Data-driven prediction of battery cycle life before capacity degradation

Severson, Kristen A.; Attia, Peter M.; Jin, Norman; Perkins, Nicholas; Jiang, Benben; Yang, Zi; Chen, Michael H.; Aykol, Muratahan; Herring, Patrick K.; Fraggedakis, Dimitrios; Bazant, Martin Z.; Harris, Stephen J.; Chueh, William C.; Braatz, Richard D.

doi:10.1038/s41560-019-0356-8

Cited by 3,228Advanced Battery Technologies ResearchAdvancements in Battery MaterialsMachine Learning in Materials Science

Data-driven prediction of battery cycle life before capacity degradation

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Severson, Kristen A., Attia, Peter M., Jin, Norman, Perkins, Nicholas, Jiang, Benben, Yang, Zi, Chen, Michael H., Aykol, Muratahan +6 more·2019·Nature Energy

DOI 10.1038/s41560-019-0356-8

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Existing machine learning models for battery degradation diagnosis are all developed with the charging and discharging curve as input.
“However, those models are all developed with the charging and discharging curve as input.”
From Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning (2020) · cites this paper as [18]
The Machine learning approach section distinguishes models using discharge data alone from a model that considers both charging and discharging features. The Results section confirms that its “variance” model uses one feature from discharge curves and its “discharge” model uses discharge measurements only. The paper therefore contradicts the claim that all such models require both charging and discharging curves as input. (LLM, conf=0.87)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured
Features derived from the charging and discharging curve are by far the most commonly used inputs for battery health prediction.
“Features derived from the charging and discharging curve are by far the most commonly used inputs because typical battery management systems collect current–voltage data13–18.”
From Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning (2020) · cites this paper as [18]
The Machine learning approach section says the authors derive predictive features from discharge voltage curves and describes voltage curves and their derivatives as a rich source for degradation diagnosis. The Discussion says differential voltage-curve methods are used extensively. Neither passage establishes that charging and discharging curves are by far the most common inputs for battery health prediction or that their use is because typical battery management systems collect current–voltage data. (LLM, conf=0.84)
Experimental — accuracy not yet measured
The first dataset consists of data from 177 batteries, cycled under 72 different one-step and two-step fast charging strategies, producing 111,211 cycles.
“The first dataset consists of data from 177 batteries,69 which were cycled under 72 different one-step and two-step fast charging strategies, producing 111,211 cycles.”
From Battery state of health estimation under fast charging via deep transfer learning (2025) · cites this paper as [69]
The Abstract (page unavailable) reports 124 cells cycled under 72 fast-charging conditions, and the Methods (page unavailable) describe single-step and two-step charging policies. The Data generation section (page unavailable) reports approximately 96,700 cycles, not 111,211. Thus the charging-strategy description is supported, but the claimed battery and cycle counts do not match this paper. (LLM, conf=0.90)
Experimental — accuracy not yet measured
Issues such as inconsistencies of cell characteristics, electrical imbalance and temperature gradients between cells complicate accurate ageing estimation and prediction models for packs.
“All these issues complicate accurate ageing estimation and prediction models for packs.”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [149]
The Abstract (page unavailable) says device variability and diverse ageing mechanisms challenge lifetime prediction, and that thermal heterogeneities within a cell complicate modelling. It does not address electrical imbalance or temperature gradients between cells, or establish those as challenges specifically for pack-level ageing prediction; its dataset and models concern individual cells. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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 [725]

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Authors: Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., Bazant, M. Z., Harris, S. J., Chueh, W. C., & Braatz, R. D.
Affiliations: Massachusetts Institute of Technology (US)
Stanford University (US)
Toyota Research Institute (US)
Lawrence Berkeley National Laboratory (US)
Corresponding author: Chueh, William C.
Braatz, Richard D.
ORCID: Severson, Kristen A.
Attia, Peter M.
Jin, Norman
Perkins, Nicholas
Jiang, Benben
Yang, Zi
Chen, Michael H.
Aykol, Muratahan
Herring, Patrick K.
Fraggedakis, Dimitrios
Bazant, Martin Z.
Harris, Stephen J.
Chueh, William C.
Braatz, Richard D.
Venue: Nature Energy
Publisher: Springer Science and Business Media LLC
Published: 2019-03-25
Volume: 4
Issue: 5
Pages: 383-391
ISSN: 2058-7546 (ISSN-L)
DOI: 10.1038/s41560-019-0356-8
OpenAlex: W2924382816
Cited by: 3,228
References: 65
Citations by year: 2026: 775 · 2025: 680 · 2024: 515 · 2023: 450 · 2022: 355 · 2021: 261 · 2020: 156 · 2019: 34
FWCI: 91.94
Type: journal article
Language: en
Open access: green
escholarship.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Energy (publishedVersion)
eScholarship (California Digital Library) (submittedVersion)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: National Science Foundation ((DGE-114747, 114747)
U.S. Department of Energy (DGE-114747)
Toyota Research Institute
Division of Materials Research (DGE-114747)
Topics: Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords: cycle life prediction, capacity fade, fast charging, battery aging mechanisms, battery datasets, cell classification, machine learning

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