Adaptive operating mode management model for efficient energy harvesting systems

Energy harvesting technology is becoming popular concerning efficient use of Internet of Things devices, which collect energy present in nature and use it to power themselves. Although the technology is eco-friendly, it is dependent on the vagaries of the surrounding environment; the amount of energy produced is sensitive to the weather and terrain, and intermittent power threatens the system’s stability. Thus, it is essential to collect data that can determine the circumstances of the surrounding environment. Furthermore, these systems should be designed efficiently for continuous energy harvesting. This efficiency can vary depending on the system’s configuration. Core voltage levels and frequencies typically influence efficiency. To maximize system efficiency, power management with an appropriate combination of controllable factors is necessary. We design an energy harvesting system for real-time data acquisition. We propose a methodology to guide the optimal operating power stage considering various adjustable factors for efficient operation. Also, we propose an adaptive operating power mode management model, which involves selecting the optimal operating power step and the transition to a low-power mode (LPM) during idle time. The proposed model was applied to an actual energy harvesting system to demonstrate its effectiveness and facilitated the operation of the harvesting system at low power.