This paper presents a control strategy for multi-actuated vehicle for controlling the vehicle lateral dynamics. The controller tries to couple torque vectoring control, which is possible thanks to independent electric motors, with four wheels steering system by analyzing the vehicle behavior in the phase plane. A performance index is then defined to weight the two actuators intervention. The controller is tested in simulation environment where several typical maneuvers were simulated.

The idea behind the active kinematics suspension is to enhance its performance of vehicle dynamics. This includes improve steady and dynamic limit stability and faster transient reaction through optimized lateral and longitudinal dynamics. The driver’s benefits are: improved safety and higher driving pleasure. To achieve more control over the position of the rear wheels and thus the tire contact patch on the ground, the active suspension introduces one independent linear actuator at each rear wheel that controls the wheels’ camber freely. This paper will present the vehicle dynamics control logic methodology of a rear active vehicle suspension implementing the Milliken Moment Method (MMM) diagram to improve the vehicle stability and controllability, achieving gradually the front and rear axle limits. A Multibody vehicle model has been used to achieve a high fidelity simulation to generate the Milliken Moment Diagram (MMD) also known as the CN-AY diagram, where the vehicle’s yaw moment coefficient (CN) about the CG versus its lateral acceleration (AY) is mapped for different vehicle sideslip angle and steering wheel angles. With the Moment Method computer program it is possible to create the limit of the diagram over the full range of steering wheel angle and side slip angle for numerous changes in vehicle configuration of rear camber wheels and operating conditions. The vehicle dynamics control logic uses the maps like a vehicle maneuvering area under different vehicle active configurations where vehicle’s control is most fundamentally expressed as a yawing moment to quantify the directional stability.

Mathematical models simulating the handling behavior of passenger cars are extensively used at a design stage for evaluating the effects of new structural solutions or control systems. The main source of uncertainty in this type of models lies in the tyre-road interaction, due high nonlinearity. Proper estimation of tyre model parameters is thus of utter importance to obtain reliable results. A methodology aimed at identifying the Magic Formula-Tyre (MF-Tyre) model coefficients of the tyres of an axle based only on the measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed and steer angle) during standard handling maneuvers (step-steers, double lane changes, etc.) is presented in this paper. The proposed methodology is based on Particle Filtering (PF) technique. PF may become a serious alternative to classic model-based techniques, such as Kalman filters. Results of the identification procedure were first checked through simulations. Then PF was applied to experimental data collected on an real instrumented passenger-car vehicles.

The challenge to enhance the vehicle driving and handling with a state estimation and prediction system is presented by fusing a primary real time multibody vehicle model capable of providing a good indication of vehicle stability and control, and a secondary model able to estimate the vehicle state from vehicle real and virtual sensors to correct the indications of the primary model. A mathematical algorithm combines these two models in the drive control system improving the behavior of the active systems of the vehicle. A Multibody vehicle model has been used to achieve a high fidelity simulation of vehicle dynamics. The selected software is LMS.Virtual.Lab Motion with Real-Time Solver which complements the AMESim Real-Time Solver to handle complex real-time 3D-1D mechatronic systems without any simplified conceptual models. A Sensor Signal Processing Model has been developed to estimate the vehicle states and calculating tire-road contact forces and vehicle sideslip angle. The methodological approach uses the equations of motion of the chassis applying the fundamental principles of classical physics: Newtonian method and Euler angles. The control logic is based on the continuous updating of the preview multibody vehicle model by the controller sensors information network, which makes the model forecast behavior closer to the real one and improve comfort and linearity of the vehicle response. The driver inputs (throttle, steer angle and torque, brake, gear) are the same for the MBS real time model and for the real vehicle. A first training logic updates the MBS model based on the real vehicle behavior calculated by the sensor network, where the logic has to update in the MBS model just the factors depending on the vehicle itself (for example car weight, tire temperature, shock absorber damping forces, tires characteristics) and to understand and keep into account different environment variation (wet / dry surface). If the real vehicle is equipped with active control systems to improve handling and stability, as active camber control, drive by wire, ESP, Body movement active controls, the real time multibody model will interact with the models 1D or 3D of these vehicle dynamics controls and will improve their performance with a very high accuracy prediction of their influence on vehicle dynamic response. In conclusion with the help of the preview multibody vehicle model the drive control logic will increase the performance and drive ability of the vehicle with smart logic interacting with all the active systems.

