A novel approach to estimate the state of charge for lithium-ion battery under different temperatures incorporating open circuit voltage online identification
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Adaptive filtering is an improved form of traditional filtering where model parameters adjust over time due to changes in battery operating conditions.
“Adaptive filtering stands as an improved form of traditional filtering like Kalman filtering whereby the model parameters are adjusted with time due to changes in the battery operating conditions [73].”
From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [73]
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- Authors
- Renxin, X., Yanwen, H., Wei, Z., & Zhaohui, C.
- Affiliations
- Kunming University of Science and Technology (CN)
- ORCID
- Yanwen, Hu
- Venue
- Journal of Energy Storage
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 67
- Pages
- 107509
- ISSN
- 2352-152X (ISSN-L)
- OpenAlex
- W4372329706
- Cited by
- 61
- References
- 59
- Citations by year
- 2026: 14 · 2025: 24 · 2024: 14 · 2023: 9
- FWCI
- 4.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Energy Storage (publishedVersion)
- Funders
- Ten Thousand Talent Plans for Young Top-notch Talents of Yunnan Province (KKRD201902062)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Keywords
- lithium-ion batteries, state of charge estimation, open-circuit voltage, temperature dependence, Thevenin equivalent circuit model, parameter estimation, adaptive recursive least squares, forgetting factor, adaptive unscented Kalman filter, closed-loop estimation, online identification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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