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A coupled analytical-numerical approach for optimal sizing of power inductors

机译：耦合分析数值方法可优化功率电感器的尺寸

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Purpose - The purpose of this paper is to present an optimal sizing methodology. It is applied to a foil-coil powder core power inductor used in new generation inverters designed for hybrid and full-electric vehicles. The methodology includes a preliminary analytical calculation and a numerical optimisation aimed at minimizing the component size. Design/methodology/approach - Unlike bulk magnetic alloys or ferrites, the magnetic non-linearity of powder materials cannot be neglected in the analytical calculation. This non-linearity requires the use of an iterative calculation to search the set of parameters for which the target inductance value and the minimum volume are simultaneously reached. The numerical optimization process is based on 2D Finite Element (FE) analysis carried out with FEMM software tool and a simplex-type algorithm run in Scilab software. These two freewares are coupled using the scifemm.sci script which is included in the FEMM distribution. Findings - The association of analytical and FE approaches provides a relevant and quick sizing methodology. It was successfully applied to size a new power inductor. Originality/value - The strong non-linearity of the powder material is correctly taken into account in the analytical model thanks to an iterative calculation process. Thus, the preliminary analytical solution is quite relevant. Consequently, a local FE-based optimization is enough to find the optimal solution close by the analytical one. No global optimization is required. A local optimum is sufficient.
机译：目的-本文的目的是提出一种最佳的上浆方法。它被应用于设计用于混合动力和全电动汽车的新一代逆变器中使用的箔线圈粉末芯功率电感器。该方法包括初步分析计算和旨在最小化组件尺寸的数值优化。设计/方法/方法-与块状磁性合金或铁氧体不同，在分析计算中不能忽略粉末材料的磁性非线性。这种非线性要求使用迭代计算来搜索同时达到目标电感值和最小体积的参数集。数值优化过程基于使用FEMM软件工具进行的2D有限元（FE）分析以及在Scilab软件中运行的单纯形算法。这两个免费软件使用FEMM发行版中包含的scifemm.sci脚本进行耦合。研究结果-分析方法与有限元方法的结合提供了一种相关且快速的方法论。它已成功应用于确定新型功率电感器的尺寸。独创性/价值-通过迭代计算过程，在分析模型中正确考虑了粉末材料的强烈非线性。因此，初步的分析解决方案非常重要。因此，基于局部有限元的优化足以找到分析解决方案附近的最佳解决方案。不需要全局优化。局部最优就足够了。

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