Analysis of Thousands of Electrochemical Impedance Spectra of Lithium-Ion Cells through a Machine Learning Inverse Model
Open accessAbstract
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.
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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]
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- Authors
- Buteau, S., & Dahn, J. R.
- Affiliations
- Dalhousie University (CA)
- ORCID
- Dahn, J. R.
- Venue
- Journal of The Electrochemical Society
- Publisher
- 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
- hybrid
- doi.org
- 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
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