Converter-Based Architectures for Precision and Intelligent Current Sensing

Dear Colleagues,

The increasing demand for high-precision and robust current sensing in applications ranging from electric vehicles and renewable energy to industrial motor control is driving a fundamental shift away from traditional analog methods—approaches long constrained by poor noise immunity, performance drift, and error accumulation. Converter-based architectures, which place signal converters like Analog-to-Digital Converters (ADCs), Digital-to-Analog Converters (DACs), and Time-to-Digital Converters (TDCs) at the core of the measurement system, are emerging as the key solution. These paradigms provide a direct path to the digital domain, unlocking unprecedented opportunities for enhanced performance, digital calibration, sophisticated signal processing, and the creation of truly intelligent sensing systems.

This Special Issue aims to gather original research on the latest research advances, including novel architectures, enabling technologies, and key challenges in the field of converter-based current sensing. We invite high-quality contributions that explore how these technologies are redefining the boundaries of performance and functionality in current measurement.

Potential topics include, but are not limited to, the following:

• High-performance ADC architectures (e.g., Delta-Sigma, SAR) specifically designed for current sensing.
• Novel current-sensing techniques based on Time-to-Digital Converters (TDCs) or Voltage-to-Frequency Converters (VFCs).
• Fully integrated and isolated current-sensing Systems-on-Chip (SoCs).
• Digital calibration and compensation techniques using DACs for offset, gain, and temperature drift correction.
• Converter-based current-sensing solutions for specific applications (e.g., battery management systems (BMSs), motor drives, digital power supplies, IoT).
• Advanced signal processing and digital filtering algorithms for digitized current signals.
• Co-design and modeling of current sensors (shunts, Hall-effect, etc.) with converter interfaces.
• AI- and Machine Learning-enhanced intelligent current-sensing systems.
• Ultra-low-power converter-based architectures for energy-constrained applications.

Dr. Yangxiao Xiang
Dr. Hongjian Lin
Dr. Yuanxi Chen
Guest Editors

Dr. Zhihao Lin
Guest Editor Assistant

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