A Design Proposal for Optimal Transcutaneous Energy Transmitters

Abstract

International audienceThe use of artificial organs completely implanted in the body of the patient requires a design of Transcutaneous Energy Transmitters (TET) that provide more comfort and reliability to the patient, i.e., a TET as small as possible with the least increase of temperature and that meets several project requirements. In this paper, the multi-objective genetic algorithm (MGA) optimization method is used to minimize volume and dissipated power per coil area of round spiral TETs. It assures the transmission of 13 W of power and load voltage within limits defined by the electronic circuits at coil gaps up to 25 mm. A variable resistance that absorbs a defined active power is used instead a fixed resistor load in order to best represent the artificial organ with regulator. Moreover, the paper discusses model issues such as the influence of the misalignment between the coils on the coupling of the TET

Analysis and optimisation of three‐coil wireless power transfer systems

“…Implantable biomedical devices are usually designed following strategies which include to implement the implantable circuit as small as possible [16][17][18][19][20][21]. Of course, the customised WPT system should in principle follow the above rules.…”

Optimizing Transcutaneous Energy Transmitter Using Game Theory

“…Similar to [3], in this paper, the increase of temperature is indirectly accounted for by the relation of the dissipated power (P dissipated ) in W to the coil area S Coil (see Fig. 1) in m 2 , denominated "thermo factor" (λ) in W/m 2…”

“…The functions used by all players ( f 1 , f 2 , f 3 , f 4 , and f 5 ) are defined in (2); f iold is the value of the function i at the previous iteration. Each player uses the GA to minimize its objective function with the constraints defined by (3). Fig.…”

“…Since they perform such an important task, seeking the smallest coils that transfer the necessary power at the maximum efficiency has attracted attention over recent years. This search was the goal of [3], which used multiobjective genetic algorithm (MGA) optimization, having seven final different configurations in the Pareto front (PF). The problem has become to decide which of the configurations from the PF should be used.…”