A Hardware-Based Multi-Stage Dynamic Power Management Architecture for Autonomous Low-Light Operation

2026-05-19

AI Summary

This work addresses the challenge of high static current in photovoltaic-powered smart sensing networks under low-light conditions, which stems from conventional software-based dynamic power management. To overcome this limitation, the authors propose a hardware-centric, multi-level dynamic power management architecture that completely powers down the microcontroller and non-essential peripherals. Autonomous wake-up is achieved through coordinated operation of an ultra-low-power PMIC, a real-time clock (RTC), and a custom latching circuit. By eliminating software-induced sleep modes and relying entirely on hardware coordination, the system reduces static current to 452 nA, substantially improving energy efficiency and significantly extending the autonomous operational lifetime of sensor nodes in dim lighting environments.

Abstract

The advance of autonomous Smart Sensor Networks and embedded systems for the Internet of Things, powered by photovoltaic energy harvesting, is severely limited by energy efficiency, especially in low-light environments. While Dynamic Power Management is essential for energy conservation, conventional software-based techniques that rely on processor-managed low-power states incur a persistent quiescent current drain. This current becomes the dominant energy sink in energy-scarce conditions, limiting autonomy. The work of this paper addresses this limitation by introducing a robust, hardware-orchestrated dynamic power management architecture that improves existing configurations for battery-based sensor nodes. The proposed architecture achieves a minimal quiescent drain of 452nA, by completely power-gating the microcontroller and all non-essential peripherals, with wake-up orchestrated by an ultra-low-power PMIC, RTC and a novel latch circuit developed specifically for this work. Our evaluation demonstrates that the dynamic power management architecture is significantly more efficient than traditional software-based sleep modes.

Problem

• Dynamic Power Management
• Energy Efficiency
• Low-Light Operation
• Quiescent Current
• Autonomous Sensor Networks

Innovation

• hardware-based power management
• ultra-low-power
• power gating
• energy harvesting
• quiescent current reduction