Publication:
Slime mould algorithm and kriging surrogate model-based approach for enhanced crashworthiness of electric vehicles

dc.contributor.authorYıldız, Betül Sultan
dc.contributor.buuauthorYILDIZ, BETÜL SULTAN
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentMakina Mühendisliği
dc.contributor.departmentElektrik ve Enerji Bölümü
dc.contributor.orcid0000-0002-7493-2068
dc.contributor.scopusid57094682600
dc.date.accessioned2025-05-13T09:22:58Z
dc.date.issued2020-01-01
dc.description.abstractEspecially during the last decade, electric vehicles have been used frequently in most of the countries. With the establishment of charging station infrastructures, fossil fuel vehicles will inevitably be replaced by electric vehicles in the next ten years. For this reason, electric vehicle components need to be developed very quickly. This paper concentrates on designing a new thin-walled energy absorber to be used in designing of electric vehicles. The material of the thin-walled energy absorber developed in this paper is cold-rolled advanced high-strength steel, which is Docol 1300. This research presents the first application of the slime mould algorithm to the optimum design of automobile components in the literature. Function evaluations are carried out using finite element analysis and estimated by using the kriging surrogate model. The results show that both the SMA and Docol 1300 advanced high-strength material provide exceptional features for enhancing crashworthiness in electric vehicle design, simultaneously.
dc.identifier.doi10.1504/IJVD.2020.114786
dc.identifier.endpage68
dc.identifier.issn0143-3369
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85105828263
dc.identifier.startpage54
dc.identifier.urihttps://hdl.handle.net/11452/52059
dc.identifier.volume83
dc.indexed.scopusScopus
dc.language.isoen
dc.publisherInderscience Publishers
dc.relation.journalInternational Journal of Vehicle Design
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectWCA
dc.subjectWater cycle algorithm
dc.subjectSSA
dc.subjectSMA
dc.subjectSlime mould algorithm
dc.subjectSalp swarm algorithm
dc.subjectOptimum design
dc.subjectEnergy absorber
dc.subjectElectric vehicles
dc.subjectDocol 1300
dc.subjectAdvanced high-strength steel
dc.subject.scopusGenetic Algorithm; Automotives; Particle Swarm Optimization
dc.titleSlime mould algorithm and kriging surrogate model-based approach for enhanced crashworthiness of electric vehicles
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/Makina Mühendisliği/Elektrik ve Enerji Bölümü
relation.isAuthorOfPublicatione544f464-5e4a-4fb5-a77a-957577c981c6
relation.isAuthorOfPublication.latestForDiscoverye544f464-5e4a-4fb5-a77a-957577c981c6

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