Hybrid energy-storage system for mobile RF energy harvesting wireless sensors

Abstract

This thesis discusses the impact of the supercapacitor size on the performance of the mobile battery-less RF energy harvesting system. The choice of supercapacitor is crucial in mobile systems. The small supercapacitor can charge quickly and activate the sensor in a few seconds in the low-energy area but cannot provide a significant amount of energy to the sensor to do heavy energy tasks such as programming or communication with the base station. On the other hand, large supercapacitors have a sensor node for heavy energy tasks in a high-energy zone but may not be able to activate in a low energy zone. The proposed hybrid energy-storage system contains two supercapacitors of different sizes and a switching circuit. An adaptive-learning switching algorithm controls the switching circuit. This algorithm predicts the available source energy and the period that the sensor node will remain in the high-energy area. The algorithm dynamically switches between the supercapacitors according to available ambient RF energy. Extensive simulation and experiments evaluated the proposed method. The proposed system showed 40% and 80% efficiency over single supercapacitor system in terms of the amount of harvested energy and sensor coverage.