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The goal of this investigation is to model a hybrid electric air-taxi and minimize its fuel consumption by on-line energy management. Urban Air Mobility (UAM) is considered as a suitable way to reduce traffic congestion and pollution as well as increase mobility in metropolitan areas. Urban air-mobility is an interesting application for electric and hybrid-electric power systems because of limited speed (compared with longer distance commuters) altitudes up to 1000ft and short-range requirements that make possible electrification even with the limited performance of today batteries. However, in case of hybrid electric propulsion systems, the fuel consumption and the environmental impact depends on the energy management. After obtaining reference values of fuel economy over four different missions with the Dynamic programming method, this investigation proposes and optimize a fuzzy logic for the on-line energy management of the hybrid vehicle for UAM in order to minimize fuel consumption and, consequently, local environmental impact.

Several studies in literature have shown how real-world emissions strongly depend on driving condition, driving style, ambient temperature and humidity, etc. so that they are significantly different from the values measured on test benches over standard driving cycles. This concern, together with the so-called Diesel-gate, has caused the introduction in Europe of an innovative procedure for the registration of vehicle based on real driving emissions (RDE) measured with a portable emission measurement system (PEMS). PEMS devices are bulky and very expensive, therefore they cannot be extensively for an actual real time monitoring of emissions. To solve this problem, the present work proposes a Neural Network model based on the interpolation of the time-histories of driving conditions (speed, altitude, ambient temperature, humidity and pressure) and emissions measured on a diesel start-and-stop vehicle while performing a series of RDE tests. Two different approaches are proposed. The first one calculates the emissions on the basis of the vehicle motion (speed and altitude profile, ambient conditions). The second one models the engine block using as input the ambient conditions, the load and the rpm of the engine as derived from the OBD-II scanner. The output of both models are the flow rates and cumulated values of CO2 and NOx. Note that the inputs of the two models are signal that can easily obtained on-board without additional sensors.

The electrification of aircraft is a well-established trend in recent years in order to achieve economic and environmental sustainability. In this framework, an application particularly interesting for hybrid electric power system is represented by urban air-mobility. For this application, the authors presented a parallel hybrid electric power system including a turboshaft engine and two electric motors and proposed a quasi-stationary simulation tool. As a further step, this paper deals with the dynamic modelling of the same turboshaft engine within the framework of a hybrid electric system where the pilot command is interpreted as a power request to be satisfied by the engine and the electric machine according to the selected energy management strategy. In this work, the dynamic behaviour of the turboshaft engine is analysed with and without the help of the electric motors to satisfy the power demand.

In a previous work, the authors optimized the hybrid electric power system for a tactical Unmanned Aerial Vehicle including a Wankel engine as thermal converter and a permanent magnet electric motor powered by lithium batteries. Startingfrom this optimal configuration, we address here the topic of a refined optimization of the energy management strategy, i.e. the contribution of the battery to the required power in each segment of the flight. The Equivalent Consumption Minimization Strategy (ECMS) was chosen with the goal of minimizing fuel consumption while fully depleting the energy stored in the battery from the beginning to the end of each mission.

This study aims at investigating the synergy between powertrain and structure within the design process of a fixed-wing tail-sitter unmanned aerial vehicle (UAV). The UAV is equipped with a pure-electric power system and has vertical take-off and landing capabilities (VTOL). The problem is addressed by running a contemporary optimization of the parameters of both the powertrain and the UAV’s structure, in order to maximize electric endurance and payload weight through the usage of a performant multi-objective evolutionary algorithm named SMS-EMOA. Three different designs are selected, discussed and compared with literature results on the same UAV to quantify the increase of payload and cruise time that can be obtained by exploiting the synergy between structure and powertrain. The potentiality of furtherly improving payload through the usage of multi-functional panels, while keeping the same endurance, is also quantified and compared with the technologies proposed in literature.