Drones or Unmanned Aerial Vehicles (UAV) are the aircraft controlled remotely by radio waves or autonomously and can fly without a pilot and passengers. Demands for drones with long flight times are increasing significantly in the personal and commercial applications. One of the main issues about drones is their power management. However, these devices are powered by a high energy density lithium battery, but a flight time range could be about 20–40 min. Increasing the battery energy storage capacity to achieve more flight time is not usually a good idea due to the additional weight in drones. In order to solve this issue, an Intelligent Battery Management System (IBMS) is proposed to predict the maximum available energy of the battery pack to make the best decision for finding the closest charging station depending on different weather conditions. In this study, lithium-ion battery with lithium titanite oxide (LTO) anode, as a fast charging and fast discharging battery, is used as the drone power supply. The proposed IBMS can not only increase the performance and life of the battery system but also it can estimate the battery cells state of charge (SOC) based on a system identification method. Results show that the proposed system has an accurate estimation of the maximum available energy, and therefore accurate flight time prediction to find the best recharging node for the drone.