Cited by 1,105Battery Modeling and ManagementReliability and Maintenance OptimizationAdvancements in Battery Materials

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Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method

He, Wei, Williard, Nicholas, Osterman, Michael, Pecht, Michael·2011·Journal of Power Sources

DOI 10.1016/j.jpowsour.2011.08.040

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Authors: He, W., Williard, N., Osterman, M., & Pecht, M.
Affiliations: University of Maryland, College Park (US)
Corresponding author: Pecht, Michael
ORCID: Williard, Nicholas
Osterman, Michael
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)
DOI: 10.1016/j.jpowsour.2011.08.040
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method

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