Lithium-sulfur cell state of charge estimation using a classification technique

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dc.contributor.authorShateri, Neda
dc.contributor.authorShi, Zhihao
dc.contributor.authorAuger, Daniel J.
dc.contributor.authorFotouhi, Abbas
dc.date.accessioned2021-02-18T11:50:18Z
dc.date.available2021-02-18T11:50:18Z
dc.date.issued2020-12-16
dc.description.abstractLithium-Sulfur (Li-S) batteries are a promising next-generation technology providing high gravimetric energy density compared to existing lithium-ion (Li-ion) technologies in the market. The literature shows that in Li-S, estimation of state of charge (SoC) is a demanding task, in particular due to a large flat section in the voltage-SoC curve. This study proposes a new SoC estimator using an online parameter identification method in conjunction with a classification technique. This study investigates a new prototype Li-S cell. Experimental characterization tests are conducted under various conditions; the duty cycle – intended to represent a real-world application – is based on an electric city bus. The characterization results are then used to parameterize an equivalent-circuit-network (ECN) model, which is then used to relate real-time parameter estimates derived using a Recursive Least Squares (RLS) algorithm to state of charge using a Support Vector Machine (SVM) classifier to estimate an approximate SoC range. The estimate is used together with a conventional coulomb-counting technique to achieve continuous SoC estimation in real-time. It is shown that this method can provide an acceptable level of accuracy with less than 3% error under realistic driving conditions.en_UK
dc.identifier.citationShateri N, Shi Z, Auger DJ, Fotouhi A. (2021) Lithium-sulfur cell state of charge estimation using a classification technique. IEEE Transactions on Vehicular Technology, Volume 70, Issue 1, January 2021, pp. 212-224en_UK
dc.identifier.issn0018-9545
dc.identifier.urihttps://doi.org/ 10.1109/TVT.2020.3045213
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16371
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectSVM Classifieren_UK
dc.subjectState of Charge Estimationen_UK
dc.subjectParameter Identificationen_UK
dc.subjectLithium-Sulfur Batteryen_UK
dc.titleLithium-sulfur cell state of charge estimation using a classification techniqueen_UK
dc.typeArticleen_UK

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