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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.

Predicting the fuel economy capability of hybrid electric vehicle (HEV) powertrains by solving the related optimal control problem has been available for a few decades. Dynamic programming (DP) is one of the most popular techniques implemented to this end. Current research aims at integrating further powertrain modeling criteria that improve the fidelity level of the optimal HEV powertrain control behavior predicted by DP, thus corroborating the reliability of the fuel economy assessment. Dedicated methodologies need further development to avoid the curse of dimensionality which is typically associated to DP when increasing the number of control and state variables considered. This paper aims at considerably reducing the overall computational effort required by DP for HEVs by removing the state term associated to the battery state-of-charge (SOC).

Modern vehicles have several active systems on board such as the Electronic Stability Control. Many of these systems require knowledge of vehicle states such as sideslip angle and yaw rate for feedback control. Sideslip angle cannot be measured with the standard sensors present in a vehicle, but it can be measured by very expensive and large optical sensors. As a result, state observers have been used to estimate sideslip angle of vehicles. The current state of the art does not present an algorithm which can robustly estimate the sideslip angle for vehicles with all-wheel drive. A deep learning network based sideslip angle observer is presented in this article for robust estimation of vehicle sideslip angle. The observer takes in the inputs from all the on board sensors present in a vehicle and it gives out an estimate of the sideslip angle. The observer is tested extensively using data which are obtained from proving grounds in high tire-road friction coefficient conditions.

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

The hydrogen and fuel cell power based technologies that are rapidly emerging can be exploited to start a new generation of propulsion systems for light aircraft and small commuter aircraft. Different studies were undertaken in recent years on fuel cells in aeronautics. Boeing Research & Technology Centre (Madrid) successfully flew its converted Super Dimona in 2008 relying on a fuel cell based system. DLR flew in July 2009 with the motor-glider Antares powered by fuel cells. The goal of the ENFICA-FC project (ENvironmentally Friendly Inter City Aircraft powered by Fuel Cells - European Commission funded project coordinated by Prof. Giulio Romeo) was to develop and validate new concepts of fuel cell based power systems for more/all electric aircrafts belonging to a “inter-city” segment of the market.

The paper presents a diaphragm spring clutch linear thermal model. The main model aim was to estimate the temperature on the clutch disc slipping surfaces. That objective was pursued through a linear model to overcome the memory and computing time problems required by models with a more complex structure. The model parameters were experimentally identified. The model was validated employing a test bench, considering shift transient different as far as energy dissipated, clutch disc wear, frequency of shifting, gearbox temperature. The model structure, the methodology adopted to identify the model parameters, the experimental results obtained are presented and discussed.

The paper deals with the design and manufacturing of a McPherson suspension arm made from short glass fiber reinforced polyamide (PA66). The design of the arm and the design of the molds have been made jointly. According to Industry 4.0 paradigms, a full digitalization of both the product and process has been performed. Since the mechanical behavior of the suspension arm strongly depends on constraints which are difficult to be modelled, a simpler structure with well-defined mechanical constraints has been developed. By means of such simple structure, the model for the behavior of the material has been validated. Since the suspension arm is a hybrid structure, the associated simple structure is hybrid as well, featuring a metal sheet with over-molded polymer. The issues referring to material flow, material to material contact, weld lines, fatigue strength, high and low temperature behavior, creep, dynamic strength have been investigated on the simple structure.

A methodology for an efficient failure prediction of automotive steel wheels during fatigue experimental tests is proposed. The strategy joins the CDTire simulative package effectiveness to a specific wheel finite element model in order to deeply monitor the stress distribution among the component to predict damage. The numerical model acts as a Software-in-the-loop and it is calibrated with experimental data. The developed tool, called VirtualWheel, can be applied for the optimisation of design reducing prototyping and experimental test costs in the development phase. In the first section, the failure criterion is selected. In the second one, the conversion of hardware test-rig into virtual model is described in detail by focusing on critical aspects of finite element modelling. In conclusion, failure prediction is compared with experimental test results.

