Battery cycle life and throughput optimization in wireless communication system with energy harvesting capability

Abstract

This research paper proposes a novel approach to address the energy challenges faced by internet of things (IoT) devices. The wireless communication system involves a transmitter equipped with energy harvesting module that charges both a rechargeable battery and a capacitor through an energy storage management system (ESMS). This ESMS is based on a reinforcement learning algorithm to dynamically switch between the battery and the capacitor, ensuring efficient power utilization. This reinforcement learning algorithm enables the device to learn and adapt its energy consumption patterns based on environmental conditions and usage, optimizing energy usage over time. Additionally, the system employs a rainflow counting method to estimate the state-of-health (SoH) of the battery, ensuring its longevity and overall system performance. By combining these approaches, the proposed system aims to significantly improve the energy efficiency and lifespan of IoT devices, as well as the amount of data sent for different temperature ranges, ultimately enhancing their cost-effectiveness and performance.