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 meth...
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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.
To solve the problem of large search demand and insufficient search ability of UAVs, this article has proposed a task planning method of UAV group based on nested learning algorithm. According to the characteristics o...
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ISBN:
(纸本)9781665440899
To solve the problem of large search demand and insufficient search ability of UAVs, this article has proposed a task planning method of UAV group based on nested learning algorithm. According to the characteristics of the target path, the method combines task allocation strategy and path planning algorithm to make decision. This method can give reasonable suggestions on the number of UAVs, and effectively improve the efficiency of UAV group search task. The algorithm uses the elite individual selection strategy based on tournament selection method to improve the optimization efficiency. And the algorithm uses neighborhood method to avoid local optimum. Finally, the algorithm is verified by the data of a search task. The experimental results show that the planning method used in this paper is suitable for the UAVs' cooperative search problem. Compared with other planning methods, it has faster solution speed and is conducive to emergency decision-making. This method also has reference value for the cooperative planning of search tasks.
To solve the problem of large search demand and insufficient search ability of UAVs,this article has proposed a task planning method of UAV group based on nested learning *** to the characteristics of the target path,...
详细信息
To solve the problem of large search demand and insufficient search ability of UAVs,this article has proposed a task planning method of UAV group based on nested learning *** to the characteristics of the target path,the method combines task allocation strategy and path planning algorithm to make *** method can give reasonable suggestions on the number of UAVs,and effectively improve the efficiency of UAV group search *** algorithm uses the elite individual selection strategy based on tournament selection method to improve the optimization *** the algorithm uses neighborhood method to avoid local ***,the algorithm is verified by the data of a search *** experimental results show that the planning method used in this paper is suitable for the UAVs' cooperative search *** with other planning methods,it has faster solution speed and is conducive to emergency *** method also has reference value for the cooperative planning of search tasks.
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