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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.

Electric vehicles comprising multiple motors allow the individual wheel torque allocation, i.e. torque-vectoring. Powertrain configurations with multiple motors provide additional degree of freedom to improve system level efficiencies while ensuring handling performances and active safety. However, most of the works available on this topic do not simultaneously optimize both vehicle dynamic performance and energy efficiency while considering the real-time implementability of the controller. In this work, a new and systematic approach in designing, modeling, and simulating the main layers of a torque-vectoring control framework is introduced. The high level control combines the actions of an adaptive Linear Quadratic Regulator (A-LQR) and of a feedforward controller, to shape the steady-state and transient vehicle response by generating the reference yaw moment. A novel energy efficient torque allocation method is proposed as a low level controller.

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.

A challenging task that is required to modern injection systems is represented by the enhanced control of the injected quantities, especially when small injections are considered, such as, pilot and main shots in the context of multiple injections. The propagation of the pressure waves triggered by the nozzle opening and closure events through the high-pressure hydraulic circuit can influence and alter the performance of the injection apparatus. For this reason, an investigation of the injection system fluid dynamics in the frequency domain has been proposed. A complete lumped parameter model of the high-pressure hydraulic circuit has been applied to perform a modal analysis. The visualization of the main vibration modes of the apparatus allows a detailed and deep comprehension of the system dynamics. Furthermore, the possible resonances, which are induced by the action of the external forcing terms, have been identified.

As technology evolves, the number of sensors and available data on vehicles grow exponentially. In this context, it is essential to use sensors for monitoring key components, increasing safety and reliability, and gathering data useful for mechanical dimensioning and control systems. This paper presents an application of strain-gauged bolts on brake calipers fixation of two electric vehicles. With this approach it was possible to evaluate the loads applied to the brake pads fixation zone and correlate them with braking behavior, therefore gaining insights on braking conditions and system state for an improved braking function control. The goal of the study is analyzing the strengths and limitations of the method and proposing developments to deploy it in real applications. This is particularly important and novel for electric vehicles, where powertrains can create positive/negative torques and generate complex interactions with braking system.

Time-resolved mass injection rates of an outward opening piezo-actuated and a solenoid actuated multi-hole GDI injector were measured to investigate (1) the influence of both hardware and software settings and (2) the influence on the injection rates from a wide range of operational parameters and (3) discuss limitations and issues with this measurement technique. The varied operating parameters were fuel pressure, back-pressure, electrical pulse width, single/double injection and injection frequency. The varied hardware/software parameters were injector protrusion, upstream fuel pressure condition and the cut-off frequency of the software's low-pass filter. Signal quality was found to be dependent on both hardware and software settings, especially the cut-off frequency of the low-pass filter. Measurements with high signal quality were not possible for back-pressures lower than 0.5 MPa.

This paper deals with the energy efficiency of cooperative cruise control technologies when considering vehicle strings in a realistic driving environment. In particular, we design a cooperative longitudinal controller using a state-of-the-art model predictive control (MPC) implementation. Rather than testing our controller on a limited set of short maneuvers, we thoroughly assess its performance on a number of regulatory drive cycles and on a set of driving missions of similar length that were constructed based on real driving data. This allows us to focus our assessment on the energetic aspects in addition to testing the controller’s robustness. The analyzed controller, based on linear MPC, uses vehicle sensor data and information transmitted by the vehicle driving the string to adjust the longitudinal trajectory of the host vehicle to maintain a reduced inter-vehicular distance while simultaneously optimizing energy efficiency.

This paper presents the design and experimental validation of a localization method for autonomous driving. The investigated method proposes and compares the application of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) to the sensor fusion of onboard data streaming from a Global Positioning System (GPS) sensor and an Inertial Navigation System (INS). In the paper, the design of the hardware layout and the proposed software architecture is presented. The method is experimentally validated in real time by using a properly instrumented all-wheel-drive electric racing vehicle and a compact Sport Utility Vehicle (SUV). The proposed algorithm is deployed on a high-performance computing platform with an embedded Graphical Processing Unit that is mounted on board the considered vehicles.

An Electronic Stability Program (ESP) control logic is designed. It is devoted to stabilize vehicle during cornering maneuvers. The aim of the activity is to obtain a feed forward (FF) control structure, capable of better performance than a previously developed closed loop one. The efficiency of ESP intervention is determined observing yaw rate peak reduction and oscillation damping time during step steer maneuver, together with vehicle side slip angle containment and longitudinal speed loss. A single track vehicle model is used to obtain two transfer functions describing vehicle and active system behavior. A third transfer function is derived from active vehicle frequency response that is the designer's target. The interaction between the transfer functions permits to design a feed forward control logic, which is then merged in a closed loop control structure in order to ensure fail safe conditions and control robustness.

In the last years, the design in the automotive sector is mainly led by emission reduction and circular economy. To satisfy the first perspective, composites materials are being increasingly used to produce lightweight structural and semi-structural components. However, the automotive mass production arises the problem of the end-of-life disposal of the vehicle and the reduction of the wastes environmental impact. The circular economy of the composite materials has therefore become a challenge of primary importance for car manufacturers and tier 1 suppliers. It is necessary to pursue a different economic model, combining traditional raw materials with the intensive use of materials from recycling processes. New technologies are being studied and developed concerning the reuse of in-line production scraps with out-of-autoclave process that makes them desirable for high production rate applications.

In the last few years, the number of Advanced Driver Assistance Systems (ADAS) on road vehicles has been increased with the aim of dramatically reducing road accidents. Therefore, the OEMs need to integrate and test these systems, to comply with the safety regulations. To lower the development cost, instead of experimental testing, many virtual simulation scenarios need to be tested for ADAS validation. The classic multibody vehicle approach, normally used to design and optimize vehicle dynamics performance, is not always suitable to cope with these new tasks; therefore, real-time lumped-parameter vehicle models implementation becomes more and more necessary. This paper aims at providing a methodology to convert experimentally validated light commercial vehicles (LCV) multibody models (MBM) into real-time lumped-parameter models (RTM).

Lithium Ion (Li-ion) batteries have emerged as the dominant technology for electric mobility due to their performance, stability, and long cycle life. Nevertheless, there are emerging environmental and economic issues from Li-ion batteries related to depleting critical resources and their potential shortage. This paper focuses on developing the Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) of a generic Li-ion battery pack with a Nickel-Manganese-Cobalt (NMC) cathode chemistry, being the most used, and a capacity of 95 kWh as an average between different carmakers. The LCA and LCC include all the relevant phases of the life cycle of the product. The costs related to the LCC assessment have been taken as secondary data. Lastly, the same system boundary has been chosen both for the LCA and LCC.