Battery Health Based Remaining Mission Time Prediction of UAV in Closed Loop

Abstract

Unmanned Aerial Vehicles (UAVs) powered by Lithium Polymer (Li-Po) batteries are widely used for a wide spectrum of applications. Usage based discharge of their batteries can greatly impact the success of the UAV mission, hence the necessity to accurately estimate their State of Charge (SoC). The SoC estimate can, then, be used to predict the Remaining Mission Time (RMT), in order to improve the overall performance and reliability of UAVs. This paper presents a model-based prognosis algorithm to first estimate the SoC of Li-Po batteries and then to predict the RMT for a class of multirotor UAVs. Under closed loop tracking, the Linear Quadratic Tracker (LQT) with an integral action is implemented to control the UAV. The effectiveness of the developed control and the proposed algorithm is tested via simulations; obtained results demonstrate the efficacy of the method to accurately predict the RMT during closed loop performance.