Flight-Path Optimization for a Hybrid-Electric Aircraft

This study aims to illustrate a sequence that optimizes the flight-path trajectory for a hybrid-electric aircraft at mission level, in addition to identifying the respective optimum power management strategy. An in-house framework for hybrid-electric propulsion system modeling is utilized. A hybrid-electric commuter aircraft serves as a virtual test-bench. Vectorized calculations, decision variable count, and optimization algorithms are considered for reducing the computational time of the framework. Performance improvements are evaluated for the aircraft's design mission profile. Total energy consumption is set as the objective function. Emphasis lies on minimizing the average value and standard deviation of the energy consumption and timeframe metrics. The best performing application decreases computational time by two orders of magnitude, while retaining equal accuracy and consistency as the original model. It is employed for creating a dataset for training an artificial neural network (ANN) against random mission patterns. The trained network is integrated into a surrogate model. The latter part of the analysis evaluates optimized mission profile characteristics with respect to energy consumption, against a benchmark flight-path. The combined optimization process decreases the multihour-scale timeframe by two orders of magnitude to a 3-min sequence. Using the novel framework, a 12% average energy consumption benefit is calculated for short, medium, and long regional missions, against equivalent benchmark profiles.