This paper presents the analysis of two air-coupled coils used to transfer energy to charge a battery. This battery is used to power an electronic device designed to monitor variables such as impact strength, range of temperature, and humidity associated with the transport of fruits. The device is inside a sealed enclosure that cannot be opened for recharging the battery. The study shows that the coupled coils need to work with a resonance capacitor, at least on the secondary coil. However, the resonance frequency also depends on the coupling factor $k$. Therefore, this work proposes a monitoring system with a closed loop for fine-tuning the resonance frequency of the secondary coil circuit. Before starting charging the battery, the system scans the resonance frequency on the primary coil and measures the output power on the secondary coil looking for the optimal point. This procedure reduces problems of coupling factor variations with positioning of the coils during the battery charging.

Wireless power transfer is a technique usually based on an inductive link, used for delivering energy to remote devices. The power of different applications ranges from microwatts to hundreds of kilowatts, e.g., in biomedical implants and electric vehicles. The transferred power is highly dependent on the relative position between the inductive link coils. Many studies have been presented considering static or quasi-static conditions, based on a fixed tuned circuit. However, when the coils are not stationary, the inductive link must be dynamically tuned to keep the designed output power. This paper presents a methodology for dynamically tune the inductive link by means of a variable capacitance. A voltage-controlled capacitance using concepts of the negative impedance converter and capacitance multiplier is proposed. The phase angle between the input voltage and current is used as the error signal to control the variable capacitance and keep the output power operating point. The experimental evaluation shows that the proposed methodology can significantly improve the power delivered to the load in comparison to a fixed inductive link.

This paper presents the design and measurement of an inductive link for transferring energy to a fully integrated wireless power receiver. The power receiver design was focused on optimizing each factor that contributes to the link efficiency while its size was constrained to 1.5 mm × 1.5 mm in a conventional CMOS 180-nm process. On the power transmitter side, the primary inductor is printed on an FR4 board and its dimensions are selected so as to optimize its quality factor and the magnetic coupling factor. A strategy is proposed to experimentally determine the performance of the entire system. Using the proposed strategy, we measured a link efficiency of -25.4 dB at a frequency of 986 MHz, with a primary inductor of average diameter 22 mm and distance 15 mm from the receiver. Considering the characteristics of the receiver: monolithic implementation, chip area, link efficiency, and distance to the transmitter, the designed wireless power transfer system exhibits a better performance than state-of-the-art systems.

One of the most important challenges in Wireless Sensor Networks (WSN) is the extension of the sensors lifetime, which are battery-powered devices, through a reduction in energy consumption. Using data prediction to decrease the amount of transmitted data is one of the approaches to solve this problem. This paper provides a comparison of deep learning methods in a dual prediction scheme to reduce transmission. The structures of the models are presented along with their parameters. A comparison of the models is provided using different performance metrics, together with the percent of points transmitted per threshold, and the errors between the final data received by Base Station (BS) and the measured values. The results show that the model with better performance in the dataset was the model with Attention, saving a considerable amount of data in transmission and still maintaining a good representation of the measured data.

Wireless energy transfer is employed in different applications such as recharging of electrical car battery, implanted biosystems and wireless sensor network nodes. In these applications, the efficiency in the energy transfer process is a key issue, requiring careful design and correct device, circuit and system modeling. In this paper, we present accurate expressions for efficiency calculation of class-E power oscillators targeting an inductive link for wireless energy powering. The model includes the influence of the load resistance variations due to changes on the magnetic coupling between the coils of the link. Moreover, we compare the results obtained from the developed model with those obtained from simulation and measurement of a class-E power oscillator operating at 125 kHz.