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In this article, the scale reduction method is applied to an induction power transfer system for electric vehicle (EV) charging for its rapid and low-cost prototyping. In addition, a co-simulation method is proposed, which is based on the coupling between a circuit analysis and a finite-element (FE) analysis. Then, it is proposed to combine the scale reduction method with the co-simulation method to consider, at the same time, the nonlinearities due to the power electronic converter and the magnetic material characteristics of the system. Finally, a 1 kW experimental IPT system is proposed to validate both methods.

Purpose – The purpose of this paper is to introduce a novel methodology for uncertainty quantification in large-scale systems. It is a non-intrusive approach based on the unscented transform (UT) but it requires far less simulations from a EM solver for certain models. Design/methodology/approach – The methodology of uncertainty propagation is carried out adaptively instead of considering all input variables. First, a ranking of input variables is determined and after a classical UT is applied successively considering each time one more input variable. The convergence is reached once the most important variables were considered. Findings – The adaptive UT can be an efficient alternative of uncertainty propagation for large dimensional systems. Originality/value – The classical UT is unfeasible for large-scale systems. This paper presents one new possibility to use this stochastic collocation method for systems with large number of input dimensions. [ABSTRACT FROM AUTHOR]

This paper presents several RF-to-DC converter topologies and compares their performances based on measurements made on fabricated prototypes. A medium range wireless low power transmission experiment is presented. A low power DC-DC boost converter designed rectenna-generated energy conditioning is also discussed.