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Cited by 26Advanced Battery Technologies ResearchAdvancements in Battery MaterialsControl Systems and Identification

State of Charge Estimation and Evaluation of Lithium Battery Using Kalman Filter Algorithms

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Hu, Longzhou, Hu, Rong, Ma, Zengsheng, Jiang, Wenjuan·2022·Materials
DOI 10.3390/ma15248744PMC PMC9785816
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Abstract

The accurate and rapid estimation of the state of charge (SOC) is important and difficult in lithium battery management systems. In this paper, an adaptive infinite Kalman filter (AUKF) was used to estimate the state of charge for a 18650 LiNiMnCoO2/graphite lithium-ion battery, and its performance was systematically evaluated under large initial errors, wide temperature ranges, and different drive cycles. In addition, three other Kalman filter algorithms on the predicted SOC of LIB were compared under different work conditions, and the accuracy and convergence time of different models were compared. The results showed that the convergence time of the AUKF algorithms was one order of magnitude smaller than that of the other three methods, and the mean absolute error was only less than 50% of the other methods. The present work can be used to help other researchers select an appropriate strategy for the SOC online estimation of lithium-ion cells under different applicable conditions.

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Authors
Hu, L., Hu, R., Ma, Z., & Jiang, W.
Affiliations
Xiangtan University (CN)
Corresponding author
  • Ma, Zengsheng
  • Jiang, Wenjuan
ORCID
Venue
Materials
Publisher
MDPI AG
Published
2022-12-07
Volume
15
Issue
24
Pages
8744
ISSN
1996-1944 (ISSN-L)
DOI
10.3390/ma15248744
PMC
PMC9785816
OpenAlex
W4311858525
Cited by
26
References
2
Citations by year
2026: 6 · 2025: 7 · 2024: 6 · 2023: 7
FWCI
1.36
Type
journal article
Language
en
Open access
Licence
Locations
  • Materials (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Education Department of Hunan Province (18B080, 2020JJ2026, 2021JJ30643, 2018RS3091, QL20220138, XDCX2022L011)
  • the Natural Science Foundation of Hunan Province (18B080, 2020JJ2026, 2021JJ30643, 2018RS3091, QL20220138, XDCX2022L011)
  • Science and Technology Innovation Project of Hunan Province (18B080, 2020JJ2026, 2021JJ30643, 2018RS3091, QL20220138, XDCX2022L011)
  • Graduate Innovation Projects of Hunan Province (18B080, 2020JJ2026, 2021JJ30643, 2018RS3091, QL20220138, XDCX2022L011)
  • the Graduate Innovation Project of Xiangtan University (18B080, 2020JJ2026, 2021JJ30643, 2018RS3091, QL20220138, XDCX2022L011)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
  • 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 charge estimation, lithium-ion batteries, adaptive unscented Kalman filter, battery management systems, 18650 cells, temperature effects, driving cycles, convergence time

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