Show all 58 references
  1. [9]

    S. J. Moura, F. B. Argomedo, R. Klein, A. Mirtabatabaei, M. Krstic, Battery state estimation for a single particle model with electrolyte dynamics, IEEE Transactions on Control Sys- tems Technology 25 (2) (2016) 453–468

    2016

  2. [10]

    S. G. Marquis, V . Sulzer, R. Timms, C. P. Please, S. J. Chapman, An asymptotic derivation of a single particle model with elec- trolyte, Journal of The Electrochemical Society 166 (15) (2019) A3693

    2019

  3. [11]

    Brosa Planella, M

    F. Brosa Planella, M. Sheikh, W. D. Widanage, Systematic derivation and validation of a reduced thermal-electrochemical model for lithium-ion batteries using asymptotic methods, Elec- trochimica Acta 388 (2021) 138524

    2021

  4. [12]

    T. L. Kirk, J. Evans, C. P. Please, S. J. Chapman, Modeling electrode heterogeneity in lithium-ion batteries: Unimodal and bimodal particle-size distributions, SIAM Journal on Applied Mathematics 82 (2) (2022) 625–653

    2022

  5. [13]

    D. Lu, M. S. Trimboli, G. Fan, R. Zhang, G. L. Plett, Nonde- structive pulse testing to estimate a subset of physics-based- model parameter values for lithium-ion cells, Journal of The 19 Electrochemical Society 168 (8) (2021) 080533

    2021

  6. [14]

    Iurilli, C

    P. Iurilli, C. Brivio, V . Wood, On the use of electrochemical impedance spectroscopy to characterize and model the aging phenomena of lithium-ion batteries: A critical review, Journal of Power Sources 505 (2021) 229860

    2021

  7. [15]

    Hileman, M

    W. Hileman, M. S. Trimboli, G. Plett, Estimating the values of the pde model parameters of rechargeable lithium-metal battery cells using linear EIS, ASME Letters in Dynamic Systems and Control (2024) 1–7

    2024

  8. [16]

    X. Zhu, M. Cazorla Soult, B. Wouters, M. H. Mamme, Study of solid-state diffusion impedance in li-ion batteries using parallel- diffusion Warburg model, Journal of The Electrochemical Soci- ety (2024)

    2024

  9. [17]

    Wimarshana, I

    B. Wimarshana, I. Bin-Mat-Arishad, A. Fly, A multi-step pa- rameter identification of a physico-chemical lithium-ion battery model with electrochemical impedance data, Journal of Power Sources 580 (2023) 233400

    2023

  10. [18]

    M. E. Orazem, B. Tribollet, Electrochemical Impedance Spec- troscopy, Wiley, 2008

    2008

  11. [19]

    S. Wang, J. Zhang, O. Gharbi, V . Vivier, M. Gao, M. E. Orazem, Electrochemical impedance spectroscopy, Nature Re- views Methods Primers 1 (1) (2021) 41

    2021

  12. [20]

    Vivier, M

    V . Vivier, M. E. Orazem, Impedance analysis of electrochemical systems, Chemical Reviews 122 (12) (2022) 11131–11168

    2022

  13. [21]

    D. Lu, M. S. Trimboli, G. Fan, Y . Wang, G. L. Plett, Nonde- structive EIS testing to estimate a subset of physics-based-model parameter values for lithium-ion cells, Journal of The Electro- chemical Society 169 (8) (2022) 080504

    2022

  14. [22]

    M. D. Murbach, V . W. Hu, D. T. Schwartz, Nonlinear electro- chemical impedance spectroscopy of lithium-ion batteries: ex- perimental approach, analysis, and initial findings, Journal of The Electrochemical Society 165 (11) (2018) A2758

    2018

  15. [23]

    T. L. Kirk, A. Lewis-Douglas, D. Howey, C. P. Please, S. J. Chapman, Nonlinear electrochemical impedance spectroscopy for lithium-ion battery model parameterization, Journal of The Electrochemical Society 170 (1) (2023) 010514

    2023

  16. [24]

    Y . Ji, D. T. Schwartz, Second-harmonic nonlinear electrochem- ical impedance spectroscopy: Part II. model-based analysis of lithium-ion battery experiments, Journal of The Electrochemi- cal Society 171 (2) (2024) 023504

    2024

  17. [25]

    Lasia, Impedance of porous electrodes, Journal of Electroan- alytical Chemistry 397 (1-2) (1995) 27–33

