Optimal design of an LCC-S WPT3 Z1 SAE J2954 compliant system, using NSGA-II with nested genetic algorithms for simultaneous local optimization

被引:9
作者
Garcia-Izquierdo, O. [1 ]
Sanz, J. F. [1 ]
Villa, J. L. [1 ]
Martin-Segura, G. [2 ]
机构
[1] Univ Zaragoza, Inst Univ Invest Mixto CIRCE, Fdn CIRCE, Zaragoza, Spain
[2] Wallbox Chargers, Barcelona, Spain
关键词
WPT; NSGA-II; Low-cost; SAE J2954; Hybrid NSGA-II; Nested genetic algorithms; POWER TRANSFER SYSTEM; MULTIOBJECTIVE OPTIMIZATION; EVOLUTIONARY ALGORITHMS; COMPENSATION TOPOLOGY; MODEL;
D O I
10.1016/j.apenergy.2024.123369
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Wireless 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.
引用
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页数:20
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