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The aim of this work is to apply an innovative adaptive ℒ1 techniques to control flutter phenomena affecting highly flexible wings and to evaluate the efficiency of this control algorithm and architecture by performing the following tasks: i) adaptation and analysis of an existing simplified nonlinear plunging/pitching 2D aeroelastic model accounting for structural nonlinearities and a quasi-steady aerodynamics capable of describing flutter and post-flutter limit cycle oscillations, ii) implement the ℒ1 adaptive control on the developed aeroelastic system to perform initial control testing and evaluate the sensitivity to system parameters, and iii) perform model validation and calibration by comparing the performance of the proposed control strategy with an adaptive back-stepping algorithm. The effectiveness and robustness of the ℒ1 adaptive control in flutter and post-flutter suppression is demonstrated.

Vehicle dynamics estimation has been the subject of study for some years now. If on-board vehicle control systems can be provided with information such as side slip angle, lateral force etc. then stability of the vehicle can be improved. To estimate these dynamic variables different observers have been used e.g., sliding mode, fuzzy logic, neural networks etc. In this article the authors propose an extended Kalman filter to estimate vehicle side slip angle. Roll angle is estimated using vertical loads as input. First, a linear Kalman filter is used to filter out the vertical forces and estimate roll angle. This information is then used to estimate the vehicle side slip angle. To take into account the nonlinearities concerning lateral vehicle dynamics, Pacejka magic formula is used to model lateral forces. Estimated results are then compared with simulations, showing good accuracy.

Active Roll Control (ARC) is one of the most promising active systems to improve vehicle comfort and handling. This paper describes the simulation based procedure adopted to conceive a double-channel Active Roll Control system, characterized by the hydraulic actuation of the stabilizer bars of a sedan. The first part of the paper presents the vehicle model adopted for this activity. It is Base Model Simulator (BMS), the 14 Degrees-of-Freedom vehicle model by Politecnico di Torino. It was validated through road tests. Then the paper describes the development of the control algorithm adopted to improve the roll dynamics of the vehicle. The implemented control algorithm is characterized by a first subsystem, capable of obtaining the desired values of body roll angle as a function of lateral acceleration during semi-stationary maneuvers.

Plug-in Hybrid Electric Vehicles (PHEVs) are one of the main technology options for reducing vehicle CO2 emissions and helping vehicle manufacturers (OEMs) to meet the CO2 targets set by different Governments from all around the world. In Europe OEMs have introduced a number of PHEV models to meet their CO2 target of 95 g/km for passenger cars set for the year 2021. Fuel consumption (FC) and CO2 emissions from PHEVs, however, strongly depend on the way they are used and on the frequency with which their battery is charged by the user. Studies have indeed revealed that in real life, with poor charging behavior from users, PHEV FC is equivalent to that of conventional vehicles, and in some cases higher, due to the increased mass and the need to keep the battery at a certain charging level.

The trend towards powertrain electrification is expected to grow significantly in the next future also for super-cars. The aim of this paper is therefore to assess, through numerical simulation, the impact on both fuel economy and performance of different 48 Volt mild hybrid architectures for a high-performance sport car featuring a Turbocharged Direct Injection Spark Ignition (TDISI) engine. In particular the hybrid functionalities of both a P0 (Belt Alternator Starter - BAS) and a P2 (Flywheel Alternator Starter - FAS) architecture were investigated and optimized for this kind of application through a global optimization algorithm. The analysis pointed out CO2 emission reductions potential of about 6% and 25% on NEDC, 7% and 28% on WLTC for P0 and P2 respectively. From the performance perspective, a 10% reduction in the time-to-torque was highlighted for both architectures in a load step maneuver at 2000 RPM constant speed.

This paper deals with the design and development of an innovative airship concept which is remotely-controlled and intended to be used for monitoring, surveillance, exploration and reconnaissance missions. Two potential solutions have been analyzed: the first consists of a double-hull configuration, characterized by the presence of a primary support structure connected by appropriated bindings to a couple of twin inflatable hulls. The second architecture is a soap-shaped exoskeleton configuration which features a single inflated section, incorporating two separate elements held internally by a system of ribs. The aim of this study is to analyze and compare the two configurations, to determine the most appropriate solution in terms of performance, cost and maneuvering capabilities

The current electrification trend involving hybrid and electric vehicles requires accurate tools to evaluate performance and reliability of electric powertrains’ control systems. Thanks to Hardware in the Loop (HiL) technique, verification, validation and virtual calibration of Electronic Control Systems can be performed without physical plants, addressing the need of frontloading, cost and time reduction of new vehicles control systems development. However, HiL applications with power electronics controllers brings several concerns due to the extremely low timestep needed for accurate simulation of electromagnetic phenomena, making FPGA-based simulation the only option. Moreover, thermal aspects of electric motors are very important from the control perspective as complex thermal management control strategies are implemented to improve the efficiency and to prevent overheating that can cause permanent damage to the electrical machine.