    A. Lasia, Impedance of porous electrodes, Journal of Electroan- alytical Chemistry 397 (1-2) (1995) 27–33

    1995

  18. [26]

    J. P. Meyers, M. Doyle, R. M. Darling, J. Newman, The impedance response of a porous electrode composed of interca- lation particles, Journal of the Electrochemical Society 147 (8) (2000) 2930

    2000

  19. [27]

    Albertus, J

    P. Albertus, J. Newman, Introduction to dualfoil 5.0, University of California Berkeley, Berkeley, CA, Tech. Rep (2007)

    2007

  20. [28]

    J. Song, M. Z. Bazant, E ffects of nanoparticle geometry and size distribution on diffusion impedance of battery electrodes, Jour- nal of The Electrochemical Society 160 (1) (2012) A15

    2012

  21. [29]

    A. M. Bizeray, J.-H. Kim, S. R. Duncan, D. A. Howey, Identifia- bility and parameter estimation of the single particle lithium-ion battery model, IEEE Transactions on Control Systems Technol- ogy 27 (5) (2018) 1862–1877

    2018

  22. [30]

    H. Zhu, T. A. Evans, P. J. Weddle, A. M. Colclasure, B.-R. Chen, T. R. Tanim, T. L. Vincent, R. J. Kee, Extracting and inter- preting electrochemical impedance spectra (EIS) from physics- based models of lithium-ion batteries, Journal of the Electro- chemical Society 171 (5) (...

    2024

  23. [31]

    Sulzer, S

    V . Sulzer, S. G. Marquis, R. Timms, M. Robinson, S. J. Chap- man, Python battery mathematical modelling (PyBaMM), Jour- nal of Open Research Software 9 (1) (2021)

    2021

  24. [32]

    Bradbury, R

    J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, Q. Zhang, JAX: composable transforma- tions of Python+NumPy programs (2018). URL http://github.com/jax-ml/jax

    2018

  25. [33]

    M. ˇZic, V . Suboti´c, S. Pereverzyev, I. Fajfar, Solving CNLS problems using Levenberg-Marquardt algorithm: A new fitting strategy combining limits and a symbolic Jacobian matrix, Jour- nal of Electroanalytical Chemistry 866 (2020) 114171

    2020

  26. [34]

    Huang, C

    Y . Huang, C. Zou, Y . Li, T. Wik, Minn: Learning the dynam- ics of differential-algebraic equations and application to battery modeling, IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

    2024

  27. [35]

    Planden, N

    B. Planden, N. E. Courtier, M. Robinson, A. Khetarpal, F. B. Planella, D. A. Howey, PyBOP: A Python package for bat- tery model optimisation and parameterisation, arXiv preprint arXiv:2412.15859 (2024)

    2024

  28. [36]

    Aitio, S

    A. Aitio, S. G. Marquis, P. Ascencio, D. Howey, Bayesian pa- rameter estimation applied to the Li-ion battery single particle model with electrolyte dynamics, IFAC-PapersOnLine 53 (2) (2020) 12497–12504

    2020

  29. [37]

    W. Hu, Y . Peng, Y . Wei, Y . Yang, Application of electrochem- ical impedance spectroscopy to degradation and aging research of lithium-ion batteries, The Journal of Physical Chemistry C 127 (9) (2023) 4465–4495

    2023

  30. [38]

    Hallemans, D

    N. Hallemans, D. Howey, A. Battistel, N. F. Saniee, F. Scar- pioni, B. Wouters, F. La Mantia, A. Hubin, W. D. Widanage, J. Lataire, Electrochemical impedance spectroscopy beyond lin- earity and stationarity—A critical review, Electrochimica Acta (2023) 142939

    2023

  31. [39]

    M. A. Zabara, J. Goh, V . Gaudio, L. Zou, M. Orazem, B. Ulgut, Utility of Lissajous plots for electrochemical impedance spec- troscopy measurements: detection of non-linearity and non- stationarity, Journal of The Electrochemical Society 171 (1) (2024) 010507

    2024

  32. [40]

    Urquidi-Macdonald, S

    M. Urquidi-Macdonald, S. Real, D. D. Macdonald, Applications of Kramers—Kronig transforms in the analysis of electrochem- ical impedance data—iii. stability and linearity, Electrochimica Acta 35 (10) (1990) 1559–1566

    1990

  33. [41]

    Agarwal, M

    P. Agarwal, M. E. Orazem, L. H. Garcia-Rubio, Application of measurement models to impedance spectroscopy: III. Evalua- tion of consistency with the Kramers-Kronig relations, Journal of the Electrochemical Society 142 (12) (1995) 4159

