Shateri, NedaShi, ZhihaoAuger, Daniel J.Fotouhi, Abbas2021-02-182021-02-182020-12-16Shateri 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. 212-2240018-9545https://doi.org/ 10.1109/TVT.2020.3045213https://dspace.lib.cranfield.ac.uk/handle/1826/16371Lithium-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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/SVM ClassifierState of Charge EstimationParameter IdentificationLithium-Sulfur BatteryArticle