Voltage prediction of drone battery reflecting internal temperature

Abstract

Drones are commonly used in mission-critical applications, and the accurate estimation of available battery capacity before flight is critical for reliable and efficient mission planning. To this end, the battery voltage should be predicted accurately prior to launching a drone. However, in drone applications, a rise in the battery's internal temperature changes the voltage significantly and leads to challenges in voltage prediction. In this paper, we propose a battery voltage prediction method that takes into account the battery's internal temperature to accurately estimate the available capacity of the drone battery. To this end, we devise a temporal temperature factor (TTF) metric that is calculated by accumulating time series data about the battery's discharge history. We employ a machine learning-based prediction model, reflecting the TTF metric, to achieve high prediction accuracy and low complexity. We validated the accuracy and complexity of our model with extensive evaluation. The results show that the proposed model is accurate with less than 1.5% error and readily operates on resource-constrained embedded devices.