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In Motorsports the understanding of the real engine performance within a complete circuit lap is a crucial topic. On the basis of the telemetry data the engineers are able to monitor this performance and try to adapt the engine to the vehicle's and race track's characteristics and driver's needs. However, quite often the telemetry is the sole analysis instrument for the Engine-Vehicle-Driver (EVD) system and it has no prediction capability. The engine optimization for best lap-time or best fuel economy is therefore a topic which is not trivial to solve, without the aid of suitable, reliable and predictive engineering tools. A complete EVD model was therefore built in a GT-SUITE™ environment for a Motorsport racing car (STCC-VW-Scirocco) equipped with a Compressed Natural Gas (CNG) turbocharged S.I. engine and calibrated on the basis of telemetry and test bench data.

Vehicle dynamics estimation has been the subject of study for some years now. If on-board vehicle control systems can be provided with information such as side slip angle, lateral force etc. then stability of the vehicle can be improved. To estimate these dynamic variables different observers have been used e.g., sliding mode, fuzzy logic, neural networks etc. In this article the authors propose an extended Kalman filter to estimate vehicle side slip angle. Roll angle is estimated using vertical loads as input. First, a linear Kalman filter is used to filter out the vertical forces and estimate roll angle. This information is then used to estimate the vehicle side slip angle. To take into account the nonlinearities concerning lateral vehicle dynamics, Pacejka magic formula is used to model lateral forces. Estimated results are then compared with simulations, showing good accuracy.

Active Roll Control (ARC) is one of the most promising active systems to improve vehicle comfort and handling. This paper describes the simulation based procedure adopted to conceive a double-channel Active Roll Control system, characterized by the hydraulic actuation of the stabilizer bars of a sedan. The first part of the paper presents the vehicle model adopted for this activity. It is Base Model Simulator (BMS), the 14 Degrees-of-Freedom vehicle model by Politecnico di Torino. It was validated through road tests. Then the paper describes the development of the control algorithm adopted to improve the roll dynamics of the vehicle. The implemented control algorithm is characterized by a first subsystem, capable of obtaining the desired values of body roll angle as a function of lateral acceleration during semi-stationary maneuvers.

A method for estimating the sideslip angle of a Formula SAE vehicle with torque vectoring is presented. Torque vectoring introduces large tire longitudinal forces which lead to a reduction of the tire lateral forces. A novel tire model is utilized to represent this reduction of the lateral forces. The estimation is realized using an extended Kalman filter which takes in standard sensor measurements. The developed algorithm is tested by simulating slalom and figure eight maneuvers on a validated VI-CarRealTime vehicle model. Results indicate that the algorithm is able to estimate the sideslip angle of the vehicle reliably on a high friction surface track.

Simulation is becoming the fundamental tool to design the main components of a vehicle. The paper describes the shock absorber model which was implemented by the Vehicle Dynamics Research Team of Politecnico di Torino. It is a modular model which can be adopted both for mono-tube and twin-tube shock absorbers. It can be used at different levels of approximation, as a function of the kind of user and his/her targets. The main data which have to be inserted in the model are fluid properties, the basic dimensions of the component and the characteristics of the orifices of the shock absorber. An experimental test bench was conceived to obtain the diagrams plotting flow rate through an orifice of a shock absorber versus the pressure drop between input and output ports. The test rig and the procedure to perform the experimental tests and insert the results in the shock absorber model are described in detail.

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.

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.

A fuel-cell-based system's performance is mainly identified in the overall efficiency, strongly depending on the amount of power losses due to auxiliary devices to supply. In such a situation, everything that causes either a decrease of the available power output or an increment of auxiliary losses would determine a sensible overall efficiency reduction.

Given the growing concern for environmental issues, the automotive industry is working more deeply on the development of innovative technologies that reduce gas emissions and fuel consumption. Many car manufacturers have identified hybrid electric vehicles (HEV) and fuel cell vehicles as the most promising solutions alternatives. IDRApegasus is a fuel cell hydrogen vehicle developed at the Politecnico of Turin. It participated at the Shell Eco-marathon Europe in Rotterdam (Netherlands) from 17-19 May 2012, a competition for low energy consumption vehicles and also an educational project that joins the value of sustainable development with a vehicle that will use the smallest amount of fuel and produce the lowest emissions possible.

Electric vehicles comprising multiple motors allow the individual wheel torque allocation, i.e. torque-vectoring. Powertrain configurations with multiple motors provide additional degree of freedom to improve system level efficiencies while ensuring handling performances and active safety. However, most of the works available on this topic do not simultaneously optimize both vehicle dynamic performance and energy efficiency while considering the real-time implementability of the controller. In this work, a new and systematic approach in designing, modeling, and simulating the main layers of a torque-vectoring control framework is introduced. The high level control combines the actions of an adaptive Linear Quadratic Regulator (A-LQR) and of a feedforward controller, to shape the steady-state and transient vehicle response by generating the reference yaw moment. A novel energy efficient torque allocation method is proposed as a low level controller.