A decentralized cooperative driving Non Linear Model Predictive Control (NLMPC) approach for path following and collision avoidance is presented in this paper. The proposed decentralized approach is based on an information network, which communicates when two or more vehicles are near and so they might collide. In the case in which vehicles are far, online trajectory control is independently computed on-board by means of a NLMPC. When two or more vehicles get closer, trajectory control is no more independently carried out: optimal solution for these vehicles is coupled and thus their trajectories are computed dependently. Performance of the proposed decentralized NLMPC for cooperative driving was assessed through numerical simulations involving two vehicles. Results were compared with ones of a centralized approach to assess optimality of the solution.

Existing friction laws for rubber like materials are tuned on available experimental data. Once their parameters are identified, a sensitivity analysis is carried out in order to check their extrapolation and prediction capabilities. It is seen that, although several fiction laws at micro scale are available in the literature, neither one is able to correctly predict the friction law at macro scale for all the tire working conditions. In the present paper a thorough review of the most advanced local friction models, i.e. Persson and Kluppel models, is carried out. Persson’s model is then integrated with a limiting criterion and an adhesive contribution is added to improve the prediction of the friction law at macro scale.

In last decades hybrid and electric vehicles have been one of the main object of study for automotive industry. Among the different layout of the electric power-train, four in-wheel motors appear to be one of the most attractive. This configuration in fact has several advantages in terms of inner room increase and mass distribution. Furthermore the possibility of independently distribute braking and driving torques on the wheels allows to generate a yaw moment able to improve vehicle handling (torque vectoring). In this paper a torque vectoring control strategy for an electric vehicle with four in-wheel motors is presented. The control strategy is constituted of a steady-state contribution to enhance vehicle handling performances and a transient contribution to increase vehicle lateral stability during limit manoeuvres. Performances of the control logic are evaluated by means of numerical simulations of open and closed loop manoeuvres. Robustness to friction coefficient changes is analysed.

The developed Active Front Steering (AFS) Linear Time Variant (LTV) Model Predictive Control (MPC) is a linear model predictive control based on linearization of the nonlinear vehicle model. A sensitivity analysis of the parameters of the controller is carried out on a simple path following test. Once the optimal parameters are found, both in terms of trajectory following and real-time performances, the LTV-MPC is used for determining the requirements for the necessary sensors (in terms of minimum obstacle distance detection) as a function of the vehicle speed. Then, the same analysis is carried out considering wet road conditions (i.e. the tyre-road friction coefficient is different from that accounted for by the controller).

The paper deals with a project aimed to improve the reliability of a condition monitoring system applied on gearboxes installed on rolling mills. In this context, to properly set up the algorithm, it is necessary to have measurements associated both to standard operating conditions and to malfunctioning. The latter, not being able to be experimentally generated, can be simulated by developing numerical models of the machine under varying conditions. The outputs generated, corresponding to different fault conditions associated with the main common failures of the elements that constitute the transmission, will provide a useful data base to tune the algorithm of condition monitoring.

In recent years the interest towards electric vehicles has increased. Among the different layout of the electric powertrain, four in-wheel motors appear to be one of the most attractive. This configuration in fact allows to re-design inner spaces of the vehicle and presents, as an embedded feature, the possibility of independently distributed braking and driving torques on the wheels in order to generate a yaw moment able to improve vehicle handling (torque vectoring). The present paper presents and compares two different torque vectoring control strategies for an electric vehicle with four in-wheel motors. Performances of the control strategies are evaluated by means of numerical simulations of open and closed loop maneuvers, also taking into account their energetic efficiency.

