A rising number of aerospace manufacturers are working on the development of new solutions in the field of Urban Air Mobility with increasing attention addressing electric and hybrid electric propulsive systems. Hybrid electric propulsive systems potentially offer performance improvements during transient maneuvers, as well as sustaining the engine during flight phases characterized by high power demands. Among the challenges of hybridization in rotorcraft, there is the necessity to predict the dynamic behavior and its effect on the control of rotor shaft speed. In the present study, the dynamic behavior of a parallel hybrid electric propulsive system for a coaxial-rotor air taxi is analyzed in response to a typical sequence of pilot commands that encompasses the range of operations from hover to forward flight. The system is modeled with a dynamic approach and includes sub-models for the coaxial rotors, the turboshaft engine, the electric machine, and the battery. The results of the investigation show a better performance during transients of the hybrid system than a conventional turboshaft configuration, especially if the electric contribution to the power request is coordinated to account for the lag due to slower engine dynamic response.
The conventional powertrain for ultralight aviation consists of a fixed pitch propeller connected to an internal combustion engine (ICE). Since ICEs have a limited thermal efficiency (<40%), new and more efficient powerplant configurations have recently been proposed in the scientific literature by adopting hybrid electric solutions. Hybridization has the additional benefit of increased safety thanks to redundancy. This is a very important issue in ultralight aviation, where a high percentage of accidents are caused by engine failure. In a previous investigation, the authors proposed the design of a series/parallel hybrid electric power system to increase safety and optimize fuel economy by controlling the engine working points during flight. A new powertrain, derived from an automotive Honda i-MMD system, is analyzed in this study and a reliability analysis is performed to underline the improved safety obtained with the proposed system.
Thanks to its typical limited speeds and altitudes, Urban Air Mobility represents an interesting application for electric and hybrid-electric power systems. In addition, short-range requirements are compatible with the limited performance of today’s batteries, conversely to their current inapplicability for commercial aviation purposes. For the present study, a parallel Hybrid Electric Propulsion System for a coaxial-rotor Air Taxi has been implemented in Simulink and tested on four different sets of operating conditions, with a transient signal as input for the Power Lever Angle command. The goal of this investigation is to analyze the transient behavior of the hybrid-electric propulsion system in question, to underline the role of electric motors in assisting thermal engine during transients, and, in particular, focuses on the benefits deriving from the adoption of a coordination block which adapts torque split between the two power sources on the basis of actual engine response.
Hybrid engines are becoming more and more widespread. Electric energy instead is a valid help to reduce the environmental impact. In hybrid engines, the number of components is higher and this results in a decrease in reliability. With Engine Health Monitoring (EHM) we mean the set of techniques used to monitor the health status of a system based on the values assumed by some related parameters. Artificial Intelligence (AI) methods are widely used nowadays in this discipline. In this paper, an EHM approach was developed to monitor the health status of some components constituting an hybrid turboshaft. The dynamic model of the hybrid electric power system is described in an accompanying paper. Feed-Forward Neural Network (FFNN) is used as AI tool to built the just cited system. The engine modelled with Simulink, was used to perform a series of steady-state simulations implementing a degradation condition in some selected components. The degradation condition was simulated by changing the value of the Performance Parameters (PPs) related to each of the selected components. The results of the simulation were used to obtain a dataset useful to train the FFNN to predict the values of the same PPs in a degraded case.
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