Real-Time Predictive Energy Management Strategy for Fuel Cell-Powered Unmanned Aerial Vehicles Based on the Control-Oriented Battery Model

Abstract

The predictive energy management (PEM) problem for hybrid electric powertrains is challenging to solve in real time, mainly due to the nonconvexity from the battery state of energy (SOE) model, which is nonlinear. This letter proposes a control-oriented battery model consisting of a stochastic linear SOE model and a quadratic power loss model to realize real-time PEM. The stochastic linear model describes the SOE trajectory from an average point of view. The quadratic power loss model describes the nonlinear power loss that the stochastic linear SOE model cannot consider. By replacing the nonlinear SOE model with the control-oriented model, the PEM problem is reformulated into quadratic programming (QP), which can be easily solved in real time by a QP solver. Simulation results obtained using a fuel cell-powered unmanned aerial vehicle (UAV) show that the proposed model predicts the trend of the SOE trajectory well, even for long prediction horizons (maximum of 750 s). In addition, PEM based on the proposed model results in near-optimal performance (0.36% difference from the global solution) with real-time capability (solved within 0.27 s).