Onboard Battery Type Determination

dc.authorscopusid57160871500
dc.authorscopusid36092815900
dc.contributor.authorDikmen I.C.
dc.contributor.authorKaradag T.
dc.date.accessioned2024-08-04T20:04:00Z
dc.date.available2024-08-04T20:04:00Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description5h International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2021 -- 21 October 2021 through 23 October 2021 -- 174473en_US
dc.description.abstractBattery type determination can be crucial in some cases such as multi-chemistry battery management systems, second life applications or recycling. The determination process is a challenging problem because voltage readings of batteries with different chemistries would be very close and overlap at certain points depending on their state of charge and state of health. In order to overcome this issue, a method proposed in this study. This method consist of three steps. Step one is data acquisition by measuring terminal voltages and corresponding instant currents under switching loads without relaxation. Step two is merging the data into frames and preprocessing it with the developed separation function based on statistical significance. Step three is training the artificial neural network which is designed to run on a microcontroller. Three types of batteries with different chemical compositions were used for this purpose. These types are the ones that first generation of electric vehicles on the market were commonly equipped. Experimental data acquired for all batteries under varying pulsed load, and statistical significance test, tTest has performed on the data in binary combinations. Here voltage data of LiFePO4 and NCR batteries has found statistically significant. Correspondingly, a separation function has developed for the separation of overlapping data. The preprocessed data with the proposed separation function has used to train an artificial neural network. Results show that, preprocessing the data results to 100% accuracy of battery type determination even on a tiny neural network. © 2021 IEEE.en_US
dc.description.sponsorship2170454; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK; Inönü Üniversitesi: 2021/005464, FOA-2018-1358en_US
dc.description.sponsorshipThis study was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) with grant number 2170454 and Research Fund of the Inonu University with project number FOA-2018-1358. Patent pending 2021/005464.en_US
dc.identifier.doi10.1109/ISMSIT52890.2021.9604658
dc.identifier.endpage365en_US
dc.identifier.isbn9781665449304
dc.identifier.scopus2-s2.0-85123312616en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage360en_US
dc.identifier.urihttps://doi.org/10.1109/ISMSIT52890.2021.9604658
dc.identifier.urihttps://hdl.handle.net/11616/92261
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofISMSIT 2021 - 5th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectbattery management systemen_US
dc.subjectbattery recyclingen_US
dc.subjectbattery second lifeen_US
dc.subjectBattery type determinationen_US
dc.subjectmulti-chemistry BMSen_US
dc.titleOnboard Battery Type Determinationen_US
dc.typeConference Objecten_US

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