Design and validation of a DC–DC converter-based inductive power transfer system with increased efficiency and reduced voltage stress across switches

Abstract

Research in wireless power transfer(WPT) focuses on improving efficiency, reducing energy consumption, improving safety, and increasing sustainability. It prioritises developing more efficient transmission technologies, such as high-frequency switching and resonant coupling, to enhance the system’s overall performance. However, high voltage stress across the semiconductor devices during switching operations at high frequencies can result in increased switching loss, leading to decreased overall efficiency of the WPT system. The switching loss can be minimised by reducing voltage stress, resulting in higher efficiency and improved energy transfer performance. This leads to more cost-effective WPT solutions without compromising performance or reliability by using lower-rated components and reducing the need for elaborate voltage clamping and protection circuits. Lowering voltage stress across switches can also help WPT systems comply with industry standards and regulations related to electrical safety, electromagnetic compatibility (EMC), and energy efficiency. Meeting these standards is essential for ensuring the widespread adoption and commercial success of WPT technology. In this article, a class ø2 inverter and a modified class-E rectifier is integrated and applied in a WPT system. The benefits of a Resonant class ø2 inverter are zero voltage switching(ZVS) and lower switch voltage stress as compared to its predecessors. Also, a resonant class E/F3 rectifier inductively coupled in the receiving stage reduces the peak voltage stress across the diode. The system shown here is tuned to achieve maximum overall transfer efficiency. The proposed class ø2-E/F3 combined WPT system operating at 50 kHz simulated in PSIM software yielded a dc-dc efficiency of 94.56 percent, and the peak voltage stress across switches is found to reduced (2.4–2.7 times the input voltage as compared to the existing topologies where it surges upto 3.6–4 times the input voltage). These are also validated in Typhoon HIL’s Real-Time Emulator HIL402. In addition to that, a machine learning algorithm based on an Artificial Neural Network is used to generate a model to predict the switching frequency for a specified output voltage and efficiency.