Angle Prediction for Field Orientation Based on Back Propagation Neural Network of Wireless Power Transfer System

Xu, Jian‐Xin; Tan, Pingan; Shen, Huanfeng; Zhang, Hao; Pang, Lisheng; Deng, Yimeng

doi:10.1109/ipemc-ecceasia48364.2020.9367945

Cited by 2 publications

(1 citation statement)

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“…In [38], the efficiency of a WPT system is maximized by means of an NN that computes the optimal parameters for a tunable compensation network in order to enforce the impedance matching of the system despite a variation in the coils distance or in the load. The NN in [39] is used to estimate the orientation of the receiving coil with respect to the transmitting ones in an omnidirectional WPTS. Position estimation is also considered in [40], with the NN processing the signals coming from four auxiliary coils to assess the relative position of the coupled coils.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach to Improve the Control of Dynamic Wireless Power Transfer Systems

Bertoluzzo,

Di Barba,

Forzan

et al. 2023

Energies

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In this paper, an innovative approach for the fast estimation of the mutual inductance between transmitting and receiving coils for Dynamic Wireless Power Transfer Systems (DWPTSs) is implemented. To this end, a Convolutional Neural Network (CNN) is used; an image representing the geometry of two coils that are partially misaligned is the input of the CNN, while the output is the corresponding inductance value. Finite Element Analyses are used for the computation of the inductance values needed for CNN training. This way, thanks to a fast and accurate inductance estimated by the CNN, it is possible to properly manage the power converter devoted to charge the battery, avoiding the wind up of its controller when it attempts to transfer power in poor coupling conditions.

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Section: Introductionmentioning

confidence: 99%