Based on experimental data, a nonlinear tire model able to predict tire contact forces as a function of slippage, slip angle, camber angle, vertical load, tire bulk and tread temperatures as well as road surface roughness and road temperature has been developed.

Active and semi-active suspension systems are widely diffused into the automotive industry and several control strategies have been proposed in the literature both concerning ride comfort and handling. The capability of several suspension active control systems in enhancing the vehicle handling performances are compared in this paper. In particular, a low-bandwidth active suspension (actuator in series with the suspension spring), an active antiroll bar, an active camber suspension and a semi-active high-bandwidth suspension (closed loop damper control) are considered. The benchmark is represented by an ideal vehicle which does not present any load transfer and has no yaw moment of inertia. The possibility of combining more than one active/semi-active suspension system is also discussed.

The design of new bobsleigh track is a challenging and difficult task since it requires several competences ranging from the physics of ice to civil engineering, from dynamics to tribology. Thus, presently, only very few companies are able to correctly design a bobsleigh track. Moreover, during the construction phase, unshared approximations, imposed by the orography and geology of the location, are adopted by the constructor. This leads to tracks that are usually characterized by unpredicted accelerations on the athletes (sometimes even above accepted standards). The following paper deals with the development of a simple though precise virtual tool for the prediction of both the speed (acceleration) profile and the trajectory of a bobsleigh able to easily account for the effects of small track profile changes. Therefore, this tool could be profitably used by both designers and constructors to avoid the necessity of “adjusting” the track once built.

Experimental testing components, innovative solutions and control strategies is essential in order to increase performances, optimize efficiency and ensure a proper safety level of a railway vehicle. These tests are usually performed directly on line, thus being very expansive. In order to reduce costs and save time during the testing/analysis phase, the use of dedicated test rigs is increasing. A roller rig for testing full scale locomotives is considered in this paper. In order to investigate the influence of the roller rig dynamics on the test bench behavior in a design stage, a numerical model of the full system (including the locomotive, the roller rig and the corresponding control systems) has been developed and a parametric analysis has been carried out.

A methodology aimed at identifying the MF-Tyre model coefficients for the steady-state pure cornering condition is presented in this paper. Only the measurements carried out on board vehicle during standard handling manoeuvres (step-steer) are considered by the identification procedure. The proposed methodology is made of three subsequent steps. During the first phase the axles cornering forces are identified through an extended Kalman filter. Then the vertical loads and the slip angles at each tire are estimated. The results of these two steps are passed as an input to the last phase, during which through a constrained minimization approach, the MF coefficients are identified. The identification procedure has been applied to the experimental data collected on an instrumented sport car.

By observing the lateral vehicle dynamics and in particular the sideslip angle, the detection of critical driving situations is possible. Thus, an adaptive observer for sideslip angle estimation is proposed in this paper. According to the proposed methodology, the sideslip angle is estimated as a weighted mean of the results provided by a kinematic formula and the ones obtained using a state observer based on a linear single-track vehicle model; tires cornering stiffness are updated during the transitory phase of a maneuver in order to take into account nonlinearities and changing of adherence conditions between tires and road.

A Multi-Body vehicle model aimed at reproducing the vertical dynamics of a light-duty commercial vehicle is presented in this paper. In order to properly model the vehicle and to identify some unknown structural parameters, experimental tests have been carried on a four-post test rig and on ordinary roads.

FSAE is a competition in which engineering students are asked to conceive, design, fabricate and compete with small, formula style, autocross racing cars ([1]). To give teams the maximum design flexibility and the freedom to express their creativity and imaginations there are very few restrictions on the overall vehicle design. DynamiΣ team (from Politecnico di Milano) has designed and optimized a new leaf spring suspension that allows to significantly reduce the weight and lower the centre of gravity of traditional suspensions that are based on linear dampers and coil springs. In fact, besides being extremely adjustable, the proposed leaf spring suspension weights a half, being made of carbon fiber and aluminum sandwich, and lowers the centre of gravity of the suspension system, being placed below the vehicle frame.