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Cited by 407Advanced Battery Technologies ResearchAdvancements in Battery MaterialsGaussian Processes and Bayesian Inference

Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries

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Richardson, Robert R., Birkl, Christoph R., Osborne, Michael A., Howey, David A.·2019·IEEE Transactions on Industrial Informatics
DOI 10.1109/tii.2018.2794997
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Abstract

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage versus time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian process regression for in situ capacity estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as incremental capacity (IC) or differential voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process that amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells, respectively. In each case, within certain voltage ranges, as little as 10 s of galvanostatic operation enables capacity estimates with approximately 2%-3% root-mean-squared error (RMSE).

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Crossref · 10.1109/tii.2018.2794997

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43 of 43 references verified0 citation errors · 0 not checkable

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  • 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 [17]

    The Introduction (p. 1) describes studies using features from IC/DV curves for capacity estimation, supporting the claim that charging-curve-derived features are used. It does not establish that they are by far the most common inputs or that their use is due to typical BMS data collection; instead, it calls model-parameter estimation the most common approach to capacity estimation. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • 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 [17]

    The Introduction (p. 1) describes earlier regression models that use features from incremental-capacity or differential-voltage curves as inputs. But it does not establish that all existing machine-learning models use charging and discharging curves; the Conclusions (p. 10) describe this paper’s own model as using small portions of voltage-time data instead. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The paper departs from prior GPR works by employing impedance spectra as input and ARD to shed light on salient frequencies.

    “Although GPR has been used in the literature in the context of Li-ion batteries17,33,34, we depart from those pioneering works by employing impedance spectra as input, as well employing ARD to shed light on salient frequencies.”

    From Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning (2020) · cites this paper as [17]

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Cites

Flagged first, then by citations

2 of the 45 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

  1. [2][2] S.-K. Jung, H. Gwon, J. Hong, K.-Y. Park, D.-H. Seo, H. Kim, J. Hyun, W. Yang, and K. Kang, 'Understanding the degradation mechanisms of LiNi0. 5Co0. 2Mn0. 3O2 cathode material in lithium ion batteries,' Advanced Energy Materials , vol. 4, no. 1, 2014. -
  2. [12][12] S. J. Moura, N. A. Chaturvedi, and M. Krstic, 'PDE estimation techniques for advanced battery management systems,' in American Control Conference (ACC), 2012 . IEEE, 2012, pp. 566-571. -
  3. [33][33] M. S. Grewal, Kalman filtering . Springer, 2011. -

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Authors
Richardson, R. R., Birkl, C. R., Osborne, M. A., & Howey, D. A.
Affiliations
University of Oxford (GB)
ORCID
Venue
IEEE Transactions on Industrial Informatics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019-01
Volume
15
Issue
1
Pages
127-138
ISSN
  • 1551-3203 (ISSN-L)
  • 1941-0050
DOI
10.1109/tii.2018.2794997
OpenAlex
W2772728162
Cited by
407
References
45
Citations by year
2026: 46 · 2025: 59 · 2024: 58 · 2023: 62 · 2022: 70 · 2021: 61 · 2020: 29 · 2019: 18 · 2018: 4
FWCI
14.88
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Industrial Informatics (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Engineering and Physical Sciences Research Council (EP/K002252/1)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
  • Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Keywords
lithium-ion batteries, battery capacity estimation, Gaussian process regression, galvanostatic operation, battery diagnostics

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