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A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

Yang, Duo, Zhang, Xu, Pan, Rui, Wang, Yujie, Chen, Zonghai·2018·Journal of Power Sources

DOI 10.1016/j.jpowsour.2018.03.015

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Authors: Yang, D., Zhang, X., Pan, R., Wang, Y., & Chen, Z.
Affiliations: University of Science and Technology of China (CN)
Corresponding author: Chen, Zonghai
ORCID: Yang, Duo
Pan, Rui
Wang, Yujie
Chen, Zonghai
Venue: Journal of Power Sources
Publisher: Elsevier BV
Published: 2018-04
Volume: 384
Pages: 387-395
ISSN: 0378-7753 (ISSN-L)
DOI: 10.1016/j.jpowsour.2018.03.015
OpenAlex: W2793702125
Cited by: 765
References: 33
Citations by year: 2026: 65 · 2025: 101 · 2024: 124 · 2023: 144 · 2022: 116 · 2021: 98 · 2020: 71 · 2019: 35 · 2018: 11
FWCI: 28.54
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Journal of Power Sources (publishedVersion)
Funders: National Natural Science Fund of China (61375079)
CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellow (2017LH007)
Topics: Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Keywords: state of health estimation, lithium-ion batteries, Gaussian process regression, charging curves, battery degradation, grey relational analysis

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A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

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