Improved state of charge estimation for lithium-sulfur batteries

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dc.contributor.authorPropp, Karsten
dc.contributor.authorAuger, Daniel J.
dc.contributor.authorFotouhi, Abbas
dc.contributor.authorMarinescu, Monica
dc.contributor.authorKnap, Vaclav
dc.contributor.authorLongo, Stefano
dc.date.accessioned2019-10-24T11:01:22Z
dc.date.available2019-10-24T11:01:22Z
dc.date.issued2019-10-23
dc.description.abstractGood state of charge estimation in lithium-sulfur batteries (Li-S) is vital, as the simplest convention methods commonly used in lithium-ion batteries – open-circuit voltage measurement and ‘coulomb counting’–are often ineffective for Li-S. Since Li-S is an ewbattery chemistry, there are few published techniques. Existing techniques based on the extended Kalman filter and the unscented Kalman filter have shown some promise, existing work has explored only one of many possible estimator architectures: a single filter based on a pre-calibrated behavioural reparameterization of an equivalent circuit network whose parameters vary as a function of state of charge and temperature. Such filters have been shown to be reasonably effective in practical cases, but they can converge slowly if initial conditions are unknown, and they can become inaccurate with changes in current density. It is desirable to understand whether other possible estimator architectures offer improved performance. One such alternative architecture is the ‘dual extended Kalman filter’, which uses voltage and current measurements to estimate into a short-term dynamic circuit parameters then uses the outputs of this in a slower acting state-of-charge estimator. This paper develops a ‘behavioural’ form of the dual extended Kalman filter, and applies this to a lithium-sulfur battery. The estimator is adapted with a term to model circuit current dependence, and demonstrated using pulse-discharge tests and scaled automotive driving cycles including some with initially partially discharged batteries. Compared to the published state-of-the-art, the new estimators were are found to be between 16.4% and 28.2% more accurate for batteries that are initially partially discharged to a 60% SoC level; the new estimators also converge faster. The resulting estimators have the potential to be extended to state-of-health measures, and the ‘behavioural’ circuit reparameterization is likely to be of use for other battery chemistries beside lithium-sulfur.en_UK
dc.identifier.citationPropp K, Auger DJ, Fotouhi A, et al., (2019) Improved state of charge estimation for lithium-sulfur batteries. Journal of Energy Storage, Volume 26, December 2019, Article number 100943en_UK
dc.identifier.cris24868208
dc.identifier.issn2352-152X
dc.identifier.urihttps://doi.org/10.1016/j.est.2019.100943
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/14636
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectLithium-sulfur batteryen_UK
dc.subjectState of charge estimationen_UK
dc.subjectExtended Kalman filteren_UK
dc.subjectEquivalent circuit network modeen_UK
dc.titleImproved state of charge estimation for lithium-sulfur batteriesen_UK
dc.typeArticleen_UK

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