Fotouhi, AbbasAuger, Daniel J.Propp, KarstenLongo, Stefano2017-11-202017-12-142017-09-28Fotouhi A, Auger D, Propp K, Longo S. (2018) Lithium-sulfur battery state-of-charge observability analysis and estimation. IEEE Transactions on Power Electronics, Volume 33, Issue 7, July 2018, pp. 5847-58590885-8993http://dx.doi.org/10.1109/TPEL.2017.2740223https://dspace.lib.cranfield.ac.uk/handle/1826/12752Lithium-Sulfur (Li-S) battery technology is considered for an application in an electric vehicle energy storage system in this study. A new type of Li-S cell is tested by applying load current and measuring cell's terminal voltage in order to parameterize an equivalent circuit network model. Having the cell's model, the possibility of state-of-charge (SOC) estimation is assessed by performing an observability analysis. The results demonstrate that the Li-S cell model is not fully observable because of the particular shape of cell's open-circuit voltage curve. This feature distinguishes Li-S batteries from many other types of battery, e.g. Li-ion and NiMH. As a consequence, a Li-S cell's SOC cannot be estimated using existing methods in the literature and special considerations are needed. To solve this problem, a new framework is proposed consisting of online battery parameter identification in conjunction with an estimator that is trained to use the identified parameters to predict SOC. The identification part is based on the well-known Prediction-Error Minimization (PEM) algorithm; and the SOC estimator part is an Adaptive Neuro-Fuzzy Inference System (ANFIS) in combination with coulomb counting. Using the proposed method, a Li-S cell's SOC is estimated with a mean error of 4% and maximum error of 7% in a realistic driving scenario.enAttribution 3.0 Internationalhttp://creativecommons.org/licenses/by/3.0/Lithium-sulfur batteryModel identificationObservability analysisState of charge estimationAdaptive neuro-fuzzy inference systemArticle18239131