Prediction of experimental thermal performance of new designed cold plate for electric vehicles' Li-ion pouch-type battery with artificial neural network

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dc.authoridKalkan, Orhan/0000-0002-9664-1819
dc.authoridDalkilic, Ahmet Selim/0000-0002-5743-3937
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.contributor.authorKalkan, Orhan
dc.contributor.authorColak, Andac Batur
dc.contributor.authorCelen, Ali
dc.contributor.authorBakirci, Kadir
dc.contributor.authorDalkilic, Ahmet Selim
dc.date.accessioned2024-11-07T13:34:15Z
dc.date.available2024-11-07T13:34:15Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractSince liquid-based thermal management systems are usually preferred methods for battery electric vehicles and cold plates are generally preferred to circulate the coolant, studies on their design are becoming increasingly essential. Besides, it seems useful to work artificial intelligence approaches to evaluate different battery thermal management systems, as it is known that the use of artificial intelligence is increasing in many applications today. The aim of this paper is to build up an artificial neural network model due to predict average battery temperature and maximum temperature difference on the battery surface which are also the artificial neural network outputs. The model inputs are depth of discharge, coolant flow rate (0.1, 0.6 and 1.1 l/min), discharge rate (1C- 5C), coolant inlet temperature (15, 25 and 35 degrees C). It is developed for a serpentine tubed cold plate, and mini channeled one which has novel design. To shorten the training time, after the optimization of the data set, a total of 270 data sets were utilized for training, validation, and test phases. In addition, the developed model predicts successfully average battery temperature and maximum temperature difference on the battery surface in the 10% error band range. Finally, the maximum margin of deviation and R values are 7.3% and 0.997%, respectively.
dc.description.sponsorshipResearch Fund of the Erzincan Binali Yil-dirim University [FBA-2019-657]
dc.description.sponsorshipThis work was funded by Research Fund of the Erzincan Binali Yil-dirim University. Project No.: FBA-2019-657.
dc.identifier.doi10.1016/j.est.2022.103981
dc.identifier.issn2352-152X
dc.identifier.issn2352-1538
dc.identifier.scopus2-s2.0-85122627151
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.est.2022.103981
dc.identifier.urihttps://hdl.handle.net/11480/15861
dc.identifier.volume48
dc.identifier.wosWOS:000780239600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of Energy Storage
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectLi -ion battery
dc.subjectCold plate design
dc.subjectMini channel
dc.subjectCooling
dc.subjectArtificial neural network
dc.titlePrediction of experimental thermal performance of new designed cold plate for electric vehicles' Li-ion pouch-type battery with artificial neural network
dc.typeArticle

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