Dynamic Optimization of Battery Health in IoT Networks

The reliability and maintainability of the Internet of Things (IoT) devices become highly important as the number of "things" grows rapidly. The majority of the IoT devices have batteries which age, degrade, and eventually require maintenance. Existing work focuses on ensuring that batteries have sufficient amount of stored charge to operate until they can recharge, but does not consider battery degradation. This leads to high replacement and maintenance costs in large IoT networks. In this paper, we formulate the problem of minimizing battery degradation to improve the lifetime of IoT networks and solve it with Model Predictive Control (MPC) leveraging models for battery dynamics and State of Health (SoH). The battery SoH is modeled using a realistic non-linear model while taking ambient temperature into account. We demonstrate that our solution can improve network lifetime up to 68.5% compared to conventional energy consumption focused algorithms, which use simple linear battery models. The proposed approach achieves near-optimal performance in terms of preserving battery health, staying within 8.7% SoH with respect to an ideal oracle solution on average.