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.

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.

Wireless power transfer (WPT) technologies are currently growing in popularity because of problems with batteries in portable devices, such as the added weight and the inconvenience of conventional wired charging methods. However, wireless charging systems generate leakage magnetic fields, causing malfunctions in adjacent electronic devices. Technologies to suppress electromagnetic interference (EMI) from portable WPT systems are required to prevent this problem. In this paper, we propose a topology design for a multiresonance reactive shield to suppress the EMI from WPT systems. The proposed shield employs closed loops, which are located peripheral to the WPT coil and resonance matching capacitor. The power efficiency and shielding performance of the proposed method were compared with those of the conventional WPT system coil shielding topologies. The proposed shield exhibited about 27.37 dB more 3rd harmonic EMI suppression than other methods while maintaining higher power transfer efficiency.

In this paper, we propose and analyze a through silicon via (TSV) with a silicon dioxide well (SDW) to reduce leakage current in the design of a high-speed signaling and low-power consumption high-bandwidth memory (HBM) interface. In the proposed TSV, the SDW is inserted into the silicon substrate area between TSVs. By inserting the SDW, the silicon substrate’s effective conductivity and permittivity are lowered compared to a conventional TSV. The proposed TSV with the SDW improves insertion loss by up to 0.079 dB at 4 GHz by minimizing the leakage current. In addition, by reducing the effective permittivity of the silicon substrate, the difference in ratio between the capacitive coupling and inductive coupling is minimized. It is also possible to obtain a reduction in far-end crosstalk (FEXT) in the range of HBM interface signaling. RLGC modeling of the proposed TSV with the SDW is presented, and is physically analyzed based on the frequency-dependent equivalent capacitance and conductance of the silicon substrate. The proposed TSV with SDW is finally evaluated using an eye diagram, and the eye height and width improved by up to 13.7 % and 4.7 % at 8 Gb/s, respectively, compared to the conventional TSV. The proposed TSV also reduced dynamic power consumption by 48.1 % at 8 Gb/s, verifying the low-power structure.

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.