Cited by 60Advanced Battery Technologies ResearchAdvancements in Battery MaterialsControl Systems and Identification

Analysis of Thousands of Electrochemical Impedance Spectra of Lithium-Ion Cells through a Machine Learning Inverse Model

Open access
Buteau, Sam, Dahn, J. R.·2019·Journal of The Electrochemical Society

Abstract

Electrochemical impedance spectra of lithium-ion cells can be collected periodically at various cycle numbers and various state of charges, producing vast amounts of data. Fitting each spectrum to an equivalent circuit can lead to physical insights about the evolution of the lithium-ion cell, yet the fitting problem requires good human initial guesses for the circuit parameters to reliably converge, making the fitting process labor intensive and difficult to scale. This article presents a paradigm to automate the fitting of measured data to physical models, replacing the good human first guesses with an inverse model parametrized with an artificial neural network. This method is simple to implement, uses principles applicable to a wide variety of fitting problems, and leads to reliable and accurate initial guesses of the circuit parameters for a given spectrum. The software implementation will be freely available once a good user interface is developed, and the performance of the system is evaluated on a dataset of about 100000 impedance spectra from lithium-ion cells, achieving a failure of fitting approximately 1% of the dataset, corresponding to the percentage of poor quality data in the dataset.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1149/2.1051908jes

Cites

21 of 23 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Work in battery ML has focused on predicting battery state, degradation, and remaining lifetime using supervised learning, fuzzy logic, and clustering.

    “Work in this area has focused on predicting battery state, degradation, and remaining lifetime using supervised learning techniques, fuzzy logic, and clustering [42,104,218,358,414,725,735,819,854].”

    From Tackling climate change with machine learning (2023) · cites this paper as [104]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. K. Nelson, thesis, Dalhousie University (2017) https://DalSpace.library.dal.ca/ /handle/10222/73493.
  2. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, J. Mach. Learn. Res., 15, 1929 (2014).

Cited by

Sorted by citations

Authors
Buteau, S., & Dahn, J. R.
Affiliations
Dalhousie University (CA)
Venue
Journal of The Electrochemical Society
Published
2019
Volume
166
Issue
8
Pages
A1611-A1622
ISSN
  • 0013-4651 (ISSN-L)
  • 1945-7111
OpenAlex
W2945480173
Cited by
60
References
20
Citations by year
2026: 6 · 2025: 7 · 2024: 10 · 2023: 8 · 2022: 15 · 2021: 8 · 2020: 5 · 2019: 1
FWCI
3.31
Type
journal article
Language
en
Open access
Locations
Journal of The Electrochemical Society (publishedVersion, cc-by)
Funders
Natural Sciences and Engineering Research Council of Canada
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
  • Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Keywords
lithium-ion cells, electrochemical impedance spectra, equivalent circuit model, inverse model, artificial neural networks, initial guess generation, battery degradation, state of charge

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review