Magneto-Rheological (MR) Fluid started to be used for industrial applications in the last 20 years, and, from that moment on, innovative uses have been evaluated for different applications to exploit its characteristic of changing yield stress as a function of the magnetic field applied. Because of the complexity of the behavior of the MR fluid, it is necessary to perform lots of simulations, combining multi-physical software capable of evaluating all the material’s characteristics. The paper proposes a strategy capable of quickly verifying the feasibility of an innovative MR system, considering a sufficient accuracy of the approximation, able to easily verify the principal criticalities of the innovative applications concerning the MR fluid main electromagnetic and fluid-dynamic capabilities.

A fuel-cell-based system's performance is mainly identified in the overall efficiency, strongly depending on the amount of power losses due to auxiliary devices to supply. In such a situation, everything that causes either a decrease of the available power output or an increment of auxiliary losses would determine a sensible overall efficiency reduction.

In a military off-road vehicle, generally designed to operate in an aggressive operating environment, the typical comfort requirements for trucks and passenger cars are revised for robustness, safety and security. An example is the cabin space optimisation to provide easy access to many types of equipment required on-board. In this field, racks hung to the cabin chassis are generally used to support several electronic systems, like radios. The dynamic loads on a rack can reach high values in the operative conditions of a military vehicle. Rack failures should be prevented for the safety of driver, crew and load and the successful execution of a mission. Therefore, dynamic and durability tests of these components, including the fixtures to the vehicle, are required.

Given the growing concern for environmental issues, the automotive industry is working more deeply on the development of innovative technologies that reduce gas emissions and fuel consumption. Many car manufacturers have identified hybrid electric vehicles (HEV) and fuel cell vehicles as the most promising solutions alternatives. IDRApegasus is a fuel cell hydrogen vehicle developed at the Politecnico of Turin. It participated at the Shell Eco-marathon Europe in Rotterdam (Netherlands) from 17-19 May 2012, a competition for low energy consumption vehicles and also an educational project that joins the value of sustainable development with a vehicle that will use the smallest amount of fuel and produce the lowest emissions possible.

Sustainable and energy-efficient consumption is a main concern in contemporary society. Driven by more stringent international requirements, automobile manufacturers have shifted the focus of development into new technologies such as Hybrid Electric Vehicles (HEVs). These powertrains offer significant improvements in the efficiency of the propulsion system compared to conventional vehicles, but they also lead to higher complexities in the design process and in the control strategy. In order to obtain an optimum powertrain configuration, each component has to be laid out considering the best powertrain efficiency. With such a perspective, a simulation study was performed for the purpose of minimizing well-to-wheel CO2 emissions of a city car through electrification. Three different innovative systems, a Series Hybrid Electric Vehicle (SHEV), a Mixed Hybrid Electric Vehicle (MHEV) and a Battery Electric Vehicle (BEV) were compared to a conventional one.

New methodologies have been developed to optimize EGR rate and injection timing in diesel engines, with the aim of minimizing fuel consumption (FC) and NOx engine-out emissions. The approach entails the application of a recently developed control-oriented engine model, which includes the simulation of the heat release rate, of the in-cylinder pressure and brake torque, as well as of the NOx emission levels. The engine model was coupled with a C-class vehicle model, in order to derive the engine speed and torque demand for several driving cycles, including the NEDC, FTP, AUDC, ARDC and AMDC. The optimization process was based on the minimization of a target function, which takes into account FC and NOx emission levels. The selected control variables of the problem are the injection timing of the main pulse and the position of the EGR valve, which have been considered as the most influential engine parameters on both fuel consumption and NOx emissions.

