Analysis of the Effect of an IoT Monitoring System on the Rate of Voltage Decline in 18650 Li-Ion Batteries Using Deep-Sleep and Non-Deep-Sleep Strategies
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IoT-based monitoring systems often rely on batteries as their main power source; however, continuous data acquisition and transmission can accelerate battery voltage drop, thereby reducing operational lifespan. This research analyzes the effect of an Internet of Things (IoT)-based monitoring system on the voltage drop rate of 18650 Li-ion batteries, and compares the characteristics of voltage drop in deep-sleep and non-deep-sleep operating modes using a comparative quantitative experimental approach. The ESP32-based monitoring system was tested under three operating conditions: baseline (without a monitoring system), deep-sleep, and non-deep-sleep. Battery voltage measurements were carried out periodically over a predetermined observation duration. The results show that the IoT monitoring system affects the characteristics of battery voltage drop, and that different device operating modes result in different voltage drop rates. The non-deep-sleep condition exhibits the fastest voltage drop, while the deep-sleep strategy is able to reduce the rate of voltage drop more effectively than continuous operation. These findings indicate that the deep-sleep strategy contributes to improved energy efficiency in battery-based monitoring systems and may represent a more appropriate approach to slowing the rate of battery discharge, supporting the development of more energy-efficient and reliable IoT systems.
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