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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.

Modeling autoignition in diesel engines is a challenging task because of the wide range of equivalence ratios over which it takes place. A variety of detailed autoignition models has been proposed in literature for different fuels. Since these models include about one thousand chemical reactions and more than one hundred species, their application to CFD engines simulations requires a very high computational time, so that they are of no practical interest. In order to lower the computational time, a number of reduced models has been developed including the shell model, which is one of the most used. This model does not take into account low temperature kinetics and consists of seven reactions and three radicals. The use of this model in engine simulations shows its limits when applied to delayed injections because of the predominant influence of the low temperature kinetics. A modified version of the shell model is proposed in the present study.

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.

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.

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 Start/Stop (S/S) system is a technology that switches off the engine without the intervention of the driver when the vehicle is stopped. The goal of this device is to eliminate the consumption of fuel associated with the idling of the engine and, consequently, save carbon dioxide (CO2) and pollutant emissions. However, its effectiveness is related to the percentage of the total driving time with the vehicle stopped. Moreover, even if the S/S system is installed and the vehicle is stopped, the S/S system can be inhibited by the condition of the vehicle like, for example, a too low state of charge of the battery. This investigation evaluates the actual effect of S/S on tailpipe gaseous emissions in Real Driving Emissions tests compliant with the new European Regulations (E-RDE). The investigation is based on data from on-road and on-track RDE tests performed with a Portable Emission Measurement System on a diesel sports utility vehicle (SUV).

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.

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.