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