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The dynamic model is built in Siemens Simcenter Amesim platform and simulates the performances on track of JUNO, a low energy demanding Urban Concept vehicle to take part in the Shell Eco-Marathon competition, in which the goal is to achieve the lowest fuel consumption in covering some laps of a racetrack, with limitations on the maximum race time. The model starts with the longitudinal dynamics, analysing all the factors that characterize the vehicle’s forward resistance, like aerodynamic forces, altimetry changes and rolling resistance. To improve the correlation between simulation and track performances, the model has been updated with the implementation of a Single-Track Model, including vehicle rotation around its roll axis, and a 3D representation of the racetrack, with an automatic trajectory following control implemented. This is crucial to characterise the vehicle’s lateral dynamics, which cannot be neglected in simulating its performances on track.

Virtual simulation is a fundamental tool for the development of new vehicles, both for individual components and for complete subsystems and full vehicles. Many software tools exist in the automotive sector to assess full-vehicle behavior and performance, including multibody software and algorithms based on 14 (or more) degrees-of-freedom vehicle dynamics models. In order to reproduce the testing maneuvers and typical vehicle mission, a key part of such simulation tools is the virtual driver algorithm. It is essential to implement a control logic that reproduces the handling response of the driver, so that the closed-loop maneuvers can be evaluated. However, the response of typical virtual drivers is not always similar to the human driving characteristics. Virtual driver algorithms can perform very fast, precise, and smooth steering and pedal actions, while humans display a more variable, delayed and often not optimal actions.

Numerical and virtual simulation of mechanical systems is a standard part of product development in the automotive sector, and multibody techniques are a consolidated tool to describe vehicle dynamics, elasto- kinematic behavior, handling, and comfort. To achieve high precision results as output of simulations, it is essential to provide the system with reliable data as input, and to accurately describe the vehicle and its subsystems. The task of gathering objective parameters to fully describe a vehicle can seem trivial to the stakeholders directly connected to a project, that can access detailed design data and a plethora of schemes and datasheets covering all subsystems of a vehicle. However, whenever this task regards benchmarking, prototyping, research projects or niche/low-volume products, data availability decreases drastically, and alternative forms of data acquisition become essential.

Aerodynamics plays a major role in the design of all kinds of vehicles throughout automotive history. Initially the main topic under investigation was the aerodynamic drag reduction to achieve high-energy efficiency, however in the late ‘60s the vertical aerodynamic forces gained traction, particularly in high performance cars. The automotive market usually treats design, aerodynamics and vehicle dynamics in different departments. This paper proposes an integrated approach for the aerodynamics development in which a sport car is defined as reference vehicle. The objective of the concurrent engineering operation is to control the aerodynamic forces by implementing active surfaces control finally improving vehicle lap time. The vehicle dynamics analysis is carried out in cooperation with vehicle aerodynamics in order to perform the hardware and software design of the active system.

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.

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.

Vehicle dynamics is a fundamental part of vehicle performance. It combines functional requirements (i.e. road safety) with emotional content (“fun to drive”, “comfort”): this balance is what characterizes the car manufacturer (OEM) driving DNA. To reach the customer requirements on Ride & Handling, integration of CAE and testing is mandatory. Beside of cutting costs and time, simulation helps to break down vehicle requirements to component level. On chassis, the damper is the most important component, contributing to define the character of the vehicle, and it is defined late, during tuning, mainly by experienced drivers. Usually 1D lookup tables Force vs. Velocity, generated from tests like the standard VDA, are not able to describe the full behavior of the damper: different dampers display the same Force vs. Velocity curve but they can give different feeling to the driver.

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.

Nowadays gas emissions and fuel consumption are two of the major challenges for the automotive industry arisen from the ever-increasing relevance of environmental issues. Over the last few years, Hybrid Electric Vehicles (HEV) and Fuel Cell Vehicles have been developed as the most promising solutions that can address these challenges. XAM (eXtreme Automotive Mobility) is a parallel hybrid electric vehicle for urban transportation developed at the Politecnico of Turin. Since 2011 it participated to the Shell Eco-marathon Europe, a competition for low consumption vehicles. In the race XAM runs within the Urban Concept category and is powered by bio-ethanol. XAM is a plug-in parallel hybrid where traction can be provided by internal combustion engine or by electric motor fed by supercapacitor. A 1D simulation model of the vehicle and its subsystems has been created in AMESim in order to predict the behavior of the vehicle during the race.

Given the ever-increasing concern about environmental issues, the automotive industry is focusing on the development of innovative technologies that allow reduction of gas emissions and fuel consumption. Over the last few years, Hybrid Electric Vehicles (HEV) and Fuel Cell Vehicles have been developed as the most promising alternative solutions for many car manufacturers. Although fuel cells are considered as the best technology to have zero emission, the impact on infrastructure for a large-scale deployment is not yet solved. For this reason, HEV represent a valid shorter-term alternative that guarantees drastic emissions reduction and reduced fuel consumption with a much lower infrastructural impact. This paper reports the results obtained by the optimization of the emissions and fuel performances of a hybrid electric city vehicle for urban transportation named XAM (eXtreme Automotive Mobility). In order to optimize these performances, a 1D model of the vehicle has been created.