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dc.contributor.authorAfianto, D
dc.contributor.authorHan, Y
dc.contributor.authorYan, P
dc.contributor.authorYang, Y
dc.contributor.authorElbarghthi, AFA
dc.contributor.authorWen, C
dc.date.accessioned2022-11-07T15:13:07Z
dc.date.issued2022-11-01
dc.date.updated2022-11-07T12:59:22Z
dc.description.abstractDue to the rise in awareness of global warming, many attempts to increase efficiency in the automotive industry are becoming prevalent. Design optimization can be used to increase the efficiency of electric vehicles by reducing aerodynamic drag and lift. The main focus of this paper is to analyse and optimise the aerodynamic characteristics of an electric vehicle to improve efficiency of using computational fluid dynamics modelling. Multiple part modifications were used to improve the drag and lift of the electric hatchback, testing various designs and dimensions. The numerical model of the study was validated using previous experimental results obtained from the literature. Simulation results are analysed in detail, including velocity magnitude, drag coefficient, drag force and lift coefficient. The modifications achieved in this research succeeded in reducing drag and were validated through some appropriate sources. The final model has been assembled with all modifications and is represented in this research. The results show that the base model attained an aerodynamic drag coefficient of 0.464, while the final design achieved a reasonably better overall performance by recording a 10% reduction in the drag coefficient. Moreover, within individual comparison with the final model, the second model with front spitter had an insignificant improvement, limited to 1.17%, compared with 11.18% when the rear diffuser was involved separately. In addition, the lift coefficient was significantly reduced to 73%, providing better stabilities and accounting for the safety measurements, especially at high velocity. The prediction of the airflow improvement was visualised, including the pathline contours consistent with the solutions. These research results provide a considerable transformation in the transportation field and help reduce fuel expenses and global emissions.en_GB
dc.format.extent1584-1584
dc.identifier.citationVol. 24(11), pp. 1584-1584en_GB
dc.identifier.doihttps://doi.org/10.3390/e24111584
dc.identifier.urihttp://hdl.handle.net/10871/131670
dc.identifierORCID: 0000-0002-4445-1589 (Wen, Chuang)
dc.identifierScopusID: 36454182800 (Wen, Chuang)
dc.identifierResearcherID: I-5663-2016 (Wen, Chuang)
dc.language.isoenen_GB
dc.publisherMDPIen_GB
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).en_GB
dc.subjectelectric vehicleen_GB
dc.subjectelectric hatchbacken_GB
dc.subjectfuel efficiencyen_GB
dc.subjectdesignen_GB
dc.subjectoptimizationen_GB
dc.subjectaerodynamicsen_GB
dc.subjectcomputational fluid dynamicsen_GB
dc.titleOptimisation and Efficiency Improvement of Electric Vehicles Using Computational Fluid Dynamics Modellingen_GB
dc.typeArticleen_GB
dc.date.available2022-11-07T15:13:07Z
dc.descriptionThis is the final version. Available on open access from MDPI via the DOI in this recorden_GB
dc.descriptionData Availability Statement: The research data supporting this publication are provided within this paper.en_GB
dc.identifier.eissn1099-4300
dc.identifier.journalEntropyen_GB
dc.relation.ispartofEntropy, 24(11)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2022-10-01
rioxxterms.versionVoRen_GB
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2022-11-07T15:11:48Z
refterms.versionFCDVoR
refterms.dateFOA2022-11-07T15:13:12Z
refterms.panelBen_GB
refterms.dateFirstOnline2022-11-01


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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's licence is described as © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).