In vehicle dynamics, simple and fast vehicle models are required, especially in the framework of real-time simulations and autonomous driving software. Therefore, a trade-off between accuracy and simulation speed must be pursued by selecting the appropriate level of detail and the corresponding simplifying assumptions based on the specific purpose of the simulation. The aim of this study is to develop a methodology for map and parameter estimation from multibody simulation results, to be used for simplified vehicle modelling focused on handling performance. In this paper, maneuvers, algorithms and results of the parameter estimation are reported, together with their integration in single track models with increasing complexity and fidelity. The agreement between the multibody model, used as reference, and four single track models is analyzed and discussed through the evaluation of the correlation index.

Nautilus S.p.A. and the Polytechnic of Turin, in cooperation with Blue Engineering, have developed a very versatile product, the ELETTRA Twin Flyers [6] (ETF), which consists in a very innovative remotely-piloted airship equipped with high precision sensors and communication devices. This multipurpose platform is particularly suitable for border and maritime surveillance missions and for telecommunication, both in military and civil area. To assess the actual maneuver capabilities of the airship [14], a prototype of reduced size and complexity has been assembled [16]. Before the flight tests a further assessment on the flight simulator is needed, because the first version of the software is tuned on the full scale prototype. Steady state performance and static stability of the demonstrator have been evaluated with CFD analysis.

In the last decades, the locomotion of wheeled and tracked vehicles on soft soils has been widely investigated due to the large interest in planetary, agricultural, and military applications. The development of a tire-soft soil contact model which accurately represents the micro and macro-scale interactions plays a crucial role for the performance assessment in off-road conditions since vehicle traction and handling are strongly influenced by the soil characteristics. In this framework, the analysis of realistic operative conditions turns out to be a challenging research target. In this research work, a semi-empirical model describing the interaction between a tire and homogeneous and fine-grained soils is developed in Matlab/Simulink. The stress distribution and the resulting forces at the contact patch are based on well-known terramechanics theories, such as pressure-sinkage Bekker’s approach and Mohr-Coulomb’s failure criterion.

The problem of wing shape modification under loads in order to enhance the aircraft performance and control is continuously improving by researchers. This requirement is in contrast to the airworthiness regulations that constraint stiffness and stress of the structure in order to maintain structural integrity under operative flight conditions. The lifting surface modification is more stringent in those cases, such as UAV configurations, where the installed power is limited but the variety of operative scenario is wider than in conventional aircraft. A possible solution for adaptive wing configuration can be found in the VENTURAS Project idea. The VENTURAS Project is a funded project with the aim of improve the wind turbine efficiency by means of introducing a twisting capability for the blade sections according to the best situation in any wind condition. The blade structure is composed by two parts: 1) internal supporting element, 2) external deformable envelope.

A prototype vehicle (PV) is equipped to test powertrain and active chassis systems with innovative control strategies for safety and energy saving. Additional sensors installed on-board allow the measurement and estimation of new information useful to the vehicle dynamic control. The PV was based on a serial production passenger car with Electronic Stability Control (ESC). Testing activities on Controller Area Network (CAN) and ESC Electronic Control Unit (ECU) are carried out to compare the vehicle dynamic performance obtainable using serial production rather than customized control strategies, while maintaining the same hardware. The PV is also utilized to provide reverse engineering analysis about the implemented control strategy for the ESC working in serial production mode.

Over the next decade, CO2 legislation will be more demanding and the automotive industry has seen in vehicle electrification a possible solution. This has led to an increasing need for advanced powertrain systems and systematic model-based control approaches, along with additional complexity. This represents a serious challenge for all the OEMs. This paper describes a novel reverse engineering methodology developed to estimate relevant powertrain data required for fuel consumption-oriented hybrid electric vehicle (HEV) modelling. The estimated quantities include high-voltage battery internal resistance, electric motor and transmission efficiency, gearshift thresholds, torque converter performance diagrams, engine fuel consumption map and front/rear hydraulic brake torque distribution. This activity provides a list of dedicated experimental tests, to be carried out on road or on a chassis dynamometer, aiming at powertrain characterization thanks to a suitable post-processing algorithm.

