Electrical Method for Battery Chemical Composition Determination

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dc.authoridDikmen, İsmail Can/0000-0002-7747-7777
dc.authorwosidDikmen, İsmail Can/H-6927-2018
dc.contributor.authorDikmen, Ismail Can
dc.contributor.authorKaradag, Teoman
dc.date.accessioned2024-08-04T20:51:36Z
dc.date.available2024-08-04T20:51:36Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractStorage of electrical energy is one of the most important technical problems in terms of today's technology. The increasing number of high-capacity high-power applications, especially electric vehicles and grid scale energy storage, points to the fact that we will be faced with a large number of batteries that will need to be recycled and separated in the near future. Additionally multi-chemistry battery management systems that enables the collective use of superior features of different batteries with different chemical composition. Here, battery chemical composition determination emerges as a technical problem. In this study, an alternative method to the currently used methods for categorizing batteries according to their chemistry is discussed. As the foundation, batteries with four different chemical composition including Lithium Nickel Cobalt Aluminium Oxide, Lithium Iron Phosphate, Nickel Metal Hydride, and Lithium Titanate Oxide aged with a battery testing hardware. Fifth, is Lithium Sulphur battery which is simulated. Brand new and aged batteries are used in experimental setup that is consist of a programmable electronic DC load and a software developed to run the algorithm on it. According to the algorithm, batteries are connected to two different loads one by one and voltage-current data are stored. Collected data are pre-processed by framing them and framed data are processed with a separation function. Eventually, the determination problem is converted to a classification problem. In order to solve this, artificial neural network and classification tree algorithms are applied. Because the artificial neural network algorithm is applied in previous studies and the high computational cost of it is presented; classification tree algorithm is concluded to be more applicable especially on low-power microcontroller applications. Consequently, 100% accuracy for battery chemical composition determination is achieved and results are presented comparatively.en_US
dc.description.sponsorshipScienti~c and Technological Research Council of Turkey (TUBITAK) [2170454]; Research Fund of the Inonu University [FOA-2018-1358]en_US
dc.description.sponsorshipThis work was supported in part by the Scienti~c and Technological Research Council of Turkey (TUBITAK) under Grant 2170454, and in part by the Research Fund of the Inonu University under Project FOA-2018-1358.en_US
dc.identifier.doi10.1109/ACCESS.2022.3143040
dc.identifier.endpage6504en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85123298747en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage6496en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2022.3143040
dc.identifier.urihttps://hdl.handle.net/11616/100432
dc.identifier.volume10en_US
dc.identifier.wosWOS:000745497000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBatteriesen_US
dc.subjectChemistryen_US
dc.subjectChemicalsen_US
dc.subjectClassification algorithmsen_US
dc.subjectLithiumen_US
dc.subjectVoltageen_US
dc.subjectSoftwareen_US
dc.subjectBatteriesen_US
dc.subjectbattery management systemen_US
dc.subjectclassification algorithmsen_US
dc.subjectelectric vehicleen_US
dc.subjectmachine learningen_US
dc.subjectoptimisationen_US
dc.subjectrecyclingen_US
dc.titleElectrical Method for Battery Chemical Composition Determinationen_US
dc.typeArticleen_US

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