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In the last years automotive industry has shown a growing interest in exploring the field of actively controlled differentials due to the promising expectations of their application for the improvement of handling performances. The differential here considered has two clutches, which connect each driveshaft with a second part that, through a gear, always rotates faster. This device allows the control system to transfer torque from one wheel to the other one, almost independently from their relative velocity. The control algorithms development has been carried out using a vehicle model that can precisely simulate the handling response, the powertrain dynamics and the actuation system behaviour. To obtain a representative powertrain response, the development of a customized library in Simulink has been required. The results of this preliminary modelling phase are shown in the first part of the paper.

In every driving condition powertrain and vehicle dynamics deeply influence each other. The main role of powertrain influence is played by the differential, which transmit the driving torque with respect to wheel kinematics. Many different solutions have been historically developed from pure mechanical devices (as open, self-locking, torque sensitive and speed sensitive) to semi-active and full-active differentials. The recently developed controlled differentials underlined the importance of a good project and tuning of this component to achieve a good performance of the vehicle, in terms of traction, stability, and, more over, drive “feeling”. This paper tries to cover the lack of present literature to provide analysis tools to be used in the preliminary phase of vehicle project in order to evaluate differential influence on vehicle performance.

Tire-road interaction is one of the main concerns in the design of control strategies for active/semi-active differentials oriented to improve handling performances of a vehicle. In particular, the knowledge of the friction coefficient at the tire-road interface is crucial for achieving the best performance in any working condition. State observers and estimators have been developed at the purpose, based on the measurements traditionally carried out on board vehicle (steer angle, lateral acceleration, yaw rate, wheels speed). However, until today, the problem of tire-road friction coefficient estimation (and especially of its maximum value) has not completely been solved. Thus, active control systems developed so far rely on a driver manual selection of the road adherence condition (anyway characterized by a rough and imprecise quality) or on a conservative tuning of the control logic in order to ensure vehicle safety among different tire-road friction coefficients.

In the last years automotive industry has shown a growing interest in exploring the field of vehicle dynamic control, improving handling performances and safety of the vehicle, and actuating devices able to optimize the driving torque distribution to the wheels. These techniques are defined as torque vectoring. The potentiality of these systems relies on the strong coupling between longitudinal and lateral vehicle dynamics established by tires and powertrain. Due to this fact the detailed (and correct) simulation of the dynamic behaviour of the driveline has a strong importance in the development of these control systems, which aim is to optimize the contact forces distribution. The aim of this work is to build an integrated vehicle and powertrain model in order to provide a proper instrument to be used in the development of such systems, able to reproduce the dynamic interaction between vehicle and driveline and its effects on the handling performances.

Aim of this study is to analyze the benefits of the measures provided by smart tyres on tyre-road friction coefficient and vehicle sideslip angle estimation. In particular, a smart tyre constituted by 2 tri-axial accelerometers glued on the tyre inner liner is considered which is able to provide the measures of the tyre-road contact forces once per wheel turn. These measures are added to the ones usually present onboard vehicle (steer angle, lateral acceleration and yaw rate) and following included into an Extended Kalman Filter (EKF) based on a single-track vehicle model. Performance of the proposed observer is evaluated on a series of handling maneuvers and its robustness to road bank angle, tyre and vehicle parameters variation is discussed.

For passenger cars, individual tyre model parameters, used in vehicle models able to simulate vehicle handling behavior, are traditionally derived from expensive component indoor laboratory tests as a result of an identification procedure minimizing the error with respect to force and slip measurements. Indoor experiments on agricultural tyres are instead more challenging and thus generally not performed due to tyre size and applied forces. However, the knowledge of their handling characteristics is becoming more and more important since in the next few years, all agricultural vehicles are expected to run on ordinary asphalt roads at a speed of 80km/h. The present paper presents a methodology to identify agricultural tyres' handling characteristics 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, J-turns, etc.), instead than during indoor tests.

In recent years, concerns for environmental pollution and oil price stimulated the demand for vehicles based on technologies alternative to traditional IC engines. Nowadays several carmakers include hybrid vehicles among their offer and first full electric vehicles appear on the market. Among the different layout of the electric power-train, four in-wheel motors appear to be one of the most attractive. Besides increasing the inner room, this architecture offers the interesting opportunity of easily and efficiently distribute the driving/braking torque on the four wheels. This characteristic can be exploited to generate a yaw moment (torque vectoring) able to increase lateral stability and to improve the handling of a vehicle. 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.

The most common automotive drivelines transmit the engine torque to the driven axle through a differential. Semi-active versions of this device ([4], [5], [6]) have been recently conceived to improve vehicle handling at limit and under particular conditions; these differentials are based on the structural scheme of the passive one but they try to manipulate the vehicle dynamics by controlling the distribution of the driving torque on the wheels of the same axle thus generating a yaw moment. Unfortunately a semi-active differential is not able to perform a complete yaw control since the torque can only be transferred from the faster wheel to the slower one; on the other hand, active differentials ([11], [12], [13]) allow to generate the most appropriate yaw moment controlling both the amount of transferred torque and its direction.

Electronic Stability Program (ESP) and Antilock Braking System (ABS) are nowadays a standard equipment for passenger cars. ESP increases vehicle safety by applying differential braking torque to the wheels while cornering, thus it extends the area of intervention of ABS which prevents the wheels from being locked up in emergency braking, especially on low friction road surfaces, allowing the driver to maintain steering control of the vehicle, to avoid obstacles and to reduce vehicle stopping distance on most road surfaces. This paper describes a flexible mechatronic test bench for ESP/ABS Electronic Control Unit (ECU) based on Hardware-In-the-Loop (HIL) simulation technique. It consists of a passenger car hydraulic braking system (from master cylinder to brake calipers), with the ESP/ABS ECU integrated and a flexible real-time platform, which simulates vehicle dynamics.