A power transfer model-based method for lithium-ion battery discharge time prediction of electric rotatory-wing UAV

This paper develops a power transfer model-based method to estimate real-time state of energy (SOE) and predict end of discharge (EOD) time of rotatory-wing UAVs lithium batteries under dynamic operational conditions. A discrete-time state-space model of battery is first established to model the process of battery power consumption and establish a mapping of battery unobservable state of energy (SOE) to measurable parameters such as voltage and current. Then a power consumption model of UAV is established based on predetermined flight mission of UAV using aerodynamics and momentum theory, which estimates power consumption of UAV under different operational conditions. Its calculation results can be directly used in battery state-space model as its input, while its model parameters are simultaneously updated by online measurements of consumed power. Finally, a Particle Filter (PF) approach with Adam optimization algorithm is developed to estimate SOE and predict EOD time on-line, and better prediction results compared to conventional PF are achieved. Real experiments on UAV verify the effectiveness of the proposed method.