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dc.contributor.authorRobayo, M
dc.contributor.authorMueller, M
dc.contributor.authorSharkh, S
dc.contributor.authorAbusara, M
dc.date.accessioned2022-11-24T14:13:21Z
dc.date.issued2022-11-29
dc.date.updated2022-11-24T13:46:53Z
dc.description.abstractWhen battery and supercapacitor (SC) Energy Storage Systems (ESSs) coexist in electric vehicles, energy management is imperative to ensure efficient power distribution based on the strengths and weaknesses of each ESS. The decoupling of highly dynamic power demands into components that match the dynamic nature of each ESS is essential. The Discrete Wavelet Transform (DWT) has been widely recommended for this purpose as part of real time energy management systems. However, due to DWT signal processing, delays in the frequency components can undermine the benefits of hybridization. This paper analyses the contribution of the SC to alleviate the battery when the DWT is used with and without time delay compensation using future demand prediction. Four different implementation strategies for a DWT based EMS have been evaluated using different metrics to quantify energy circulation and SC assistance during acceleration and braking. Simulation results using urban and highway driving cycles, show that obtaining the SC current reference as the difference between the real time current demand and the DWT low frequency component enhances SC assistance during acceleration and braking at the expense of higher energy circulation. The complexity added by future demand prediction does not reap SC performance benefits.en_GB
dc.identifier.citationVol. 57, article 106200en_GB
dc.identifier.doi10.1016/j.est.2022.106200
dc.identifier.urihttp://hdl.handle.net/10871/131846
dc.identifierORCID: 0000-0002-4195-5079 (Abusara, Mohammad)
dc.language.isoenen_GB
dc.publisherElsevieren_GB
dc.rights© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en_GB
dc.subjectHybrid energy storageen_GB
dc.subjectDiscrete wavelet transformen_GB
dc.subjectElectric vehicleen_GB
dc.subjectEnergy management systemen_GB
dc.subjectLong-short term memory neural networken_GB
dc.titleAssessment of supercapacitor performance in a hybrid energy storage system with an EMS based on the discrete wavelet transformen_GB
dc.typeArticleen_GB
dc.date.available2022-11-24T14:13:21Z
dc.identifier.issn2352-152X
dc.descriptionThis is the final version. Available on open access from Elsevier via the DOI in this recorden_GB
dc.identifier.journalJournal of Energy Storageen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2022-11-19
dcterms.dateSubmitted2022-03-30
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2022-11-19
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2022-11-24T13:46:55Z
refterms.versionFCDAM
refterms.dateFOA2022-11-24T14:13:24Z
refterms.panelBen_GB


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© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's licence is described as © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).