The DOI matches its Crossref record; 10 of the 11 references it cites were found in a registry or an index.
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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In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Gordon, N., Salmond, D. J., & Smith, A. F. M. (1993) · IEE Proceedings F Radar and Signal Processing · doi:10.1049/ip-f-2.1993.0015 · Cited by 7,711
Advances in neural information processing systems 11
Peter Sollich, A. (1999) · Cited by 3,168
Tipping, M. E. (1999) · Cited by 869
Dynamic lithium-ion battery model for system simulation
Gao, L., Liu, S., & Dougal, R. A. (2002) · IEEE Transactions on Components and Packaging Technologies · doi:10.1109/tcapt.2002.803653 · Cited by 797
Battery Monitoring and Electrical Energy Management
Meissner, E., & Richter, G. (2003) · Journal of Power Sources · doi:10.1016/s0378-7753(02)00713-9 · Cited by 257
An integrated approach to battery health monitoring using bayesian regression and state estimation
Saha, B., Poll, S., Goebel, K., & Christophersen, J. (2007) · 2007 IEEE Autotestcon · doi:10.1109/autest.2007.4374280 · Cited by 207
Self-citationCox, D. C., & Perez-Kite, R. (2002) · International Telecommunications Energy Conference (INTELEC) · doi:10.1109/intlec.2000.884272 · Cited by 38
Christophersen, J. P., Bloom, I., Thomas, E. V., Gering, K. L., Henriksen, G. L., Battaglia, V. S., & Howell, D. (2006) · doi:10.2172/911596 · Cited by 26
Self-citationDetermining the state-of-health of maintenance-free aircraft batteries
Vutetakis, D. G., & Viswanathan, V. (2002) · Annual Battery Conference on Applications and Advances · doi:10.1109/bcaa.1995.398516 · Cited by 25
Flagged first, then by citations
1 of the 11 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- 2001, PNGV Test Plan for Advanced Technology Development Gen 2 Lithium-Ion Cells
Cited by
Sorted by citations
Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110015 · Cited by 1,394
Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193
Critical review of state of health estimation methods of Li-ion batteries for real applications
Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Van Mierlo, J., & Van den Bossche, P. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.11.042 · Cited by 1,126
He, W., Williard, N., Osterman, M., & Pecht, M. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.08.040 · Cited by 1,105
Metadata
- 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 › Battery Modeling and Management
- 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
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