A fast measurement of the car handling performance is highly desirable to easily compare and assess different car setup, e.g. tires size and supplier, suspension settings, etc. Instead of the expensive professional equipment normally used by car manufacturers for vehicle testing, the authors propose a low-cost solution that is nevertheless accurate enough for comparative evaluations. The paper presents a novel measuring system for vehicle dynamics analysis, which is based uniquely on the sensors embedded in a smartphone and therefore completely independent on the signals available through vehicle CAN bus. Data from tri-axial accelerometer, gyroscope, GPS and camera are jointly used to compute the typical quantities analyzed in vehicle dynamics applications.

Despite the legislation targets set by several governments of a full electrification of new light-duty vehicle fleets by 2035, the development of innovative, environmental-friendly Internal Combustion Engines (ICEs) is still crucial to be on track toward the complete decarbonization of on road-mobility of the future. In such a framework, the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) project aims at developing a C SUV-class plug-in hybrid (P0/P4) vehicle demonstrator capable to achieve a -10% fuel consumption reduction with respect to current EU6 vehicle while complying with upcoming EU7 pollutant emissions limits. Such ambitious targets will require the optimization of the whole engine system, exploiting the possible synergies among the combustion, the aftertreatment and the exhaust waste heat recovery systems.

The battery of a vehicle with an electrified powertrain (Hybrid Electric Vehicle or Battery Electric Vehicle), is required to operate with highly dynamic power outputs, both for charging and discharging operation. Consequently, the battery current varies within an extensive range during operation and the battery temperature also changes. In some cases, the relationship between the current flow and the change in the electrical energy stored seems to be affected by inefficiencies, in literature described as current losses, and nonlinearities, typically associated with the complex chemical and physical processes taking place in the battery. When calculating the vehicle electrical energy consumption over a trip, the change in the electrical energy stored at vehicle-level has to be taken into account. This quantity, what we could call the vehicle electricity balance, is typically obtained through a time-based integration of the battery current of all the vehicle batteries during operation.

Component sizing generally represents a demanding and time-consuming task in the development process of electrified powertrains. A couple of processes are available in literature for sizing the hybrid electric vehicle (HEV) components. These processes employ either time-consuming global optimization techniques like dynamic programming (DP) or near-optimal techniques that require iterative and uncertain tuning of evaluation parameters like the Pontryagin’s minimum principle (PMP). Recently, a novel near-optimal technique has been devised for rapidly predicting the optimal fuel economy benchmark of design options for electrified powertrains. This method, named slope-weighted energy-based rapid control analysis (SERCA), has been demonstrated producing results comparable to DP, while limiting the associated computational time by near two orders of magnitude.

Active tire pressure control (ATPC) is an automatic central tire inflation system (CTIS), designed, prototyped, and tested at the Politecnico di Torino, which is aimed at improving the fuel consumption, safety, and drivability of passenger vehicles. The pneumatic layout of the system and the designed solution for on board integration are presented. The critical design choices are explained in detail and supported by experimental evidence. In particular, the results of experimental tests, including the characterizations of various pneumatic components in working conditions, have been exploited to obtain a design, which allows reliable performance of the system in a lightweight solution. The complete system has been tested to verify its dynamics, in terms of actuation time needed to obtain a desired pressure variation, starting from the current tire pressure, and to validate the design.

Meticulous design of the energy management control algorithm is required to exploit all fuel-saving potentials of a hybrid electric vehicle. Equivalent consumption minimization strategy is a well-known representative of on-line strategies that can give near-optimal solutions without knowing the future driving tasks. In this context, this paper aims to propose an adaptive real-time equivalent consumption minimization strategy for a multi-mode hybrid electric powertrain. With the help of road recognition and vehicle speed prediction techniques, future driving conditions can be predicted over a certain horizon. Based on the predicted power demand, the optimal equivalence factor is calculated in advance by using bisection method and implemented for the upcoming driving period. In such a way, the equivalence factor is updated periodically to achieve charge sustaining operation and optimality.