The selection and tuning of the Fuel Injection System (FIS) are among the most critical tasks for the automotive diesel engine design engineers. In fact, the injection strongly affects the combustion phenomena through which controlling a wide range of related issues such as pollutant emissions, combustion noise and fuel efficiency becomes feasible. In the scope of the engine design optimization, the simulation is an efficient tool in order to both predict the key performance parameters of the FIS, and to reduce the amount of experiments needed to reach the final product configuration. In this work a complete characterization of a solenoid ballistic injector for a Light-Duty Common Rail system was therefore implemented in a commercially available one-dimensional computational software called GT-SUITE. The main phenomena governing the injector operation were simulated by means of three sub-models (electro-magnetic, hydraulic and mechanical).

The innovative highly flexible wings made of extremely light structures, yet still capable of carrying a considerable amount of non- structural weights, requires significant effort in structural simulations. The complexity involved in such design demands for simplified mathematical tools based on appropriate nonlinear structural schemes combined with reduced order models capable of predicting accurately their aero-structural behaviour. The model presented in this paper is based on a consistent nonlinear beam-wise scheme, capable of simulating the unconventional aeroelastic behaviour of flexible composite wings. The partial differential equations describing the wing dynamics are expanded up to the third order and can be used to explore the effect of static deflection imposed by external trim, the effect of gust loads and the one of nonlinear aerodynamic stall.

Emission requirements for diesel engines are becoming increasingly strict, leading to the increase of engine architecture complexity. This evolution requires a more systematic approach in the development of control systems than presently adopted, in order to achieve improved performances and reduction of times and costs in design, implementation and calibration. To this end, large efforts have been devoted in recent years to the application of advanced Model-Based MIMO control systems. In the present paper a new MIMO nonlinear feedback control is proposed, based on an innovative data-driven method, which allows to design the control directly from the experimental data acquired on the plant to be controlled. Thus, the proposed control design does not need the intermediate step of a reliable plant model identification, as required by Model-Based methods.

A real-time approach has been developed and assessed to control BMEP (brake mean effective pressure) and MFB50 (crank angle at which 50% of fuel mass has burnt) in a Euro 6 1.6L GM diesel engine. The approach is based on the use of feed-forward ANNs (artificial neural networks), which have been trained using virtual tests simulated by a previously developed low-throughput physical engine model. The latter is capable of predicting the heat release and the in-cylinder pressure, as well as the related metrics (MFB50, IMEP - indicated mean effective pressure) on the basis of an improved version of the accumulated fuel mass approach. BMEP is obtained from IMEP taking into account friction losses. The low-throughput physical model does not require high calibration effort and is also suitable for control-oriented applications. However, control tasks characterized by stricter demands in terms of computational time may require a modeling approach characterized by a further lower throughput.

Electric scooters are suited to mobility in zones with environmental traffic limitations, and particularly for city centers with very poor room for parking. Aim of this paper is the illustration of the performance that can be obtained from a purposely designed electric scooter. The features of the main components of the scooter driveline: battery package, converter, motor and control will be described.

Hybrid electric vehicle (HEV) powertrains are characterized by a complex design environment as a result of both the large number of possible layouts and the need for dedicated energy management strategies. When selecting the most suitable hybrid powertrain architecture at an early design stage of HEVs, engineers usually focus solely on fuel economy (directly linked to tailpipe emissions) and vehicle drivability performance. However, high voltage batteries are a crucial component of HEVs as well in terms of performance and cost. This paper introduces a multitarget assessment framework for HEV powertrain architectures which considers both fuel economy and battery lifetime. A multi-objective formulation of dynamic programming is initially presented as an off-line optimal HEV energy management strategy capable of predicting both fuel economy performance and battery lifetime of HEV powertrain layout options.

The production of multi-mode power-split hybrid vehicles has been implemented for some years now and it is expected to continually grow over the next decade. Control strategy still represents one of the most challenging aspects in the design of these vehicles. Finding an effective strategy to obtain the optimal solution with light computational cost is not trivial. In previous publications, a Power-weighted Efficiency Analysis for Rapid Sizing (PEARS) algorithm was found to be a very promising solution. The issue with implementing a PEARS technique is that it generates an unrealistic mode-shifting schedule. In this paper, the problematic points of PEARS algorithm are detected and analyzed, then a solution to minimize mode-shifting events is proposed. The improved PEARS algorithm is integrated in a design methodology that can generate and test several candidate powertrains in a short period of time.