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Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method
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“Prognostic predictions need to cope with uncertainties coming from the measurement, the operation environment and the model itself -these arise from the structure of the model and uncertain parameters [110].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [110]
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Research works printed but not linked: 2
- IEEE Std. 1188-2005 IEEE Recommended Practices for Maintenance, Testing and Replacement of Valve Regulated Lead-Acid (VRLA) Batteries in Stationary Applications.
- Pecht, 1995, Journal of the Institute of Environmental Sciences, 38, 30
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Metadata
- Authors
- He, W., Williard, N., Osterman, M., & Pecht, M.
- Affiliations
- University of Maryland, College Park (US)
- Corresponding author
- Pecht, Michael
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2011-12
- Volume
- 196
- Issue
- 23
- Pages
- 10314-10321
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W2078279667
- Cited by
- 1,105
- References
- 30
- Citations by year
- 2026: 138 · 2025: 140 · 2024: 126 · 2023: 132 · 2022: 90 · 2021: 76 · 2020: 73 · 2019: 61 · 2018: 61 · 2017: 54 · 2016: 42 · 2015: 37 · 2014: 29 · 2013: 29 · 2012: 16
- FWCI
- 49.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Power Sources (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 › Electrical and Electronic Engineering › Advancements in Battery Materials
- Keywords
- lithium-ion batteries, battery prognostics, remaining useful life prediction, state of health estimation, Dempster-Shafer theory, Bayesian Monte Carlo, battery degradation modeling, model parameter updating
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