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Traditional CACC systems utilize inter-vehicle wireless communication to maintain minimal yet safe inter-vehicle distances, thereby improving traffic efficiency. However, introducing communication delays generates system uncertainties that jeopardize string stability, a crucial requirement for robust CACC performance. To address these issues, we introduce a decentralized Model Predictive Control (MPC) approach that incorporates Kalman Filters and state predictors to counteract the uncertainties posed by noise and communication delays. We validate our approach through MATLAB Simulink simulations, using stochastic and mathematical models to capture vehicular dynamics, Wi-Fi communication errors, and sensor noises. In addition, we explore the application of a Reinforcement Learning (RL)-based algorithm to compare its merits and limitations against our decentralized MPC controller, considering factors like feasibility and reliability.

In the face of growing concerns about environmental sustainability and urban congestion, the integration of eco-driving strategies has emerged as a pivotal solution in the field of the urban transportation sector. This study explores the potential benefits of a CAV functioning as a virtual eco-driving controller in an urban traffic scenario with a group of following human-driven vehicles. A computationally inexpensive and realistic powertrain model and energy management system of the Chrysler Pacifica PHEV are developed with the field experiment data and integrated into a forward-looking vehicle simulator to implement and validate an eco-driving speed planning and energy management strategy assuming longitudinal automation. The eco-driving algorithm determines the optimal vehicle speed profile and energy management strategy.

Flow noise produced by the turbulent motion of the exhaust gases is one of the main contributions to the noise generation for a heavy-duty vehicle. The exhaust system has therefore to be optimized since the early stages of the design to improve the engine’s Noise Vibration Harshness (NVH) performance and to comply with legislation noise limits. In this context, the availability of reliable Computational Aero-Acoustics (CAA) methodologies is crucial to assess the noise mitigation potential of different exhaust system designs. In the present work, a characterization of the sound generation in a heavy-duty exhaust system was carried out evaluating the noise attenuation potential of a design modification, by means of a hybrid CAA methodology.

Worldwide regulations concerning noise emissions of road vehicles are constantly demanding further reductions of acoustic emissions, which are considered a major environmental health concern in several countries. Among the different sources contributing to noise generation in vehicles equipped with internal combustion engines, exhaust flow noise is one of the most significant, being generated by turbulence development in the exhaust gases, and robust and reliable numerical methodologies for its prediction in early design phases are currently still needed. To this extent, Computational Aero-Acoustics (CAA) can be considered a valuable approach to characterize the physical mechanisms leading to flow noise generation and its propagation, and it could therefore be used to support exhaust system development prior to the execution of experimental testing campaigns.

Ride comfort assessment is undoubtedly related to the interaction between the vehicle tires and the road surface. Indeed, the road profile represents the typical input for tire vertical load estimation in durability analysis and for active/semi-active suspension controller design. However, the road profile evaluation through direct experimental measurements involves long test time and excessive cost required by professional instrumentations to detect the road irregularities with sufficient accuracy. An alternative is shifting attention towards efficient and robust algorithms for indirect road profile evaluation. The object of this work aims at providing road profile estimation starting from vehicle dynamics measurements, through accessible and traditional sensors, with the application of a linear Kalman filter algorithm.

As an era of autonomous driving approaches, it is necessary to translate handling comfort - currently a responsibility of human drivers - to a vehicle imbedded algorithm. Therefore, it is imperative to understand the relationship between perceived driving comfort and human driving behaviour. This paper develops a methodology able to generate the information necessary to study how this relationship is expressed in highway overtakes. To achieve this goal, the approach revolved around the implementation of sensor Data Fusion, by processing data from CAN, camera and LIDAR from experimental tests. A myriad of variables was available, requiring individuating the key-information and parameters for recognition, classification and understanding of the manoeuvres. The paper presents the methodology and the role each sensor plays, by expanding on three main steps: Data segregation and parameter selection; Manoeuvre detection and processing; Manoeuvre classification and database generation.