    1995

  34. [42]

    M. E. Orazem, Measurement model for analysis of electrochem- ical impedance data, Journal of Solid State Electrochemistry 28 (3) (2024) 1273–1289

    2024

  35. [43]

    Hallemans, W

    N. Hallemans, W. D. Widanage, X. Zhu, S. Moharana, M. Rashid, A. Hubin, J. Lataire, Operando electrochemical impedance spectroscopy and its application to commercial Li- ion batteries, Journal of Power Sources 547 (2022) 232005

    2022

  36. [44]

    R. J. LeVeque, et al., Finite volume methods for hyperbolic problems, V ol. 31, Cambridge University Press, 2002

    2002

  37. [45]

    C.-H. Chen, F. Brosa Planella, K. O’regan, D. Gastol, W. D. Widanage, E. Kendrick, Development of experimental tech- niques for parameterization of multi-scale lithium-ion battery models, Journal of The Electrochemical Society 167 (8) (2020) 080534

    2020

  38. [46]

    Vandeputte, N

    F. Vandeputte, N. Hallemans, J. A. Kuzhiyil, N. F. Saniee, W. D. Widanage, J. Lataire, Frequency domain parametric estimation of fractional order impedance models for Li-ion batteries, IFAC- PapersOnLine 56 (2) (2023) 4325–4330

    2023

  39. [47]

    M. E. Orazem, B. Ulgut, On the proper use of a Warburg impedance, Journal of The Electrochemical Society 171 (4) (2024) 040526

    2024

  40. [48]

    Vandeputte, N

    F. Vandeputte, N. Hallemans, J. Lataire, Parametric estima- tion of arbitrary fractional order models for battery impedances, IFAC-PapersOnLine 58 (15) (2024) 97–102. 20

    2024

  41. [49]

    G. L. Plett, M. S. Trimboli, Process for lumping parameters to enable nondestructive parameter estimation for lithium-ion physics-based models, Proceedings of the 35th International Electric Vehicle Symposium and Exhibition (EVS35), Oslo, Norway (2022)

    2022

  42. [50]

    Khalik, M

    Z. Khalik, M. Donkers, J. Sturm, H. J. Bergveld, Parameter es- timation of the Doyle–Fuller–Newman model for lithium-ion batteries by parameter normalization, grouping, and sensitivity analysis, Journal of Power Sources 499 (2021) 229901

    2021

  43. [51]

    Lu, C.-Z

    Y . Lu, C.-Z. Zhao, J.-Q. Huang, Q. Zhang, The timescale iden- tification decoupling complicated kinetic processes in lithium batteries, Joule 6 (6) (2022) 1172–1198

    2022

  44. [52]

    Ciucci, C

    F. Ciucci, C. Chen, Analysis of electrochemical impedance spectroscopy data using the distribution of relaxation times: A bayesian and hierarchical bayesian approach, Electrochimica Acta 167 (2015) 439–454

    2015

  45. [53]

    J. J. Rissanen, Fisher information and stochastic complexity, IEEE transactions on information theory 42 (1) (1996) 40–47

    1996

  46. [54]

    Mendoza, S

    H. Mendoza, S. A. Roberts, V . E. Brunini, A. M. Grillet, Me- chanical and electrochemical response of a LiCoO2 cathode using reconstructed microstructures, Electrochimica Acta 190 (2016) 1–15

    2016

  47. [55]

    J. S. Horner, G. Whang, D. S. Ashby, I. V . Kolesnichenko, T. N. Lambert, B. S. Dunn, A. A. Talin, S. A. Roberts, Electrochem- ical modeling of GITT measurements for improved solid-state diffusion coefficient evaluation, ACS Applied Energy Materials 4 (10) (2021) 11460–11469

    2021

  48. [56]

    S. G. Marquis, Long-term degradation of lithium-ion batteries, Ph.D. thesis, University of Oxford (2020)

    2020

  49. [57]

    X. Zhu, N. Hallemans, B. Wouters, R. Claessens, J. Lataire, A. Hubin, Operando odd random phase electrochemical impedance spectroscopy as a promising tool for monitoring lithium-ion batteries during fast charging, Journal of Power Sources 544 (2022) 231852

    2022

  50. [58]

    H. A. van der V orst, Bi-CGSTAB: A fast and smoothly converg- ing variant of Bi-CG for the solution of nonsymmetric linear systems, SIAM Journal on Scientific and Statistical Computing 13 (2) (1992) 631–644. 21

    1992

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.