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.

The superior mobility of a military vehicle provides the combat crew with a tactical advantage through increased cross country speed. The suspension system plays a fundamental role in evaluating a vehicle mobility. A mathematical model that allows realistic simulations of vehicles operating in a wide spectrum of environmental conditions may help to lower costs and time required during their development. The paper concerns with vehicle-terrain interaction modeling, for a military tracked tank, through multi-body and block-oriented approaches. It is focused on the consequences that the suspension system has got on the comfort and on the performance. Thus through a multi-body software a realistic three dimensional model of a tracked fighting vehicle is developed. This virtual model confirms some experimental data available on its longitudinal dynamics. In order to simplify the multi-body simulations, a block-oriented approach is adopted to develop a model of the same vehicle.

The production of multi-mode power-split hybrid vehicles has been implemented for some years now and it is expected to continually grow over the next decade. Control strategy still represents one of the most challenging aspects in the design of these vehicles. Finding an effective strategy to obtain the optimal solution with light computational cost is not trivial. In previous publications, a Power-weighted Efficiency Analysis for Rapid Sizing (PEARS) algorithm was found to be a very promising solution. The issue with implementing a PEARS technique is that it generates an unrealistic mode-shifting schedule. In this paper, the problematic points of PEARS algorithm are detected and analyzed, then a solution to minimize mode-shifting events is proposed. The improved PEARS algorithm is integrated in a design methodology that can generate and test several candidate powertrains in a short period of time.

As technology evolves, the number of sensors and available data on vehicles grow exponentially. In this context, it is essential to use sensors for monitoring key components, increasing safety and reliability, and gathering data useful for mechanical dimensioning and control systems. This paper presents an application of strain-gauged bolts on brake calipers fixation of two electric vehicles. With this approach it was possible to evaluate the loads applied to the brake pads fixation zone and correlate them with braking behavior, therefore gaining insights on braking conditions and system state for an improved braking function control. The goal of the study is analyzing the strengths and limitations of the method and proposing developments to deploy it in real applications. This is particularly important and novel for electric vehicles, where powertrains can create positive/negative torques and generate complex interactions with braking system.

This paper presents the design and experimental validation of a localization method for autonomous driving. The investigated method proposes and compares the application of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) to the sensor fusion of onboard data streaming from a Global Positioning System (GPS) sensor and an Inertial Navigation System (INS). In the paper, the design of the hardware layout and the proposed software architecture is presented. The method is experimentally validated in real time by using a properly instrumented all-wheel-drive electric racing vehicle and a compact Sport Utility Vehicle (SUV). The proposed algorithm is deployed on a high-performance computing platform with an embedded Graphical Processing Unit that is mounted on board the considered vehicles.

An Electronic Stability Program (ESP) control logic is designed. It is devoted to stabilize vehicle during cornering maneuvers. The aim of the activity is to obtain a feed forward (FF) control structure, capable of better performance than a previously developed closed loop one. The efficiency of ESP intervention is determined observing yaw rate peak reduction and oscillation damping time during step steer maneuver, together with vehicle side slip angle containment and longitudinal speed loss. A single track vehicle model is used to obtain two transfer functions describing vehicle and active system behavior. A third transfer function is derived from active vehicle frequency response that is the designer's target. The interaction between the transfer functions permits to design a feed forward control logic, which is then merged in a closed loop control structure in order to ensure fail safe conditions and control robustness.

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.

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.

The paper discusses a gearbox design method based on an optimization algorithm coupled to a fully integrated tool to draw 3D virtual models, in order to verify both functionality and design. The aim of this activity is to explain how the state of the art of the gear design may be implemented through an optimization software for the geometrical parameters selection of helical gears of a manual transmission, starting from torque and speed time histories, the required set of gear ratios and the material properties. This approach can be useful in order to use either the experimental acquisitions or the simulation results to verify or design all of the single gear pairs that compose a gearbox. Genetic algorithm methods are applied to solve the optimization problems of gears design, due to their capabilities of managing objective functions discontinuous, non-differentiable, stochastic, or highly non-linear.

Antilock Braking System (ABS) is designed to prevent wheels from locking, in order to enhance vehicle directional stability during braking manoeuvres. Basically, ABS closed-loop control logic uses tyres slip as control variable. Slip is estimated by comparing vehicle reference speed with the angular speed of each wheel. Thus it is crucial to correctly estimate the longitudinal vehicle speed, in order to get a control system capable of good performance. The control is also affected by road condition; since vehicles are not equipped with sensors able to measure the tyre/road friction coefficient, an other estimation has to be performed. The paper presents an algorithm for the estimation of longitudinal speed, based on the measurements of the four wheel angular speed. A method to assess the road friction, commonly known as “learning phase” is also described: it is carried out during the early stage of the active control intervention and relies on the wheel rotation sensors as well.