Prognostics in Battery Health Management
Abstract
In this article, we examine prognostics and health management (PHM) issues using battery health management of Gen 2 cells, an 18650-size lithium-ion cell, as a test case. We will show where advanced regression, classification, and state estimation algorithms have an important role in the solution of the problem and in the data collection scheme for battery health management that we used for this case study.
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For the Kalman filter family, the state space PDF remains Gaussian at every iteration and the filter equations propagate and update the mean and covariance of the distribution.
“For the KF family, the state space PDF remains Gaussian at every iteration and the filter equations propagate and update the mean and covariance of the distribution [120].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [120]
GPR is a kernel based ML method that can realize prognostics combined with prior knowledge based on a Bayesian framework and provide variance around its mean prediction to describe associated uncertainty.
“GPR is a kernel based ML method, which can realize prognostics combined with prior knowledge based on a Bayesian framework and provide variance around its mean prediction to describe the associated uncertainty [120].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [120]
Capturing the conditional distribution to capture prediction uncertainty level is a real challenge due to uncertainties from various sources such as measurements, state estimation, model inaccuracies, and future load uncertainty.
“Ideally, however, the conditional distribution in order to capture the prediction uncertainty level, is a real challenge due to uncertainties from various sources such as measurements, state estimation, model inaccuracies, and future load uncertainty [120].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [120]
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- Authors
- Goebel, K., Saha, B., Saxena, A., Celaya, J., & Christophersen, J.
- Affiliations
- Ames Research Center (US)
- Idaho National Laboratory (US)
- Venue
- IEEE Instrumentation & Measurement Magazine
- Published
- 2008-08
- Volume
- 11
- Issue
- 4
- Pages
- 33-40
- ISSN
- 1094-6969 (ISSN-L)
- OpenAlex
- W2115233498
- Cited by
- 594
- References
- 11
- Citations by year
- 2026: 21 · 2025: 46 · 2024: 46 · 2023: 62 · 2022: 46 · 2021: 59 · 2020: 40 · 2019: 48 · 2018: 39 · 2017: 29 · 2016: 26 · 2015: 28 · 2014: 34 · 2013: 24 · 2012: 17
- FWCI
- 22.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Instrumentation & Measurement Magazine (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- prognostics and health management, battery health management, lithium-ion cells, 18650 cells, battery degradation prediction, regression analysis, classification, state estimation
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 ›
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