Pedro Miguel Carneiro

Abstract
Motion-driven electromagnetic energy harvesting is a well-suited technological solution to autonomously power a broad range of autonomous devices. Although different harvester configurations and mechanisms have been already proposed to perform effective tuning and broadband harvesting, no methodology has proven to be effective to maximize the harvester performance for unknown and time-varying patterns of mechanical power sources externally exciting the harvesters. This paper provides, for the first time, a radically new concept of energy harvester to maximize the harvested energy for time-varying excitations: the self-adaptive electromagnetic energy harvester. This research work aims to analyze the electric energy harvesting gain when self-adaptive electromagnetic harvesters, using magnetic levitation architectures, are able to autonomously adapt their architecture as variations in the excitation patterns occur. This was accomplished by identifying the optimal harvester length for different excitation patterns and load resistances. Gains related to electric current and power exceeding 100 can be achieved for small-scale harvesters. The paper also describes comprehensive case studies to verify the feasibility of the self-adaptive harvester, considering the energy demand from the adaptive mechanism, namely the sensing, processing and actuation systems. These successful results highlight the potential of this innovative methodology to design highly sophisticated energy harvesters, both for a small- and large-scale power supply.