Cited by 144Battery Modeling and ManagementElectric and Hybrid Vehicle TechnologiesControl Systems and Identification

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Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering

Hu, Lin, Hu, Xiaosong, Che, Yunhong, Feng, Fei, Lin, Xianke, Zhang, Zhiyong·2020·Applied Energy

DOI 10.1016/j.apenergy.2020.114569

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Authors: Hu, L., Hu, X., Che, Y., Feng, F., Lin, X., & Zhang, Z.
Affiliations: Chongqing University (CN)
Ontario Tech University (CA)
Changsha University of Science and Technology (CN)
Corresponding author: Hu, Lin
Hu, Xiaosong
Feng, Fei
ORCID: Hu, Lin
Che, Yunhong
Feng, Fei
Lin, Xianke
Zhang, Zhiyong
Venue: Applied Energy
Publisher: Elsevier BV
Published: 2020-03
Volume: 262
Pages: 114569
ISSN: 0306-2619 (ISSN-L)
DOI: 10.1016/j.apenergy.2020.114569
OpenAlex: W3005296994
Cited by: 144
References: 44
Citations by year: 2026: 6 · 2025: 18 · 2024: 12 · 2023: 16 · 2022: 34 · 2021: 41 · 2020: 17
FWCI: 9.99
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Applied Energy (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: National Natural Science Foundation of China
Topics: Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Keywords: state of charge estimation, battery pack, series-connected batteries, cell inconsistency, fusion weights, measurement error, fault tolerance, electric vehicles

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Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering

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