Rademics Research Institute

Abstract

Artificial intelligence (AI) integration with photovoltaic and wireless charging systems is transforming the landscape of energy management for implantable and wearable medical devices. By embedding AI into these hybrid frameworks, devices gain the ability to harvest, store, and distribute energy intelligently, adapting in real time to dynamic environmental and physiological conditions. AI-powered photovoltaic energy harvesting enhances efficiency by predicting light availability and optimizing capture strategies, while AI-optimized wireless power transfer ensures safe, precise, and continuous energy delivery through adaptive alignment and scheduling. Hybrid energy management systems orchestrate multiple energy sources under AI control, maintaining uninterrupted operation and extending device longevity. Secure and reliable communication pathways, strengthened by embedded AI, protect sensitive data and enable real-time decision-making without excessive power drain. Well-designed system architecture and ultra-low-power embedded AI processors make local data processing feasible, supporting fault tolerance, modular upgrades, and personalized energy profiles. Together, these advancements pave the way for self-sustaining, minimally invasive implants that improve patient safety, comfort, and quality of life. As AI hardware and algorithms evolve, the promise of autonomous, resilient, and secure biomedical energy systems becomes increasingly attainable, marking a significant milestone in next-generation medical technology and intelligent IoT device design.

Introduction

The growing demand for intelligent, long-lasting, and minimally invasive medical devices has accelerated the development of advanced energy management systems that rely on sustainable power sources [1]. Traditional implantable devices often depend on batteries with finite lifespans, which require surgical replacement when depleted—posing risks, costs, and significant inconvenience for patients [2]. This challenge has driven researchers and engineers to explore innovative ways to extend operational life by integrating alternative power sources such as photovoltaic energy harvesting and wireless power transfer [3]. Yet, to make these technologies reliable and adaptive enough for real-world use, they must be governed by intelligent systems that can predict, control [4], and optimize energy flow continuously [5].

Artificial intelligence brings the level of adaptability and learning that these hybrid energy systems need. By embedding AI algorithms directly within the energy management architecture [6], implants and wearables can interpret data from light sensors, power modules, and usage patterns in real time [7]. This local intelligence allows the system to switch seamlessly between harvesting modes [8], optimize charging sessions, and fine-tune energy consumption based on the user’s unique physiology and daily routines. In doing so, AI not only maximizes efficiency but also reduces the need for manual intervention [9], transforming passive energy modules into active, responsive power ecosystems [10].

Photovoltaic energy harvesting alone can supply sustainable power when ambient light conditions are favorable [11], but fluctuations due to changing environments or user activities can limit its reliability [12]. Wireless power transfer complements this by delivering energy on demand [13], but its efficiency can suffer from misalignment or excessive heat generation if not carefully controlled. AI bridges this gap by orchestrating when and how each energy source is used, ensuring safe, consistent, and optimized power delivery. Predictive algorithms can schedule charging [14], adjust transmission parameters, and coordinate with other devices to balance energy needs intelligently [15].