000095093 001__ 95093
000095093 005__ 20220120225835.0
000095093 0247_ $$2doi$$a10.3390/s20133633
000095093 0248_ $$2sideral$$a118791
000095093 037__ $$aART-2020-118791
000095093 041__ $$aeng
000095093 100__ $$aLuque, Pablo
000095093 245__ $$aMulti-objective evolutionary design of an electric vehicle chassis
000095093 260__ $$c2020
000095093 5060_ $$aAccess copy available to the general public$$fUnrestricted
000095093 5203_ $$aAn iterative algorithm is proposed for determining the optimal chassis design of an electric vehicle, given a path and a reference time. The proposed algorithm balances the capacity of the battery pack and the dynamic properties of the chassis, seeking to optimize the tradeoff between the mass of the vehicle, its energy consumption, and the travel time. The design variables of the chassis include geometrical and inertial values, as well as the characteristics of the powertrain. The optimization is constrained by the slopes, curves, grip, and posted speeds of the different sections of the track. Particular service constraints are also considered, such as limiting accelerations due to passenger comfort or cargo safety. This methodology is applicable to any vehicle whose route and travel time are known in advance, such as delivery vehicles, buses, and race cars, and has been validated using telemetry data from an internal combustion rear-wheel drive race car designed for hill climb competitions. The implementation of the proposed methodology allows to reduce the weight of the battery pack by up to 20%, compared to traditional design methods.
000095093 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/TIN2017-84804-R
000095093 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000095093 590__ $$a3.576$$b2020
000095093 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b14 / 64 = 0.219$$c2020$$dQ1$$eT1
000095093 591__ $$aCHEMISTRY, ANALYTICAL$$b26 / 83 = 0.313$$c2020$$dQ2$$eT1
000095093 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b82 / 273 = 0.3$$c2020$$dQ2$$eT1
000095093 592__ $$a0.636$$b2020
000095093 593__ $$aAnalytical Chemistry$$c2020$$dQ2
000095093 593__ $$aAtomic and Molecular Physics, and Optics$$c2020$$dQ2
000095093 593__ $$aBiochemistry$$c2020$$dQ2
000095093 593__ $$aMedicine (miscellaneous)$$c2020$$dQ2
000095093 593__ $$aInformation Systems$$c2020$$dQ2
000095093 593__ $$aInstrumentation$$c2020$$dQ2
000095093 593__ $$aElectrical and Electronic Engineering$$c2020$$dQ2
000095093 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000095093 700__ $$aMántaras, Daniel A.
000095093 700__ $$aMaradona, Álvaro
000095093 700__ $$aRoces, Jorge
000095093 700__ $$aSánchez, Luciano
000095093 700__ $$0(orcid)0000-0002-9007-1560$$aCastejón, Luis$$uUniversidad de Zaragoza
000095093 700__ $$0(orcid)0000-0002-0341-8408$$aMalón, Hugo$$uUniversidad de Zaragoza
000095093 7102_ $$15004$$2530$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Ingen.e Infraestr.Transp.
000095093 7102_ $$15004$$2545$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Ingeniería Mecánica
000095093 773__ $$g20, 13 (2020), 3633 [22 pp.]$$pSensors$$tSensors$$x1424-8220
000095093 8564_ $$s3369239$$uhttps://zaguan.unizar.es/record/95093/files/texto_completo.pdf$$yVersión publicada
000095093 8564_ $$s465834$$uhttps://zaguan.unizar.es/record/95093/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000095093 909CO $$ooai:zaguan.unizar.es:95093$$particulos$$pdriver
000095093 951__ $$a2022-01-20-22:57:30
000095093 980__ $$aARTICLE