Simultaneous Power Feedback and Maximum Efficiency Point Tracking for Miniaturized RF Wireless Power Transfer Systems

Predicting Receiver Characteristics without Sensors in an LC-LC Tuned Wireless Power Transfer System Using Machine Learning

Improvement of wireless power transfer (WPT) systems is necessary to tackle issues of power transfer efficiency, high costs due to sensor and communication requirements between the transmitter (Tx) and receiver (Rx), and maintenance problems. Analytical techniques and hardware-based synchronization research for Rx-sensorless WPT may not always have been available or accurate. To address these limitations, researchers have recently employed machine learning (ML) to improve efficiency and accuracy. The objective of this work was to replace Tx-Rx communication with ML, utilizing Tx-side parameters to predict the load and coupling coefficients on an LC-LC tuned WPT system. Based on current and voltage features collected on the Tx-side for various load and coupling coefficient values, we developed two models for each load and coupling prediction. This study demonstrated that the extra trees regressor effectively predicted the characteristics of LC-LC tuned WPT systems, with coefficients of determination of 0.967 and 0.996 for load and coupling, respectively. Additionally, the mean absolute percentage errors were 0.11% and 0.017%.

A Linear-Power-Regulated Wireless Power Transfer Method for Decreasing the Heat Dissipation of Fully Implantable Microsystems

Magnetic coupling resonance wireless power transfer can efficiently provide energy to intracranial implants under safety constraints, and is the main way to power fully implantable brain-computer interface systems. However, the existing maximum efficiency tracking wireless power transfer system is aimed at optimizing the overall system efficiency, but the efficiency of the secondary side is not optimized. Moreover, the parameters of the transmitter and the receiver change nonlinearly in the power control process, and the efficiency tracking mainly depends on wireless communication. The heat dissipation caused by the unoptimized receiver efficiency and the wireless communication delay in power control will inevitably affect neural activity and even cause damage, thus affecting the results of neuroscience research. Here, a linear-power-regulated wireless power transfer method is proposed to realize the linear change of the received power regulation and optimize the receiver efficiency, and a miniaturized linear-power-regulated wireless power transfer system is developed. With the received power control, the efficiency of the receiver is increased to more than 80%, which can significantly reduce the heating of fully implantable microsystems. The linear change of the received power regulation makes the reflected impedance in the transmitter change linearly, which will help to reduce the dependence on wireless communication and improve biological safety in received power control applications.

A Fully Integrated, Power-Efficient, 0.07-2.08 mA, High-Voltage Neural Stimulator in a Standard CMOS Process

This paper presents a fully integrated high-voltage (HV) neural stimulator with on-chip HV generation. It consists of a neural stimulator front-end that delivers stimulation currents up to 2.08 mA with 5 bits resolution and a switched-capacitor DC-DC converter that generates a programmable voltage supply from 4.2 V to 13.2 V with 4 bits resolution. The solution was designed and fabricated in a standard 180 nm 1.8 V/3.3 V CMOS process and occupied an active area of 2.34 mm2. Circuit-level and block-level techniques, such as a proposed high-compliance voltage cell, have been used for implementing HV circuits in a low-voltage CMOS process. Experimental validation with an electrical model of the electrode−tissue interface showed that (1) the neural stimulator can handle voltage supplies up to 4 times higher than the technology’s nominal supply, (2) residual charge—without passive discharging phase—was below 0.12% for the whole range of stimulation currents, (3) a stimulation current of 2 mA can be delivered with a voltage drop of 0.9 V, and (4) an overall power efficiency of 48% was obtained at maximum stimulation current.

Behavioral Modeling of DC/DC Converters in Self-Powered Sensor Systems with Modelica

DC/DC converters are the essential component of power management in applications such as self-powered systems. Their simulation plays an important role in the configuration, analysis and design. A major drawback is the lack of behavioral models for DC/DC converters for long-term simulations (days or months). Available models are cycle-to-cycle-based due to the switch-mode nature of the converters and are therefore not applicable. In this work, we present a new behavioral model of a DC/DC power converter. The model is based on a thorough discussion of the model aspects that are relevant for self-powered systems, such as electrical representation and the causal connection if input and output. The model implementation is shown in the Modelica language and is available as an open-source library. The highlights of the model are a feedback controller for operation at the maximum power point (MPP), a loss-based efficiency function, and the start/stop behavior. The model’s capabilities are demonstrated in a 24h-experiment to predict voltage levels and the conversion efficiency.