Wireless power transfer (WPT) is a promising energy transfer method for its security, reliability and convenience, and has been applied in various scenarios. In practical applications, in order to prolong the battery lifecycles, the WPT system preferably has the load-independent constant current (CC) and constant voltage (CV) charging characteristics and zero phase angle (ZPA) operation for high power transfer efficiency. However, since the equivalent resistance of the battery significantly changes over the entire charging process, it is difficult to design a WPT system with the above functions. This manuscript proposes a S/S/P-compensated three-coil structure WPT system and its corresponding parameter tuning method to achieve CC and CV charging modes at two different ZPA operating frequencies. For the proposed three-coil structure WPT system, since only one compensation capacitor is required on the receiver, the compactness and portability of the receiver of the WPT system are guaranteed. Firstly, the comprehensive theoretical analysis for the CC and CV charging modes and ZPA operation is presented, and the detailed parameter design method of the proposed system is provided. Secondly, the sensitivity of variations in system parameters to CC and CV characteristics is analyzed, and the implementation of ZVS operation for further improving efficiency is introduced. Thirdly, a confirmatory experimental prototype with charging current of 4.6A and charging voltage of 56V is fabricated, and the experimental results are basically consistent with the theoretical analysis. Finally, a comprehensive comparison with previous WPT systems of the same type is carried out to highlight the superiority of the proposed three-coil structure WPT system.

In some practical applications such as battery charging and LED driving, it is desirable for the WPT systems to have load-independent constant current output (CCO) characteristic. However, the existing WPT systems with CCO function suffer from low efficiency, limited output current by the loosely coupled transformer (LCT) parameters, low space utilization and high cost. In order to solve the above issues, this manuscript proposes a new integrated LCC/S WPT system with CCO function, in which the transmitter-side compensation inductor and the transmitter-side coil are coaxially and symmetrically stacked on the same plane to save the space occupied when they are placed separately in the traditional structure. A detailed mathematical derivation of the proposed integrated LCC/S WPT system with both load-independent CCO characteristic and zero phase angle (ZPA) operation is carried out, and the explicit corresponding parameter design method is provided. It is worth emphasizing that the output current of the proposed integrated LCC/S WPT system is not limited by the LCT parameters due to sufficient design freedom. Then, a comprehensive theoretical analysis on the realization of zero voltage switching (ZVS) operation for higher overall efficiency and the impact on the CCO characteristic by slightly adjusting the parameters of the compensation capacitors is performed. Furthermore, a detailed and thorough efficiency comparison between the proposed integrated LCC/S WPT system and the SS WPT system is performed under alignment, long-distance and misalignment conditions to reflect the efficiency advantage of the proposed system. Finally, a demonstrative experimental prototype with 60-V DC input voltage and 4.9-A CCO is fabricated to validate the correctness and rationality of the proposed integrated LCC/S WPT system and the corresponding theoretical analysis.

Wireless power transfer (WPT) has attracted considerable attention due to its potential application prospect. It has a requirement that the transmitting coil and receiving coil should be aligned with each other during the charging process. Once axial or radial misalignment occurs, transfer efficiency decreases. To cope with this problem, an anti-misalignment WPT system is proposed in this letter. It is designed based on nonlinear Duffing resonance. The proposed system is theoretically analyzed and power transfer efficiency is computed. Simulation and experimental work are conducted to validate the performance of the proposed system. Compared with the traditional linear WPT system, the results show that the output voltage of the proposed nonlinear system is robust to variations of operating frequency. Under axial and radial misalignments, it is found that the power transfer efficiency of the proposed anti-misalignment system is generally higher than the linear counterpart.

Intracerebral hemorrhage (ICH) is a common disease which has characteristics of high disability rate and high mortality rate. Rapid detection and continuous monitoring is crucial for diagnosis and treatment of ICH. As a potential technique, electrical impedance tomography (EIT) offers an alternative for brain imaging. With this technique, an image representing conductivity distribution variation is reconstructed and physiological change in the intracerebral region can be provided. However, image reconstruction is an inherently nonlinear and severely ill-posed inverse problem. To solve the problem, a convolutional neural network (CNN) and transformer combined hybrid network (CTHN) is proposed to map the nonlinear relationship between boundary measurement and conductivity distribution. With the hybrid network, high-level features of voltage measurement sequence are extracted and long-range dependencies learning ability can be improved with few layers. To evaluate the performance of the proposed method, extensive simulations are carried out on a three-layer head model with the inclusion which simulates ICH. Image reconstructions are separately performed under noiseless condition, different noise level condition, conductivity variation in three layers and deformed head models. The performance of the proposed method is also tested by the experiments. Both reconstructed images and quantitative evaluations show that ICH can be accurately reconstructed with the proposed approach.

Electrical impedance tomography (EIT) is an emerging technique for medical imaging. According to reconstructed images, the human disease can be identified. Note that the reconstruction of EIT is an ill-posed inverse problem. To solve this problem, various sensitivity-based methods have been proposed. Nevertheless, a sensitivity matrix is affected by a number of factors. Therefore, classification reflected by images is sometimes not very reliable. In this work, a novel method based on a residual neural network is proposed for stroke classification. It is realized by directly processing measured difference data from EIT. After training of the residual neural network, the proposed method is quantitatively evaluated when noise is exerted, shape deviation occurs, and conductivity varies in three different layers. Also, impacts of contact impedance and small-sized inclusion are studied. Furthermore, phantom experiments are conducted. Compared with fully connected neural networks and shallow convolution neural networks, the results demonstrate that the proposed method has a better performance in classification for various cases. This article offers an effective alternative for stroke classification in EIT.