Optimal Design of Bipolar Power Pad for Dynamic Inductive EV Charging System Application

H. Jafari, Temitayo O. Olowu, Maryam Mahmoudi, A. Sarwat
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Abstract

This paper proposes a multi-objective design optimization of bipolar power pads (BPP) of dynamic inductive electric power transfer (IPT) with application in electric vehicles. Minimization of IPT's design cost, power loss, and maximization of the IPT system's tolerance against horizontal/vertical misalignment are considered as objective functions during optimization process. The proposed design variables of the proposed algorithm are the shield plate length and width, ferrite bar length and width, the overlapping length of the coils, the coil width and inner length of the coil. Power electronic limitations, maximum allowable electromagnetic field exposure, minimum efficiency (≤ 80%), and upper/lower limits of design parameters are considered as the constraints of this optimization problem. The time harmonic electromagnetic physics model of the BPP is analyzed using an FEMM software coupled with MATLAB. A Non-Dominated Genetic Algorithm (NSGA-II) is employed as the optimization method, in which, the electromagnetic magnetic measurements from the FEMM software is used to evaluate the fitness values of the proposed objectives. The proposed BPP design optimization is applied on a 10-kW IPT system as a case study. The optimization results produced 15 Pareto optimal solutions which allows the designer to select the best design parameters based on the objectives of highest priority. The experimental setup of the dynamic IPT system based on one of the Pareto solution parameters is constructed and illustrated with details.
用于电动汽车动态感应充电系统的双极电源垫优化设计
提出了一种应用于电动汽车的动态感应电力传输(IPT)双极电源垫(BPP)多目标优化设计方法。在优化过程中,以IPT的设计成本最小、功率损失最小、IPT系统对水平/垂直偏差的容忍度最大为目标函数。提出的算法设计变量为屏蔽板的长度和宽度、铁氧体棒的长度和宽度、线圈的重叠长度、线圈的宽度和线圈的内长度。考虑电力电子限制、最大允许电磁场暴露、最小效率(≤80%)和设计参数的上下限作为优化问题的约束条件。利用FEMM软件和MATLAB软件对BPP的时谐电磁物理模型进行了分析。优化方法采用非支配遗传算法(non - dominant Genetic Algorithm, NSGA-II),利用FEMM软件的电磁测量值对目标的适应度值进行评估。并以10kw IPT系统为例进行了BPP优化设计。优化结果产生了15个Pareto最优解,允许设计师根据最高优先级的目标选择最佳设计参数。建立了基于其中一个帕累托解参数的动态IPT系统的实验装置,并进行了详细说明。
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