Speaker
Description
With the upcoming demand for "Internet of Things" (IoT) devices, intensive research has been conducted on sustainable power solutions for these devices. IoT devices utilize sensors embedded in everyday objects, such as light switches, doors, and vehicles, to collect and communicate data over the internet. One promising method to power these devices is indoor photovoltaics (PVs). Next-generation indoor PVs leverage recent advancements in PV technologies, incorporating flexible, inexpensive, and sustainable materials such as molecular semiconductors. Under low light conditions, a single PV cell may not generate sufficient voltage to efficiently power an IoT device; thus, multiple PV cells are typically connected to form a mini-module. This study aims to calculate the optimal number of cells required for indoor PV modules to maximize overall IoT system efficiency under various lighting scenarios. Initially, the thermodynamic limit of a single ideal PV cell was simulated under typical indoor lighting conditions. Following this, the performance of an ideal 5 cm × 5 cm module was evaluated using finite element modelling (FEM). Voltage-dependent efficiency data from typical energy harvesting chips were employed to predict the overall energy harvesting efficiency of this module. Subsequently, leveraging data on state-of-the-art indoor PV technologies, various models were developed to calculate and compare the overall energy harvesting efficiency across several systems. The analysis revealed that the optimal number of cells for the modelled systems ranged from approximately 4 to 6 cells. However, this finding is highly dependent on the illuminance conditions under which the module is deployed, as system efficiency is significantly influenced by the voltage at the maximum power point.
