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The paper presents a method for the indoor testing of road vehicle suspension systems. A suspension is positioned on a rotating drum which is located in the Laboratory for the Safety of Transport at Politecnico di Milano. Special six-axis load cells have been designed and used for measuring the forces/moments acting at each suspension-chassis joints. The forces/moments, wheel accelerations, displacements are measured up to 100 Hz. Two different types of test can be performed. The tire/wheel unbalance effect on the suspension system behavior (Vibration and Harshness, VH) has been analyzed by testing the suspension system from zero to the vehicle maximum speed on a flat surface and by monitoring the forces transmitted to the chassis. In the second kind of test, the suspension system has been excited as the wheel passes over different cleats fixed on the drum.

A comprehensive research is presented aiming at assessing the ride comfort of subjects seated into road or off-road vehicles. Although many papers and books have appeared in the literature, many issues on ride comfort are still to be understood, in particular, the paper investigates the mutual effects of the posture and the vibration caused mostly from road unevenness. The paper is divided into two parts. In the first part, a mathematical model of a seated subject is validated by means of actual measurements on human subjects riding on a car. Such measurements refer to the accelerations acting at the subject/seat interface (vertical acceleration at the seat cushion and horizontal acceleration at the seat back). A proper dummy is used to derive the seat stiffness and damping.

This paper presents a nonlinear control approach to achieve good performances in vehicle path following and collision avoidance when the vehicle is driving under cruise highway conditions. Nonlinear model predictive control (NLMPC) is adopted to achieve online trajectory control based on a simplified vehicle model. GMRES/Continuation algorithm is used to solve the online optimization problem. Simulations show that the proposed controller is capable of tracking the desired path as well as avoiding the obstacles.

The human-robot interaction (HRI) is involved in a lift assistant system of manufacturing assembly line. The admittance model is applied to control the end effector motion by sensing intention from force of applied by a human operator. The variable admittance including virtual damping and virtual mass can improve the performance of the systems. But the tuning process of variable admittance is un-convenient and challenging part during the real test for designers, while the offline simulation is lack of learning process and interaction with human operator. In this paper, the Iterative learning algorithm is proposed to emulate the human learning process and facilitate the variable admittance control design. The relationship between manipulate force and object moving speed is demonstrated from simulation data. The effectiveness of the approach is verified by comparing the simulation results between two admittance control strategies.

Metallic foams or sponges are materials with a cell structure suitable for many industrial applications, such as reformers, heat catalytic converters, etc. The success of these materials is due to the combination of various characteristics such as mechanical strength, low density, high specific surface, good thermal exchange properties, low flow resistance and sound absorption. Different materials and manufacturing processes produce different type of structure and properties for various applications. In this work a genetic algorithm has been developed and applied to support the design of catalytic devices. In particular, two substrates were considered, namely the traditional honeycomb and an alternative open-cell foam type. CFD simulations of pressure losses and literature based correlations for the heat and mass transfer were used to support the genetic algorithm in finding the best compromise between flow resistance and pollutant abatement.

Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies.

In the last few years, the effect of diabatic test conditions on compressor performance maps has been widely investigated, leading some Authors to propose different correction models. The accuracy of turbocharger performance map constitute the basis for the tuning and validation of a numerical method, usually adopted for the prediction of engine-turbocharger matching. Actually, it is common practice in automotive applications to use simulation codes, which can either require measured compression ratio and efficiency maps as input values or calculate them “on the fly” throughout specific sub-models integrated in the numerical procedures. Therefore, the ability to correct the measured performance maps taking into account internal heat transfer would allow the implementation of commercial simulation codes used for engine-turbocharger matching calculations.

Multi-dimensional models represent today consolidated tools to simulate the combustion process in HCCI and diesel engines. Various approaches are available for this purpose, it is however widely accepted that detailed chemistry represents a fundamental prerequisite to obtain satisfactory results when the engine runs with complex injection strategies or advanced combustion modes. Yet, integrating such mechanisms generally results in prohibitive computational cost. This paper presents a comprehensive methodology for fast and efficient simulations of combustion in internal combustion engines using detailed chemistry. For this purpose, techniques to tabulate the species reaction rates and to reduce the chemical mechanisms on the fly have been coupled.

