MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641127077 A) filed by Sathyabama Institute Of Science And Technology on October 26, 2026, for Deep Learning-Based Adaptive Power Management System For Mems Piezoelectric Energy Harvesters.
Inventors include Dr. T Gomathi; Dr. R Priyadharsini; Dr. R Indhu; Dr. R Bhavani; Ms. S Annie Angeline Preethi; and Dr. A Aranganathan.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: Intelligent energy harvesting and energy management techniques are needed to address the growing demand for self-powered electronic devices and energy-efficient Internet of Things (IoT) applications. Micro-Electro-Mechanical Systems (MEMS)-based piezoelectric energy harvesters hold a lot of promise for converting mechanical vibrations of the surrounding environment into electrical energy. But changes in vibration frequency, vibration acceleration, environment and electrical load could lower energy conversion efficiency and impact the stability of the harvested power. Traditional power management systems are generally based on a set of power control laws, which may be insufficient to handle the varying operating conditions. To address the above limitations, an adaptive power management system for MEMS piezoelectric energy harvesters using deep learning is proposed. The methodology is novel in that the deep learning-based energy prediction is combined with feedback- driven adaptive control to optimize energy extraction, power conditioning, and energy storage under dynamically varying vibration and load conditions, as compared to conventional fixed-parameter approaches. The input parameters, such as vibration frequency, acceleration, voltage, current and load demand are processed to assist in making intelligent control decisions and a feedback system continually adjusts the control strategy to optimize the usable energy extracted, to maintain output voltage stability and to reduce energy losses. The proposed system will be tested by simulation and experimental studies for various vibration profiles and electrical loading scenarios. It will be evaluated based on harvested power, energy utilization, power conversion efficiency, voltage regulation and response time when compared to traditional power management methods. The goal is to develop an intelligent, energy-efficient and adaptive management framework for the MEMS piezoelectric energy harvesters for wireless sensor networks, wearable electronics, structural health monitoring, or low-power IoT devices. The purpose of this research is to develop a deep learning-aided predictive control strategy that improves energy utilization and can facilitate reliable operation under varying operating and environmental conditions.
Disclaimer: Curated by HT Syndication.
