Publication:
Prediction and optimization of the design decisions of liquid cooling systems of battery modules using artificial neural networks

dc.contributor.authorBulut, Emre
dc.contributor.authorAlbak, Emre İsa
dc.contributor.authorSevilgen, Gökhan
dc.contributor.authorÖztürk, Ferruh
dc.contributor.buuauthorBULUT, EMRE
dc.contributor.buuauthorALBAK, EMRE İSA
dc.contributor.buuauthorSEVİLGEN, GÖKHAN
dc.contributor.buuauthorÖZTÜRK, FERRUH
dc.contributor.departmentBursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü.
dc.contributor.departmentBursa Uludağ Üniversitesi/Gemlik Asım Kocabıyık Meslek Yüksekokulu/Hibrit ve Elektrikli Araç Teknolojisi.
dc.contributor.orcid0000-0001-9159-5000
dc.contributor.orcid0000-0001-9215-0775
dc.contributor.orcid0000-0002-7746-2014
dc.contributor.researcheridI-9483-2017
dc.contributor.researcheridABG-3444-2020
dc.contributor.researcheridAAG-8907-2021
dc.contributor.researcheridFRD-1816-2022
dc.date.accessioned2024-10-24T10:04:13Z
dc.date.available2024-10-24T10:04:13Z
dc.date.issued2022-01-03
dc.description.abstractLiquid cooling systems are effective for keeping the battery modules in the safe temperature range. This study focuses on decreasing the power consumption of the pump without compromising the cooling performance. Artificial neural network (ANN) models are created to predict the effects of the height and width of the cooling channel and the mass flow rate on the maximum temperature, convective heat transfer coefficient, and pressure drop. The ANN models are used as surrogate models for the design and optimization of the liquid cooling battery system. Particle swarm optimization (PSO) and genetic algorithm (GA), which are commonly utilized optimization methods in many areas, and chaos game optimization (CGO) and coot optimization algorithm (COOT) methods, which are recently presented methods, are adopted to minimize the power consumption of the pump. The results are compared in terms of computational performance and best, worst, average, and SD values. Despite all of the optimization methods used giving similar results, the CGO method comes forward due to fast converging, SD, and finding the minimum power consumption of the pump among other optimization methods. A 22.4% decrease in the power consumption of the pump is achieved with the use of the ANN-based CGO method while conserving the cooling performance. When comparing the ANN predicted and CFD results, the relative errors are less than 1%.
dc.identifier.doi10.1002/er.7637
dc.identifier.endpage7308
dc.identifier.issn0363-907X
dc.identifier.issue6
dc.identifier.startpage7293
dc.identifier.urihttps://doi.org/10.1002/er.7637
dc.identifier.urihttps://onlinelibrary.wiley.com/doi/10.1002/er.7637
dc.identifier.urihttps://hdl.handle.net/11452/47006
dc.identifier.volume46
dc.identifier.wos000737546000001
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherWiley-Hindawi
dc.relation.journalInternational Journal of Energy Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectLithium-ion battery
dc.subjectPlate
dc.subjectPower
dc.subjectArtificial neural network
dc.subjectBattery thermal modeling
dc.subjectCfd
dc.subjectCooling plate
dc.subjectLiquid cooling
dc.subjectOptimization
dc.subjectSerpentine channel
dc.subjectEnergy & fuels
dc.subjectNuclear science & technology
dc.titlePrediction and optimization of the design decisions of liquid cooling systems of battery modules using artificial neural networks
dc.typeArticle
dspace.entity.typePublication
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relation.isAuthorOfPublication975d5454-a37e-43a5-a932-2de51b928419
relation.isAuthorOfPublication407521cf-c5bd-4b05-afca-6412ef47700b
relation.isAuthorOfPublication.latestForDiscoveryf40336d8-7dee-4bc0-b37a-c7f07578c139

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