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Many research centers and companies in general aviation have been devoting efforts to the electrification of propulsive plants to reduce environmental impact and/or increase safety. Even if the final goal is the elimination of fossil fuels, the limitations of today's battery in terms of energy and power densities suggest the adoption of hybrid-electric solutions that combine the advantages of conventional and electric propulsive systems, namely reduced fuel consumption, high peak power, and increased safety deriving from redundancy. Today, lithium batteries are the best commercial option for the electrification of all means of transportation. However, lithium batteries are a family of technologies that presents a variety of specifications in terms of gravimetric and volumetric energy density, discharge and charge currents, safety, and cost.

This investigation analyzes the feasibility of using a traffic simulator, and in particular the open source software SUMO (Simulation of Urban Mobility) to reproduce speed profiles acquired under Real Driving Emission (RDE) tests. The first step of the investigation describes the experimental tests performed in Lecce to obtain RDE cycles with a Class3b vehicle. Several tests are executed with the same vehicle over the same route with the same driver. The plots of Relative Positive Acceleration versus vehicle obtained in these tests are used to tune and validate SUMO together with the qualitative speed time histories and emissions of carbon dioxide. The experimental tests also revealed the possibility to correlate CO2 emissions with either the specification of the cycle (speed and acceleration of the vehicle) or the engine working points (load and speed).

The design of a hybrid electric powertrain requires a complex optimization procedure because its performance will strongly depend on both the size of the components and the energy management strategy. The problem is particular critical in the aircraft field because of the strong constraints to be fulfilled (in particular in terms of weight and volume). The problem was addressed in the present investigation by linking an in-house simulation code for hybrid electric aircraft with a commercial many-objective optimization software. The design variables include the size of engine and electric motor, the specification of the battery (typology, nominal capacity, bus voltage), the cooling method of the motor and the battery management strategy. Several key performance indexes were suggested by the industrial partner. The four most important indexes were used as fitness functions: electric endurance, fuel consumption, take-off distance and powertrain volume.

The present study aims at the implementation of a Matlab/Simulink environment to assess the performance (thrust, specific fuel consumption, aircraft/engine mass, cost, etc.) and environmental impact (greenhouse and pollutant emissions) of conventional and more electric aircrafts. In particular, the benefits of adopting more electric solutions for either aircrafts at given missions specifications can be evaluated. The software, named PLA.N.E.S, includes a design workflow for the input of aircraft specification, kind of architecture (e.g. series or parallel) and for the definition of each component including energy converter (piston engine, turboprop, turbojet, fuel cell, etc.), energy storage system (batteries, super-capacitors), auxiliaries and secondary power systems. It is also possible to setup different energy management strategies for the optimal control of the energy flows among engine, secondary equipment and storage systems during the mission.

This investigation describes the results of an experimental and numerical research project aimed at comparing mileage and CO2 emissions from two different commercial versions of Daimler AG Smart ForTwo car: conventional (gasoline) and electric (ED). The investigation includes numerical simulations with the AVL CRUISE software package and on-board acquisitions. A data acquisition system has been designed for this purpose and assembled on board of the Smart ED. The system is composed by a GPS antenna with USB interface, two current transducers, a NI-DAQ device and a netbook computer with a LabView-VI. This system provided on-board information about driving cycle and current flows, gathered simultaneously by GPS, transducers and NI-DAQ. The system was also used to evaluate the losses of energy during the recharge of the electric car. The two cars have been tested over a wide range of driving conditions related to different routes, traffic conditions and use of on-board accessories (i.e.

An analytical methodology to efficiently evaluate design alternatives in the conversion of a Common Rail Diesel engine to either CNG dedicated or dual fuel engine has been presented in a previous investigation. The simulation of the dual fuel combustion was performed with a modified version of the KIVA3V code including a modified version of the Shell model and a modified Characteristic Time Combustion model. In the present investigation, this methodology has been validated at two levels. The capability of the simulation code in predicting the emissions trends when changing pilot specification, like injected amount, injection pressure and start of injection, and engine configuration parameters, like compression ratio and axial position of the diesel injector has been verified. The second validation was related to the capability of the proposed computer-aided procedure in finding optimal solutions in a reduced computational time.