In automotive market, today more than in the past, it is very important to reduce time to market and, mostly, developing costs before the final production start. Ideally, bench and on-road tests can be replaced by multi-body studies because virtual approach guarantees test conditions very close to reality and it is able to exactly replicate the standard procedures. Therefore, today, it is essential to create very reliable models, able to forecast the vehicle behavior on every road condition (including uneven surfaces). The aim of this study is to build an accurate multi-body model of a heavy-duty truck, check its handling performance, and correlate experimental and numerical data related to comfort tests for model tuning and validation purposes. Experimental results are recorded during tests carried out at different speeds and loading conditions on a Belgian blocks track. Simulation data are obtained reproducing the on-road test conditions in multi-body environment.

Urban mobility represents one of the most critical global challenges nowadays. Several options regarding design and power sources technologies were recently proposed; among which electric and hybrid vehicles are quite successful to meet the increasingly restrictive environmental targets. This significant goal may affect the perceived vehicle comfort and drivability, especially in everyday urban scenarios. The purpose of this paper is to carry out a comparison in terms of comfort between vehicles belonging to different categories, but all designed for urban mobility: an electric 2-passenger quadricycle used during the demonstration phase of the European project STEVE, an internal combustion engine 2-passenger car (Smart Fortwo), an electric 4-passenger car (Bolloré Bluecar) and an internal combustion engine 4-passenger car (Fiat 500). Leading European car-sharing services use the last three car models.

Until 2017 in Europe the Type Approval (TA) procedure for light duty vehicles for the determination of pollutant emissions and fuel consumption was based on the New European Driving Cycle (NEDC), a test cycle performed on a chassis dynamometer. However several studies highlighted significant discrepancies in terms of CO2 emissions between the TA test and the real world, due to the limited representativeness of the test procedure. Therefore, the European authorities decided to introduce a new, up-to date, test procedure capable to closer represent real world driving conditions, called Worldwide Harmonized Light Vehicles Test Procedure (WLTP). This work aims to analyze the effects of the new WLTP on vehicle CO2 emissions through both experimental and simulation investigations on two different Euro 5 vehicles, a petrol and a diesel car, representatives of average European passenger cars.

A novel methodology, for the assessment of Dual Clutch Transmission vibrations during gear shifts, is proposed in this paper. It is based on the capability to predict through numerical simulation a typical dynamic quantity used to objectively evaluate the vibrational behavior of a gearbox during experimental tests, i.e. the acceleration of a point on the external surface of the gearbox housing. To achieve this result, a two-step approach is proposed: an accurate simulation of the internal transmission dynamics and an offline uncoupled computation of the gearbox housing acceleration from the output of the simulation. The first step required the definition of a suitable nonlinear lumped parameter model of the car equipped with a DCT that was implemented in Amesim software.

Autonomous Driving is one of the main subjects of academic research and one important trend in the automotive industry. With the advent of self-driving vehicles, the interest around trajectory planning raises, in particular when a customer-oriented analysis is performed, since more and more the carmakers will have to pay attention to the handling comfort. With that in mind, an experimental approach is proposed to assess the main characteristics of human driving and gain knowledge to enhance quality of autonomous vehicles. Focusing on overtaking maneuvers in a highway environment, four comfort indicators are proposed aiming to capture the key aspects of the chosen paths of a heterogeneous cohort. The analysis of the distribution of these indicators (peak to peak lateral acceleration, RMS lateral acceleration, Smoothness and Jerk) allowed the definition of a human drive profile.

Development trends in modern Common Rail Fuel Injection System (FIS) show dramatically increasing capabilities in terms of optimization of the fuel injection pattern through a constantly increasing number of injection events per engine cycle along with a modulation and shaping of the injection rate. In order to fully exploit the potential of the abovementioned fuel injection pattern optimization, numerical simulation can play a fundamental role by allowing the creation of a kind of a virtual injection rate generator for the assessment of the corresponding engine outputs in terms of combustion characteristics such as burn rate, emission formation and combustion noise (CN). This paper is focused on the analysis of the effects of digitalization of pilot events in the injection pattern on Brake Specific Fuel Consumption (BSFC), CN and emissions for a EURO 6 passenger car 4-cylinder diesel engine.

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.

This paper presents a novel strategy for the control of the motor torques of a rear wheel drive electric vehicle with the objective of improving the lap time of the vehicle around a racetrack. The control strategy is based upon increasing the size of the friction circle by implementing torque vectoring and tire slip control. A two-level nested control strategy is used for the motor torque control. While the outer level is responsible for computing the desired corrective torque vectoring yaw moment, the inner level controls the motor torques to realize the desired corrective torque vectoring yaw moment while simultaneously controlling the wheel longitudinal slip. The performance of the developed controller is analyzed by simulating laps around a racetrack with a non-linear multi-body vehicle model and a professional human racing driver controller setting.