The implementation and the combination of advanced boundary conditions and subgrid scale models for Large Eddy Simulation (LES) in the multi-dimensional open-source CFD code OpenFOAM® are presented. The goal is to perform reliable cold flow LES simulations in complex geometries, such as in the cylinders of internal combustion engines. The implementation of a boundary condition for synthetic turbulence generation upstream of the valve port and of the compressible formulation of the Wall-Adapting Local Eddy-viscosity sgs model (WALE) is described. The WALE model is based on the square of the velocity gradient tensor and it accounts for the effects of both the strain and the rotation rate of the smallest resolved turbulent fluctuations and it recovers the proper y₃ near-wall scaling for the eddy viscosity without requiring dynamic procedure; hence, it is supposed to be a very reliable model for ICE simulation.

The Hybrid Cellular Automaton (HCA) algorithm is a generative design approach used to synthesize conceptual designs of crashworthy vehicle structures with a target mass. Given the target mass, the HCA algorithm generates a structure with a specific acceleration-displacement profile. The extended HCA (xHCA) algorithm is a generalization of the HCA algorithm that allows to tailor the crash response of the vehicle structure. Given a target mass, the xHCA algorithm has the ability to generate structures with different acceleration-displacement profiles and target a desired crash response. In order to accomplish this task, the xHCA algorithm includes two main components: a set of meta-parameters (in addition target mass) and surrogate model technique that finds the optimal meta-parameter values. This work demonstrates the capabilities of the xHCA algorithm tailoring acceleration and intrusion through the use of one meta-parameter (design time) and the use of Kriging-assisted optimization.

The potentialities shown by controlled differentials is making the automotive industry to explore this field. While VDC systems can only guarantee a safe behaviour at limit, a controlled differential can also increase the handling performance. The system derives from a RWD driveline with a semi-active differential, to which has been added a controlled wet clutch that directly connects the engine to the front axle. This device allows to distribute the drive torque between the two axles. It can be easily understood that in this device the torque distribution doesn't depend only from the central clutch action, but also from the engaged gear. Because of this particular layout this system can't work in the whole gear because thermal problems due to kinematical reasons. So the central clutch controller has to consider the gear position too.

Model-based control system design improves quality, shortens development time, lowers engineering cost, and reduces rework. Evaluating a control system's performance, functionality, and robustness in a simulation environment avoids the time and expense of developing hardware and software for each design iteration. Simulating the performance of a design can be straightforward (though sometimes tedious, depending on the complexity of the system being developed) with mathematical models for the hardware components of the system (plant models) and control algorithms for embedded controllers. This paper describes a software tool and a methodology that not only allows a complete system simulation to be performed early in the product design cycle, but also greatly facilitates the construction of the model by automatically connecting the components and subsystems that comprise it.

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.

The ride comfort of three Alfa Romeo cars, namely Giulietta (1955), Alfetta (1972) and 159 (2005) has been assessed both objectively and subjectively. The three cars belong to the same market segment. The aim is to let young engineers or graduate students understand how technology has evolved and eventually learn a lesson from the assessed trend. A number of cleats have been fixed at the ground and the three cars have traversed such uneven surface. The objective assessment of the ride comfort has been performed by means of accelerometers fixed at the seat rails, additionally a special dummy developed at Politecnico di Milano has been employed. The subjective assessment has been performed by a panel of passengers. The match between objective and subjective ratings is very good. Simple mathematical models have been employed to establish a (successful) comparison between experimental and computational results. The ride comfort differs substantially among the cars.

