Nonlinear Model Predictive Control-Based Optimal Energy Management for Hybrid Electric Aircraft Considering Aerodynamics-Propulsion Coupling Effects

Hybrid electric propulsion systems have been identified as feasible solutions for regional jets and narrow-body aircraft to reduce block fuel burn, emissions, operating costs. In this article, a nonlinear model predictive control-based optimal energy management scheme (MPC-EMS) has proposed minimize the burn during flight. First, artificial neural network (ANN) is adopted predict turbofan engine performance; meanwhile, gas turbine–electrical powertrain integration investigated analyzed typical conditions. Then, by combining point-mass dynamic model, MPC with cross-entropy method (CEM) obtain based on fully coupled aerodynamics-propulsion hybrid model. Besides, state-constrained control problem reformulated state-unconstrained penalty function computational load. Finally, MPC-EMS algorithm applied Boeing 737-800 mechanically parallel configuration compared optimization results using global genetic (GA)-based EMS equivalent consumption minimization strategy (ECMS). The simulation indicate that can effectively time GA-based while achieving performance only minor difference of 1.71% emissions reductions.