State of charge estimation of lithium sulfur batteries using sliding mode observer

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dc.contributor.authorMunisamy, Srinivasan
dc.contributor.authorWu, Wenxuan
dc.date.accessioned2022-08-08T15:49:59Z
dc.date.available2022-08-08T15:49:59Z
dc.date.issued2022-07-21
dc.description.abstractThe lithium-sulfur (Li-S) batteries are high energy storage systems that can be used for electric grid and solar power air vehicle applications. Such applications require an accurate state of charge (SOC) estimator to control and optimize battery performance. Modelling and estimation of discharging Li-S are highly challenging than other batteries as the discharge voltage of Li-S batteries has highly nonlinear and typical characteristics than the Lithium-Ion batteries. For Li-S battery SOC estimation, literature has proposed filters and machine learning techniques, but no literature on sliding mode observer (SMO). This paper presents the SMO for discharging Li-S SOC estimation and compares it to the extended Kalman filter (EKF). Both estimators use a first-order equivalent circuit network (ECN) model of Li-S cell parameters given in the literature. The performance of such ECN model based SOC estimators influenced by the Q- uncertainty, which is a perturbation in the form of process noise state-space model. Therefore, this work studies an optimal trade-off characteristic of SMO and EKF over the Q-uncertainty. With constant and mixed-amplitude pulse load current sequences, numerical simulation has performed. Simulation results illustrate that the SMO is optimal, converges to the true SOC than the EKF when the perturbation increased.en_UK
dc.identifier.citationMunisamy S, Xu W. (2022) State of charge estimation of lithium sulfur batteries using sliding mode observer. In: 2022 IEEE 7th International Energy Conference (ENERGYCON), 9-12 May 2022, Riga, Latviaen_UK
dc.identifier.eisbn978-1-6654-7982-0
dc.identifier.isbn978-1-6654-7983-7
dc.identifier.urihttps://doi.org/10.1109/ENERGYCON53164.2022.9830474
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18292
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectBattery energy storageen_UK
dc.subjectnonlinear filtersen_UK
dc.subjectLithium-sulfur batteriesen_UK
dc.subjectstate of chargeen_UK
dc.subjectsliding mode observeren_UK
dc.titleState of charge estimation of lithium sulfur batteries using sliding mode observeren_UK
dc.typeConference paperen_UK

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