A likelihood-partitioned Bayesian framework for lithium sulfur battery state discharging of charge estimation

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dc.contributor.authorMunisamy, Srinivasan
dc.date.accessioned2022-07-06T08:59:10Z
dc.date.available2022-07-06T08:59:10Z
dc.date.issued2022-04-14
dc.description.abstractLithium sulfur (Li-S) batteries are promising energy storage devices and alternative to lithium-Ion (Li-Ion) batteries in electric grid and vehicle applications. However, compared to Li-Ion, the discharge voltage of Li-S is much complex and nonlinear. This results a challenging state of charge (SoC) estimation problem while Li-S is discharging. For such a problem, the traditional extended Kalman filter fails to provide accurate SoC. Therefore, this paper proposes a novel likelihood partitioned Bayesian filtering (LPBF) framework and its linearized version for SoC estimation of discharging Li-S battery cell. Though both traditional EKF and linearized LPBF use a prediction error minimization based equivalent circuit network (ECN) parameterization, the LPBF uses a partitioned ECN parameterization. The portioned models result two likelihoods, whereas the EKF uses a single state-space model throughout discharge from 100 percent SoC to zero SoC. With experiment data obtained at two different temperature conditions, numerical simulation results compare both EKF and linearized LPBF based SoC estimators. Simulation results show that the LPBF's accuracy is impressive, about 97 percent, for considered dynamic load current, operating temperature and uncertain initial SoC conditions.en_UK
dc.description.sponsorshipEuropean Union funding: 814471en_UK
dc.identifier.citationMunisamy S. (2022) A likelihood-partitioned Bayesian framework for lithium sulfur battery state discharging of charge estimation. In: 2022 IEEE Texas Power and Energy Conference (TPEC), 28 February - 1 March 2022, College Station, Texasen_UK
dc.identifier.eisbn978-1-6654-7902-8
dc.identifier.isbn978-1-6654-7903-5
dc.identifier.urihttps://doi.org/10.1109/TPEC54980.2022.9750711
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18119
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 management systemsen_UK
dc.subjectBayesian filtersen_UK
dc.subjectLithium-sulfur batteryen_UK
dc.subjectLikelihood probability densityen_UK
dc.subjectNonlinear functionsen_UK
dc.subjectState of chargeen_UK
dc.titleA likelihood-partitioned Bayesian framework for lithium sulfur battery state discharging of charge estimationen_UK
dc.typeConference paperen_UK

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