Explore the vast range of titles available.

Sizing and energy management strategy (EMS) for a hybrid electric propulsion system (HEPS), taking into account failures, are challenging areas, especially for regional aircraft. In this paper, a failure‐based sizing method and a resilient switching‐fuzzy logic control (RSFLC) for a regional hybrid aircraft concept named AFT‐ATR42 are presented. For this purpose, the sizing procedure for the HEPS components under the failures of either the all‐turbine or the battery pack, which is equivalent to one engine inoperative (OEI) condition in fossil fuel aircraft, has been formulated. The reference battery state of charge (SOC) trajectory has then been determined based on the HEPS simulation during the flight mission. In addition, using the data generated by a combined rule‐based regulator and optimal EMS, an RSFLC is tuned by the genetic algorithm that is able to satisfy the reference SOC trajectory. Moreover, model‐in‐the‐loop results are provided to show the satisfaction of HEPS operating constraints. Furthermore, by comparing the performance of the hybrid AFT‐ATR42 and conventional aircraft, the effectiveness of the proposed RSFLC for reducing fuel consumption and emissions has been demonstrated. Finally, using the hardware‐in‐the‐loop testing, the suitable and resilient operation of the RSFLC in real‐world conditions has been confirmed. Sizing of hybrid propulsion system considering failure of hybrid propulsion energy sources. Determination of reference battery SOC trajectories during flight for emergency or safe landing of aircraft in the event of all turbine's failure. Design of resilient switching‐fuzzy energy management strategy for HEPS.

An equivalent consumption minimization strategy (ECMS) is one of the most powerful and practical ways to improve the fuel efficiency of hybrid electric vehicles (HEVs). In an ECMS, it is important to determine the optimal equivalent factor to reach a global optimal solution. The optimal equivalent factor is determined by driving conditions. Previous studies have used an adaptive ECMS (A-ECMS) to determine the appropriate equivalent factor according to changing driving conditions. An A-ECMS adjusts the equivalent factor by controlling the battery’s state of charge (SOC) to follow a reference SOC trajectory. It is therefore critical to identify a reference SOC trajectory that reflects real-world driving conditions. These conditions, which are composed of the HEV’s nonlinear dynamics and complex constraints, can be formulated into a nonlinear optimal control problem (NOCP). Here, we propose applying nonlinear programming (NLP) to an A-ECMS. The NLP-based ECMS algorithm can be divided into two parts: the use of an NLP to solve an NOCP to obtain the reference SOC trajectory and the application of an NLP solution (the result of the first part) to an A-ECMS. Simulation results demonstrate that the proposed NLP-based ECMS closely resembles a global optimal solution for dynamic programming in a relatively brief calculation time.