Stochastic Analysis of the Efficiency of a Wireless Power Transfer System Subject to Antenna Variability and Position Uncertainties

• Published in: Sensors (Basel, Switzerland) Vol. 16; no. 7; p. 1100
• Main Authors: Rossi, Marco, Stockman, Gert-Jan, Rogier, Hendrik, Vande Ginste, Dries
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.07.2016; MDPI
• Subjects: Efficiency; Monte Carlo simulation; polynomial chaos; power transfer efficiency; radiative near-field; stochastic collocation; stochastic testing; textile antenna; uncertainty quantification; wireless power transfer; textile antenna; stochastic testing; uncertainty quantification; radiative near-field; power transfer efficiency; wireless power transfer; stochastic collocation; polynomial chaos
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16071100

The efficiency of a wireless power transfer (WPT) system in the radiative near-field is inevitably affected by the variability in the design parameters of the deployed antennas and by uncertainties in their mutual position. Therefore, we propose a stochastic analysis that combines the generalized polynomial chaos (gPC) theory with an efficient model for the interaction between devices in the radiative near-field. This framework enables us to investigate the impact of random effects on the power transfer efficiency (PTE) of a WPT system. More specifically, the WPT system under study consists of a transmitting horn antenna and a receiving textile antenna operating in the Industrial, Scientific and Medical (ISM) band at 2.45 GHz. First, we model the impact of the textile antenna’s variability on the WPT system. Next, we include the position uncertainties of the antennas in the analysis in order to quantify the overall variations in the PTE. The analysis is carried out by means of polynomial-chaos-based macromodels, whereas a Monte Carlo simulation validates the complete technique. It is shown that the proposed approach is very accurate, more flexible and more efficient than a straightforward Monte Carlo analysis, with demonstrated speedup factors up to 2500.