To address the issue that the output voltage and power of medium- and low-power wireless power transfer (WPT) systems cannot remain constant under coil misalignment, this paper proposes a single-switch WPT system with misalignment-tolerant characteristics. Based on a single-switch topology, the system combines the LCC-S and S-S compensation networks through an input-series and output-series connection, forming a simplified hybrid-compensated single-switch WPT topology. By exploiting the complementary output characteristics of the two compensation networks, a stable output voltage is achieved under varying mutual inductance conditions. To further enhance misalignment adaptability, a grid-type flat spiral (GFSP) coil is designed for the magnetic coupler. This coil configuration avoids magnetic flux cancelation during lateral displacement, while maintaining a consistent mutual inductance variation trend between the dual windings, thereby exhibiting strong tolerance to misalignment along the X-axis. The proposed system is validated through MATLAB/Simulink simulations and experiments on a 50 W prototype. The results demonstrate that the system maintains resonance and achieves zero-voltage switching (ZVS) of the power device under ±60 mm X-axis misalignment, with output voltage fluctuation below 4% and efficiency fluctuation below 3%, verifying the proposed system’s effectiveness in misalignment tolerance.

With the widespread adoption of electric vehicles (EVs), the management and scheduling of charging and discharging play a crucial role in the performance of both the electricity grid and electric vehicles. Particularly in the context of peak shaving, valley filling, and the promotion of the energy internet infrastructure, efficient management of the EV charging and discharging process is vital. This study investigates the control and management issues surrounding EV charging and discharging, proposing a management strategy based on deep reinforcement learning. By constructing an intelligent decision-making model, it integrates factors such as the operating conditions of the electrical grid, user behavioral preferences, EV battery characteristics, and renewable energy outputs. The study collects real-world EV usage data from a city, establishing an experimental environment to simulate the interaction between the electricity grid and electric vehicles. Using techniques such as Deep Q-Network (DQN) and policy gradients, it constructs a decision network to explore charging and discharging strategies across different time scales and load situations. Experimental results show that this strategy, compared to traditional charging schedule methods, can effectively reduce energy loss during charging, enhance battery life, and balance the grid load, while suppressing demand peaks, thus achieving intelligent optimization and reliability enhancement of the charging and discharging process. Particularly, an adaptive charging power adjustment technique within the strategy can dynamically adjust the charging power according to the real-time status of the EV and grid load without affecting the user’s daily use, thereby achieving the dual objectives of efficient energy saving and economy. The research also quantitatively analyzes battery degradation characteristics and the continuity of charging to ensure the long-term sustainability of the charging strategy. The research findings are significant for understanding and guiding the practical management of EV charging and discharging.

Coaxial cable plays a vital role in the wide application of telecommunications, network, and television broadcasting and other fields, with its transmission performance directly affecting signal quality and transmission efficiency. In practical applications, the length of the cable and the terminal load state of the connection often affect the stability of the signal. In order to solve this problem, we used STMicroelectronics STM32F407VET6 (STMicroelectronics, Geneva, Switzerland) as the master controller in this system, and deduced the length of the cable by analyzing the functional relationship between the length of the cable and the open circuit frequency. An open cable is regarded as a capacitor, and any two core wires are regarded as two plates of a flat capacitor. The linear relationship between open frequency and length is used to detect the length of the coaxial cable. The system then determines whether the terminal load is capacitance or resistance based on the detected frequency. If no frequency is detected, then the load is considered resistance. The system detects the resistance value of the resistor through series voltage division. If a frequency is detected, this indicates that the load is capacitance. At this time, the system uses an RC oscillation circuit composed of HGSEMI ICL8038 (Huagao Semiconductor Co., Ltd., Wuxi, China) for testing, and provides the phase shift required by the corresponding signal through the RC network, so as to detect the capacitance value. Finally, we successfully designed a coaxial cable length and terminal load detection system based on STM32F407VET6. Through this system, the user can accurately understand the length of the coaxial cable and the load of the connection terminal, which provides a reliable guarantee for the stability of signal transmission.