000135144 001__ 135144
000135144 005__ 20250923084428.0
000135144 0247_ $$2doi$$a10.1016/j.apenergy.2024.123369
000135144 0248_ $$2sideral$$a138519
000135144 037__ $$aART-2024-138519
000135144 041__ $$aeng
000135144 100__ $$aGarcía-Izquierdo, O.$$uUniversidad de Zaragoza
000135144 245__ $$aOptimal design of an LCC-S WPT3 Z1 SAE J2954 compliant system, using NSGA-II with nested genetic algorithms for simultaneous local optimization
000135144 260__ $$c2024
000135144 5060_ $$aAccess copy available to the general public$$fUnrestricted
000135144 5203_ $$aWireless Power Transfer (WPT) for electric vehicles is one of the most promising methods that, given its advantages, will drive the deployment of electric vehicles. This paper presents a mathematical optimization method applied to the complete design of an LCC-S WPT3 Z1 11 kW system that complies with the SAE J2954 standard (Wireless Power Transfer for Light-Duty Plug-in/Electric Vehicles and Alignment Methodology, 2020). A design method based on three phases is proposed, allowing the complete inductor system, including ferrites shielding and compensation circuit components, to function in any relative primary and secondary position. In Phase 1, a multi-objective NSGA-II algorithm is designed, utilizing three nested genetic algorithms. The goal is simultaneously searching for the local optimum between the primary and secondary systems in three positions. This is achieved by modeling the circuit’s electrical and electromagnetic parameters with equations, enabling an iterative process with reduced computational time. The NSGA-II algorithm yields three scenarios: primary copper volume minimization, secondary copper volume minimization, and a compromise solution that optimizes the total volume. The result is then modeled in Phase 2 using a 3D finite element program that includes ferrite and optimal shielding, obtaining the values of inductances and mutual inductance in the three positions, as well as design data for manufacturing. This result is introduced in Phase 3 to optimize compensation circuit components using a second NSGA-II algorithm with three nested genetic algorithms. Again, three scenarios are obtained based on the desired system behavior and the optimal cost of the components. The result is validated through simulation with Matlab-Simulink and experimentally using a prototype constructed for this purpose.
000135144 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-141796OB-I00
000135144 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttp://creativecommons.org/licenses/by-nc/3.0/es/
000135144 590__ $$a11.0$$b2024
000135144 592__ $$a2.902$$b2024
000135144 591__ $$aENGINEERING, CHEMICAL$$b12 / 175 = 0.069$$c2024$$dQ1$$eT1
000135144 591__ $$aENERGY & FUELS$$b20 / 182 = 0.11$$c2024$$dQ1$$eT1
000135144 593__ $$aEnergy (miscellaneous)$$c2024$$dQ1
000135144 593__ $$aBuilding and Construction$$c2024$$dQ1
000135144 593__ $$aRenewable Energy, Sustainability and the Environment$$c2024$$dQ1
000135144 593__ $$aMechanical Engineering$$c2024$$dQ1
000135144 593__ $$aManagement, Monitoring, Policy and Law$$c2024$$dQ1
000135144 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135144 700__ $$0(orcid)0000-0001-7407-0608$$aSanz, J.F.$$uUniversidad de Zaragoza
000135144 700__ $$0(orcid)0000-0002-2207-7418$$aVilla, J.L.$$uUniversidad de Zaragoza
000135144 700__ $$aMartin-Segura, G.
000135144 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000135144 773__ $$g367 (2024), 123369 [20 pp.]$$pAppl. energy$$tApplied Energy$$x0306-2619
000135144 8564_ $$s3307434$$uhttps://zaguan.unizar.es/record/135144/files/texto_completo.pdf$$yVersión publicada
000135144 8564_ $$s2505008$$uhttps://zaguan.unizar.es/record/135144/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135144 909CO $$ooai:zaguan.unizar.es:135144$$particulos$$pdriver
000135144 951__ $$a2025-09-22-14:41:04
000135144 980__ $$aARTICLE