Battery-free digital twins: A perspective on self-powered sensing for next-generation cyber-physical systems

Abstract

Digital twin (DT) technology establishes a computational mirror of physical systems, enabling real-time monitoring, predictive analytics, and informed decision-making throughout their lifecycle. However, large-scale deployment remains constrained by reliance on wired power or batteries, leading to extensive cabling, frequent battery replacement or recharging, and labor-intensive maintenance, particularly in distributed, large-scale, or hard-to-access harsh environments. Meanwhile, advances in energy harvesting, low-power electronics, and on-chip edge computing are enabling smart systems that operate sustainably by harnessing energy from ambient environments. This paper explores how emerging technologies collectively promote the development of the next generation of battery-free digital twins. We begin by reviewing the foundations of self-powered sensing, low-power communication, and intermittent computing, and discuss the architectural shifts required to sustain reliable digital representations under fluctuating and uncertain energy availability. Key challenges may arise from energy volatility, asynchronous and sparse data acquisition, scalability across large-scale sensor networks, and the hardware-algorithm gap that restricts on-node intelligence. Yet these challenges also create opportunities to reimagine digital twins as predictive, self-adaptive, and environmentally resilient systems. By tightly coupling energy and information flows, battery-free digital twins offer a promising route toward large-scale, maintenance-free cyber-physical intelligence spanning applications from smart infrastructure and healthcare to agriculture and marine engineering.