Currently, Model Based Development (MBD) is the de-facto methodology in automotive industry. This has led to conversions of legacy code to Simulink models. Our previous work was related to implementing the C2M tool to automatically convert legacy code to Simulink models. While the tool has been implemented and deployed on few OEM pilot code-sets there were several improvement areas identified w.r.t. the generated models. One of the improvement areas identified was that the generated model used atomic blocks instead of abstracted blocks available in Simulink. E.g. the generated model used an ADD block and feedback loop to represent an integration operation instead of using an integrator block directly. This reduced the readability of the model even though the functionality was correct. Thus, as a user of the model, an engineer would like to see abstract blocks rather than atomic blocks.

Styrene-Butadiene Rubber (SBR), a copolymer of butadiene and styrene, is widely used in the automotive industry due to its high durability and resistance to abrasion, oils and oxidation. Some of the common applications include tires, vibration isolators, and gaskets, among others. This paper characterizes the dynamic behavior of SBR and discusses the suitability of a visco-elastic model of elastomers, known as the Kelvin model, from a mathematical and physical point of view. An optimization algorithm is used to estimate the parameters of the Kelvin model. The resulting model was shown to produce reasonable approximations of measured dynamic stiffness. The model was also used to calculate the self heating of the elastomer due to energy dissipation by the viscous damping components in the model. Developing such a predictive capability is essential in understanding the dynamic behavior of elastomers considering that their dynamic stiffness can in general depend on temperature.

In today’s race for improved fuel economy and lower emissions from gasoline engines, precise metering of delivered fuel is essential. Gasoline Direct Injection fuel systems provide the means for improved combustion efficiency through mixture preparation and better atomization. These improvements can be achieved from both increasing fuel pressure and using multiple injection events, which significantly reduce the required energizing time per injection, and in a number of cases, force the injector to operate at less than full stroke. When the injector operates in this condition, the influence of variation in injector dynamics account for a large percentage of the delivered fuel and require compensation to ensure accurate fuel delivery. Injector dynamics such as opening delay and closing time are influenced by operating conditions such as fuel pressure, energizing time, and temperature.

Detailed chemistry represents a fundamental pre-requisite for a realistic simulation of combustion process in Diesel engines to properly reproduce ignition delay and flame structure (lift-off and soot precursors) in a wide range of operating conditions. In this work, the authors developed reduced mechanisms for n-dodecane starting from the comprehensive kinetic mechanism developed at Politecnico di Milano, well validated and tested in a wide range of operating conditions [1]. An algorithm combining Sensitivity and Flux Analysis was employed for the present skeletal reduction. The size of the mechanisms can be limited to less than 100 species and incorporates the most important details of low-temperature kinetics for a proper prediction of the ignition delay. Furthermore, the high-temperature chemistry is also properly described both in terms of reactivity and species formation, including unsaturated compounds such as acetylene, whose concentration controls soot formation.

The current development to set up an automatic procedure for automatic mesh generation and automatic mesh motion for internal combustion engine simulation in OpenFOAM®-2.2.x is here described. In order to automatically generate high-quality meshes of cylinder geometries, some technical issues need to be addressed: 1) automatic mesh generation should be able to control anisotropy and directionality of the grid; 2) during piston and valve motion, cells and faces must be introduced and removed without varying the overall area and volume of the cells, to avoid conservation errors. In particular, interpolation between discrete fields is frequent in computational physics: the use of adaptive and non-conformal meshes necessitates the interpolation of fields between different mesh regions. Interpolation problems also arise in areas such as model coupling, model initialization and visualisation.

In an earlier paper, the authors described how Model-Based System Engineering could be utilized to provide a virtual Hardware-in-the-Loop simulation capability, which creates a framework for the development of virtual ECU software by providing a platform upon which embedded control algorithms may be developed, tested, updated, and validated. The development of virtual ECU software is increasingly valuable in automotive control system engineering because vehicle systems are becoming more complex and tightly integrated, which requires that interactions between subsystems be evaluated during the design process. Variational analysis and robustness studies are also important and become more difficult to perform with real hardware as system complexity increases. The methodology described in this paper permits algorithm development to be performed prior to the availability of vehicle and control system hardware by providing what is essentially a virtual integration vehicle.