This paper proposes an autonomous coil alignment system (ACAS) for electric vehicles (EVs) with dynamic wireless charging (DWC) to mitigate the reduction in received power caused by lateral misalignment between the source and load coils. The key component of the ACAS is a novel sensor coil design, which can detect the load coil’s left or right position relative to the source coil by observing the change in voltage phase. This allows the lateral misalignment to be estimated through the wireless power transfer (WPT) system alone, which is a novel tracking method for vehicular applications. Once misalignment is detected, the vehicle’s lateral position is self-adjusted by an autonomous steering function. The feasibility of the overall operation of the ACAS was verified through simulation and experiments. In addition, an analysis based on experimental results was conducted, demonstrating that 26% more energy can be transferred during DWC with the ACAS, just by keeping the vehicle’s load coil aligned with the source coil.

The paper proposes a method for locating an electric means of transportation such as electric vehicles that adopts dynamic wireless charging system. The method works with magnetic wireless power transfer systems, based on a ferrite core counting approach. The ferrite core counter system operates by counting all the ferrite cores of the source coils used for guiding the magnetic field in a magnetic wireless power transfer system. The proposed ferrite core counter has a position detection accuracy equivalent to the spacing of the ferrite cores. In addition, this paper proposes a ferrite position identification (FPID) messaging unit to correct ferrite core counter errors. FPID blocks which are successively placed under the source coil are recognized by the ferrite core counter, and their passage is reported as a sort of message in the vehicle. This FPID messaging unit provides the ferrite core counter with a system that can correct errors in the number of ferrite cores counted. The effectiveness of the vehicle positioning system with the ferrite core counter and the FPID messaging unit has been demonstrated through simulation and experiments.

Wireless power transfer (WPT) is being developed to supply electric power to electric trains, using a source coil in/on the railway track and a pick-up coil on the train. A number of benefits can be obtained by eliminating the catenary and pantograph currently used for railway electric power supply. However, the WPT employs coils, and the electromagnetic field generated by the WPT can interfere with conventional train detection methods, such as track circuits and RFID, installed on the tracks. Train position information is critical for railway operation, especially for high speed trains. This paper proposes a novel system which provides train position information using a source coil segment. It consists of onboard sensor coils, ferrite blocks designed using specific position information, and a detector. The proposed system can be implemented using the source coil and load coil of the WPT. Information about the train's relative position is obtained by the detection of ferrite blocks distributed within the source coil segment. The train position information provided by the ferrite components is detected by onboard sensor coils and a detector. The proposed ferrite position identification (FPID) system provides accurate train position information and is not affected by WPT electromagnetic interference. The operating principles of the FPID system are presented in detail. The performance of the proposed FPID system was measured by simulations and experiments.

An autonomous coil alignment system (ACAS) using fuzzy steering control is proposed for vehicles with dynamic wireless charging. The misalignment between the power receiver coil and power transmitter coil is determined based on the voltage difference between two coils installed on the front-left/front-right of the power receiver coil and is corrected through autonomous steering using fuzzy control. The fuzzy control is chosen over other control methods for implementation in ACAS due to the nonlinear characteristic between voltage difference and lateral misalignment distance, as well as the imprecise and constantly varying voltage readings from sensors. The operational validity and feasibility of the ACAS are verified through simulation, where the vehicle equipped with ACAS is able to align with the power transmitter in the road majority of the time during operation, which also implies achieving better wireless power delivery. An autonomous coil alignment system (ACAS) using fuzzy steering control is proposed for vehicles with dynamic wireless charging. The misalignment between the power receiver coil and power transmitter coil is determined based on the voltage difference between two coils installed on the front-left/front-right of the power receiver coil and is corrected through autonomous steering using fuzzy control. The fuzzy control is chosen over other control methods for implementation in ACAS due to the nonlinear characteristic between voltage difference and lateral misalignment distance, as well as the imprecise and constantly varying voltage readings from sensors. The operational validity and feasibility of the ACAS are verified through simulation, where the vehicle equipped with ACAS is able to align with the power transmitter in the road majority of the time during operation, which also implies achieving better wireless power delivery.

S-parameters are widely utilized for characterizing circuit components and systems. Although they only contain the frequency domain responses with a limited range, the time domain characteristics of the circuit systems can be estimated using processing methods for extracting the causal impulse response from the S-parameter. This paper presents an efficient extrapolation method for the accurate transient simulation of circuit systems using band-limited S-parameters. The proposed method constructs the extrapolation function of the given S-parameter for ensuring the causality of its impulse response, with higher accuracy over the measured frequency range, and without computationally intensive iteration. We implemented this method using the non-iterative approach for extracting coefficients of the extrapolation function, and the optimal selection scheme for its input parameters. To verify the proposed method, 2-port network using the Chebyshev band-stop filters and 4-port network using the coupled transmission line were designed. In the first case, the proposed method had a normalized mean square error of -71.32 dB, a non-causal error of below system resolution, and the computation time of 0.17 sec. Under the similar accuracy and causality conditions, the conventional methods, such as the iterative extrapolation and the vector fitting, required 122.45 sec and 3.98 sec respectively. In the second case, the proposed extrapolation method also ensured the causality of the time domain response with higher accuracy and computational efficiency.

This letter presents a new wideband equivalent circuit model for through silicon via (TSV) with consideration of the effective substrate current loop in silicon substrate and proximity effect on the currents in the TSV. The proposed model can accurately predict the electrical performance of the TSV at wide frequency range. The proposed model correlates well up to 100 GHz when compared to the full-wave electromagnetic simulation.