Comparative investigation of the usability of different machine learning algorithms in the analysis of battery thermal performances of electric vehicles

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dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.contributor.authorColak, Andac Batur
dc.date.accessioned2024-11-07T13:24:58Z
dc.date.available2024-11-07T13:24:58Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThe cooling and thermal management of battery packs, which are the most important components used in electric vehicles (EV), are of critical importance for the efficiency and performance of EV. This study aims to analyze the usability of machine learning algorithms in determining the thermal parameters of the battery thermal management system (BTMS) used in EV and to determine the machine learning algorithm with the highest prediction performance. The prediction performance of three different artificial neural networks developed by using Levenberg-Marquardt, Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) machine learning algorithms have been extensively and comparatively investigated. As input parameters of the models, discharge rate, flow rate, and inlet temperature values were defined and the average temperature of the battery surface and maximum temperature difference on the surface values were estimated. The coefficient of determination values for the Levenberg-Marquardt, BR, and SCG algorithms was calculated as 0.99848, 0.98751, and 0.97592, respectively. The results showed that the machine learning algorithms can determine the thermal parameters of the BTMS of EV with high accuracy. However, it has been observed that the highest prediction accuracy belongs to the Levenberg-Marquardt algorithm.
dc.identifier.doi10.1002/er.8492
dc.identifier.endpage21126
dc.identifier.issn0363-907X
dc.identifier.issn1099-114X
dc.identifier.issue15
dc.identifier.scopus2-s2.0-85135513403
dc.identifier.scopusqualityQ1
dc.identifier.startpage21104
dc.identifier.urihttps://doi.org/10.1002/er.8492
dc.identifier.urihttps://hdl.handle.net/11480/14437
dc.identifier.volume46
dc.identifier.wosWOS:000837564200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley-Hindawi
dc.relation.ispartofInternational Journal of Energy Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectartificial neural network
dc.subjectbattery
dc.subjectbattery thermal management system
dc.subjectelectric vehicles
dc.subjectmachine learning algorithms
dc.titleComparative investigation of the usability of different machine learning algorithms in the analysis of battery thermal performances of electric vehicles
dc.typeArticle

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