The term driveability describes the driver's complex subjective perception of the interactions with the vehicle. One of them is associated to longitudinal acceleration aspects. A relevant contribution to the driveability optimization process is, nowadays, realized by means of track tests during which a considerable amount of driveline parameters are tuned in order to obtain a good compromise of longitudinal acceleration response. Unfortunately, this process is carried out at a development stage when a design iteration becomes too expensive. In addition, the actual trend of downsizing and supercharging the engines leads to higher vibrations that are transmitted to the vehicle. A large effort is therefore dedicated to develop, test and implement ignition strategies addressed to minimize the torque irregularities. Such strategies could penalize the engine maximum performance, efficiency and emissions. The introduction of the dual mass flywheel is beneficial to this end.

The use of composite materials is very important in automotive field to meet the European emission and consumption standards set for 2020. The most important challenge is to apply composite materials in structural applications not only in racing vehicles or supercars, but also in mass-production vehicles. In this paper is presented a real case study, that is the suspension wishbone arm (with convergence tie and pull-rod system) of the XAM 2.0 urban vehicle prototype, that it has the particular characteristics that of the front and rear, and left and right suspension system has the same geometry. The starting point has been an existing solution made in aluminum to manufacture a composite one.

This paper describes the optimization of the layout and of the control strategy of through-the-road (TTR) parallel hybrid electric vehicles equipped with two compression-ignition engines that feature different values of maximum output power. First, a tool has been developed to define the optimal layout of each TTR vehicle. This is based on the minimization of the powertrain and fuel cost over a 10-year time span, taking into account the fuel consumption. Several performance requirements are guaranteed during the optimization, namely maximum vehicle velocity, 0-100 km/h acceleration time, gradeability and the all-electric range. A benchmark optimizer that is based on the dynamic programming theory has been developed to identify the optimal working mode and the gear number, which are the control variables of the problem. A mathematical technique, based on the pre-processing of a configuration matrix, has been developed in order to speed up the calculation time.

In mathematical and mechanical modeling terms, automotive seating is characterized by boundary conditions at the nonlinear contact interfaces. These contact interfaces are subjected to vibro-impacts (slaps) and frictional slips. The slaps occur in contact interfaces at high amplitude vibrations, being characterized by very short duration, rapid dissipation of energy and large accelerations and decelerations. By considering friction in contact interface modeling, the simulation of the interaction between the driver and the vehicle seat becomes more realistic. Vibro-impacts and frictional slips can be simultaneously developed in a contact surface. The boundary conditions identification for a seat and a wide range of drivers' body types is performed using the concept of interference distance or penetration. The interference distance is introduced as an optimization problem. It is shown that the optimization problem provides robust solutions to minimum distance and interference problems.

The paper presents a simulation methodology created to support an integrated safety system development process which was tested for the side impact collision load case. The methodology is based on the coupled and complementary use of two software packages: PreScan and Madymo. PreScan was utilized for designing two traffic scenarios and the sensing and control systems for the side collision recognition, while Madymo was utilized for assessing the effects of pre-crash deployment of thorax airbag. The collision conditions from the scenarios were used as input to define a Madymo side collision model of the host vehicle and to investigate and optimize several airbag deployment parameters: pre-crash deployment time, airbag permeability, vent hole size and vent hole opening time.

The paper addresses some aspects of an ongoing research on a commercial compact excavator. The interest is focused on the analysis and modelling of the whole hydraulic circuit that, beside a load sensing variable displacement pump, features a stack of nine proportional directional control valves modules of which seven are of the load sensing type. Loads being sensed are the boom swing, boom, stick and bucket, right and left track motors and work tools; instead, the blade and the turret swing users do not contribute to the load sensing signal. Of specific interest are the peculiarities that were observed in the stack. In fact, to develop an accurate AMESim modelling, the stack was dismantled and all modules analysed and represented in a CAD environment as 3D parts. The load sensing flow generation unit was replaced on the vehicle by another one whose analysis and modelling have been developed using available design and experimental data.