Lithium-sulfur cell equivalent circuit network model parameterization and sensitivity analysis

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dc.contributor.authorFotouhi, Abbas
dc.contributor.authorAuger, Daniel J.
dc.contributor.authorPropp, Karsten
dc.contributor.authorLongo, Stefano
dc.contributor.authorPurkayastha, Rajlakshmi
dc.contributor.authorO'Neill, Laura
dc.contributor.authorWalus, Sylwia
dc.date.accessioned2017-05-09T11:59:38Z
dc.date.available2017-05-09T11:59:38Z
dc.date.issued2017-04-18
dc.description.abstractCompared to lithium-ion batteries, lithium-sulfur (Li-S) batteries potentially offer greater specific energy density, a wider temperature range of operation, and safety benefits, making them a promising technology for energy storage systems especially in automotive and aerospace applications. Unlike lithium-ion batteries, there is not a mature discipline of equivalent circuit network (ECN) modelling for Li-S. In this study, ECN modelling is addressed using formal ‘system identification’ techniques. A Li-S cell’s performance is studied in the presence of different charge/discharge rates and temperature levels using precise experimental test equipment. Various ECN model structures are explored, considering the trade-offs between accuracy and speed. It was concluded that a ‘2RC’ model is generally a good compromise, giving good accuracy and speed. Model parameterization is repeated at various state-of-charge (SOC) and temperature levels, and the effects of these variables on Li-S cell’s ohmic resistance and total capacity are demonstrated. The results demonstrate that Li-S cell’s ohmic resistance has a highly nonlinear relationship with SOC with a break-point around 75% SOC that distinguishes it from other types of battery. Finally, an ECN model is proposed which uses SOC and temperature as inputs. A sensitivity analysis is performed to investigate the effect of SOC estimation error on the model’s accuracy. In this analysis, the battery model’s accuracy is evaluated at various SOC and temperature levels. The results demonstrate that the Li-S cell model has the most sensitivity to SOC estimation error around the break-point (around 75% SOC) whereas in the middle SOC range, from 20% to 70%, it has the least sensitivity.en_UK
dc.identifier.citationFotouhi A, Auger DJ, Propp K, et al., (2017) Lithium-Sulfur cell equivalent circuit network model parameterization and sensitivity analysis. IEEE Transactions on Vehicular Technology, Volume 66, Issue 9, September 2017, pp. 7711-7721en_UK
dc.identifier.cris17493478
dc.identifier.issn0018-9545
dc.identifier.urihttp://dx.doi.org/10.1109/TVT.2017.2678278
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/11864
dc.language.isoenen_UK
dc.publisherInstitute of Electrical and Electronics Engineersen_UK
dc.rightsAttribution 3.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/
dc.subjectlithium-sulfur cellen_UK
dc.subjectbattery modellingen_UK
dc.subjectidentificationen_UK
dc.subjectstate-of-charge estimationen_UK
dc.subjectsensitivity analysisen_UK
dc.titleLithium-sulfur cell equivalent circuit network model parameterization and sensitivity analysisen_UK
dc.typeArticleen_UK

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