Adaptive Maximum Effective Energy Evaluation for Lithium Battery in Hydrogen-Electric Hybrid Unmanned Aerial Vehicle Applications

Abstract

Accurate evaluation of the maximum effective energy (MEE) of the lithium battery is crucial for energy management and optimization in hydrogen-electric hybrid unmanned aerial vehicle (UAV) applications. However, uncertainties in ambient temperatures and cell aging levels could affect the MEE of the lithium battery, posing challenges for accurate evaluation. This paper presents an adaptive MEE evaluation method based on an improved sliding mode observer (SMO) for the lithium battery. The study begins by conducting comprehensive long-term experimental tests to analyze the dependencies of the lithium battery's MEE on ambient temperatures and cell aging levels. Subsequently, an SMO is developed to estimate the variations of the battery's open circuit voltage (OCV) by constructing a dynamic lithium battery model. To enhance OCV estimation accuracy, the proportional coefficient of the SMO is adjusted using a Kalman filter. Finally, real-time OCV estimation is employed to achieve adaptive MEE evaluation for the lithium battery. Experimental and simulation results verify that the proposed method could accurately evaluate the MEE of the lithium battery, even in the presence of uncertain ambient temperatures and cell aging levels.

Keywords: Hydrogen-electric hybrid unmanned aerial vehicles, Lithium battery, Maximum effective energy evaluation, Improved sliding mode observer