A new technique is proposed to reconstruct faulty wiring networks from the time-domain reflectometry (TDR) response. The developed method is also for characterization of defects in the branches of the network. The direct problem (propagation along the cables) is modeled by RLCG circuit parameters computed by finite element method (FEM) and the finite-difference time domain (FDTD) method. Genetic algorithms (GAs) are used to solve the inverse problem. The proposed method allows to accurately locate wire faults. Some examples are presented to validate and illustrate the ability of this reconstruction method.

This paper presents an evaluation of the EMFs in the human body exposed to the wireless inductive charging system of electric vehicles such that the compliance of this charging system with respect to human EM exposure limits can be examined. A magnetic resonance imaging-derived and high-resolution model of the human body is used. An exposure assessment of a representative wireless inductive charging system, under a limited set of operating conditions, is provided to estimate the induced EMFs. The numerical analysis is performed with the finite element method. Numerical modeling of the system next to a standing human model shows that the EM exposure limits can be absolutely satisfied even when the transmitter coil is very close to the body. Furthermore, the worst configuration for the exposure evaluation of the wireless charging system is taken into consideration. This paper provides a useful guideline for the industry to develop inductive charging systems following the safety standards of radiation protection.

This paper presents a new technique to reconstruct faulty wiring networks and/or to localize the defects affecting the branches of the wiring network from the time domain reflectometry response. The method is also for characterization of defects in branches on the network. The direct model for wave propagation along the transmission lines is modeled by RLCG circuit parameters computed by finite elements method (FEM) or analytical solution and the finite difference time-domain (FDTD) method. Neural networks (NNs) are used to solve the inverse problem. A set of experimental results is carried out in order to validate the calculations.

This paper focuses on the design of a contactless charging system for electric vehicles (EVs) using inductive loops connected to a resonance converter. The study carries out the system operation, electromagnetic radiation, and testing. It is shown that the presence of the chassis leads to a double resonance and has a strong influence on the radiated fields.

Time-reversal multiple signal classification (TR-MUSIC) is here applied to testing cable networks in order to detect and locate soft faults. TR-MUSIC is shown to provide spatial resolution in the millimeter range while using continuous-wave (CW) test signals, even at frequencies with guided wavelengths much larger than cables length. State-of-the-art time-domain reflectometry (TDR) methods would require bandwidths in the order of hundreds of megahertz for a similar performance. As opposed to TDR, TR-MUSIC does not suffer from the ambiguity created by the existence of multiple echoes in cable networks, which can be easily misinterpreted as multiple faults, leading to false alarms. TR-MUSIC is intrinsically adapted to dealing with multiple faults, handing a direct estimate of the number of faults found in a network under test. Furthermore, the detection capabilities of TR-MUSIC are insensitive to the severity of a fault, as faults are not detected based on the intensity of their echoes as done in TDR techniques, but on a subspace approach mostly dependent on phase patterns. Accurate identification of faults from CW signals points to the possibility of designing simpler test systems, not requiring pulse generators and fast electronics. TR-MUSIC accuracy is demonstrated experimentally for locating both single as well as multiple soft faults in two cable networks. The proposed method also gives access to the reflection coefficient of each fault, thus enabling an estimate of its severity.

This paper proposes a methodology for the assessment of the human exposure to magnetic fields generated by dynamic inductive power transfer systems for automotive applications. Since the magnetic field is pulsed, current safety standards and guidelines require the use of time-domain approaches to evaluate the peak exposure which has to be limited under the prescribed limits. This paper shows that, for these kind of systems, the peak exposure can be efficiently evaluated by means of a time-harmonic formulation. Furthermore, a methodology to identify the worst case scenario is proposed.