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Impact of hybrid and mono nanofluids on the cooling performance of lithium-ion batteries: Experimental and machine learning insights

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dc.citedby2
dc.contributor.authorKanti P.K.en_US
dc.contributor.authorYang E.S.J.en_US
dc.contributor.authorWanatasanappan V.V.en_US
dc.contributor.authorSharma P.en_US
dc.contributor.authorSaid N.M.en_US
dc.contributor.authorid57216493630en_US
dc.contributor.authorid59329587500en_US
dc.contributor.authorid57217224948en_US
dc.contributor.authorid58961316700en_US
dc.contributor.authorid57217198447en_US
dc.date.accessioned2025-03-03T07:41:38Z
dc.date.available2025-03-03T07:41:38Z
dc.date.issued2024
dc.description.abstractThe present study investigates the preparation and application of mono and hybrid nanofluids to enhance the cooling performance of 18,650 lithium-ion batteries. Researchers dispersed Al2O3 and CuO nanoparticles in water at a volume concentration of 0.5 % to create these advanced coolants. The experimental setup evaluated battery cooling efficiency under diverse conditions, including varying coolant types, flow rates (150, 250, and 350 ml/min), and battery discharge rates (0.5 and 1C). Al2O3-CuO hybrid nanofluids exhibited superior thermal conductivity, surpassing CuO and Al2O3 mono nanofluids by 35.26 % and 29.1 %, respectively at 60 �C. Notably, the 0.5 % of Al2O3-CuO nanofluid achieved a remarkable 54.23 % reduction in lithium-ion battery cell temperature at a flow rate of 350 ml/min, compared to water alone. These findings highlight the promising potential of hybrid nanofluids as effective working fluids in thermal management systems for lithium-ion battery cells. Following the identification of the optimal nanofluid, researchers developed prediction models using machine-learning techniques. The random forest approach was employed, with linear regression serving as a baseline for comparison. The RF-based model demonstrated exceptional predictive accuracy, achieving 98.4 % accuracy compared to the LR model's 80.82 %, while maintaining minimal prediction errors. ? 2024 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo113613
dc.identifier.doi10.1016/j.est.2024.113613
dc.identifier.scopus2-s2.0-85204049076
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85204049076&doi=10.1016%2fj.est.2024.113613&partnerID=40&md5=d00bb8e6426999b1f0b2b903685b0967
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36228
dc.identifier.volume101
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Energy Storage
dc.subjectBattery cells
dc.subjectCooling performance
dc.subjectDischarge rates
dc.subjectExperimental learning
dc.subjectHybrid nanofluid
dc.subjectIon batteries
dc.subjectLithium ions
dc.subjectNanofluids
dc.subjectSoft-Computing
dc.subjectThermal
dc.titleImpact of hybrid and mono nanofluids on the cooling performance of lithium-ion batteries: Experimental and machine learning insightsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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