The effect of the shape of the bowl on the combustion process and emissions of a Natural Gas - Diesel dual fuel engine is analyzed. The simulation of the dual fuel combustion is performed with a modified version of the KIVA3V code where diesel is treated as the main fuel and a further reacting specie is introduced as methane (CH4). The auto-ignition of the pilot is simulated with a modified version of the Shell model and the first stage of the combustion, related to the pilot burning process, is simulated with the Characteristic Time Combustion model. When the temperature of the mixture reaches a certain threshold, a kernel of combustion is initialized. Until the kernel reaches a nominal radius the combustion of CH4 is prevented. The combustion of CH4 is simulated with a turbulent characteristic time too. Numerical models were chosen as a compromise between accuracy and computational time.

The goal of the investigation is the evaluation of the environmental impact of hybrid and electric mobility in Lecce, a city of about 100,000 inhabitants in southern Italy. The investigation starts from the definition of specific driving cycles for the University campus and Lecce city center under different conditions of traffic and weather. The data acquired in this way are used to evaluate the performance of four typologies of vehicles: a gasoline city car (Smart Fortwo), the corresponding electric version (Smart ED), three range extenders and a plug-in hybrid electric vehicle operating with blended discharge. The simulation of the different power trains is performed with AVL-Cruise and validate through comparison with literature results on the European driving cycle.

This investigation describes the dynamic modeling of a PEM (Polymer Electrolyte Membrane) fuel cell applied to a commercial 1kW dead end anode configuration. The system is tested and validated through some initial experiments. The model allows the characterization of the polarization curve, the evaluation of cell performance in terms of efficiency and consumption and the estimation of water production. To this purpose, an experimental set-up has been created using an electronic DC load (connected to a computer by RS232 serial communication) and an NI DAQ CompactRio evaluation board. The target is studying and testing solutions to improve performance, in particular with reference to hydrogen recovery solution from the purge valve. The fuel cell model has been interfaced with a 3D race simulator that is able to reproduce the environment of the competition and the specification of the vehicle.

An adaptative energy management strategy for series hybrid electric vehicles based on optimized maps and the SUMO (Simulation of Urban MObility) predictor is presented here. The first step of the investigation is the off line optimization of the control strategy parameters (already developed by the authors) over a series of reference mini driving cycles (duration of 60s) obtained from standard driving cycles (UDDS, EUDC, etc) and realistic driving cycles acquired on the ITAN500 HEV. The optimal variables related to each mini driving cycle are stored in maps that are then implemented on the ITAN500 vehicles. When the vehicle moves, a wireless card is used to exchange information with surrounding vehicle and infrastructure. These information are used by a local instance of the SUMO traffic prediction tool (run on board) to predict the driving conditions of the HEV in the future period of time T=60s.

The aim of this work is the development of an energy-based model to predict primary break-up of urea and water (Ad Blue) sprays in condition typical of Selective Catalyst Reduction (SCR) systems. The atomization model for Ad Blue injection, developed in the present investigation, performs an energy balance of the liquid column to asses its deformation near the nozzle exit and to predict the dimension of droplets after the primary break-up. To formulate the energy balance it is necessary to evaluate several terms: the theoretical kinetic energy of the jet in absence of dissipation, its real kinetic energy, the fraction of energy dissipated due to turbulence effects, the surface energy before break-up, the surface energy of the droplets after the break-up. The model was implemented in the KIVA3v code and experimental data from Malvern analysis were used for the validation.

The optimization procedure adopted in the present investigation is based on Genetic Algorithms (GA) and allows different fitness functions to be simultaneously maximized. The parameters to be optimized are related to the geometric features of the combustion chamber, which ranges of variation are very wide. For all the investigated configurations, bowl volume and squish-to-bowl volume ratio were kept constant so that the compression ratio was the same for all investigated chambers. This condition assures that changes in the emissions were caused by geometric variations only. The spray injection angle was also considered as a variable parameter. The optimization was simultaneously performed for different engine operating conditions, i.e. load and speed, and the corresponding fitness values were weighted according to their occurrence in the European Driving Test.

The aim of the present investigation is the implementation of an innovative procedure to optimise the design of a high pressure common rail electro-injector. The optimization method is based on the use of genetic programming, a search procedure developed by John Holland at the University of Michigan. A genetic algorithm (GA) creates a random population which evolves combining the genetic code of the most capable individual of the previous generation. For the present investigation an algorithm which includes the operators of crossover, mutation and elitist reproduction has been developed. This genetic algorithm allows the optimization of both single and multicriteria problems. For the determination of the multi-objective fitness function, the concept of Pareto optimality has been implemented. The performance of the multiobjective genetic algorithm was examined by using appropriate mathematical functions and was compared with the single objective one.