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The paper presents the today’s power train systems, which largely reflect a one to one mapping of physical units into a dedicated electronic control system. A new approach is suggested for a breakdown strategy with an ECU centered structure linked to a surrounding harness of sensors and actuators. Like body electronics did first, automotive graded combination of semiconductor and packaging technologies are used to develop a network of mechatronic components. This allows an easy and effective separation between the SW development at the vehicle level and an off-line optimization and calibration of components. A development project is shown for a gasoline direct injected engine, where mechatronic components (e.g. cylinder, fuel pump and injectors, valve train) are networked and controlled by a master digital core, which is the application SW restricted area of the car manufacturer.

The continuous advance in mechatronics has long attracted researchers towards the development of new highly integrated actuators for automotive applications. Shape Memory Alloys (SMAs) offer many favourable characteristics that make this technology very attractive for mechatronic actuators. This paper presents the design, the prototype fabrication and the functional testing of an SMA binary actuator for automotive tumble flaps. The innovative solid-state actuation system is proposed as an alternative to traditional electric or pneumatic effectors used to drive tumble flaps of an air intake manifold for internal combustion engines.

Components of an automatized transmission system were improved by using techniques of finite element numerical simulation and topology optimization, in order to achieve mass saving and higher performance. Numerical simulations have being applied more frequently during the components design, once the models become more sophisticated, higher computational capacity is available and more precise material properties can be determined. In this paper, a good correlation between the simulation models and the experimental tests was achieved through the material properties determination by means of the impulse excitation technique. This impulse excitation technique consists of a non-destructive test for the dynamic elasticity modulus and material damping through the vibration natural frequencies. The test specimens are evaluated by an impulsive mechanical excitation and the response acoustic signal is collected by a microphone and processed in a conventional computer.

A hydraulic accumulator used in gear selection phase of an automated manual transmission was studied to employ simulations which could predict response time of entire system. A spring-mass-damper mathematical model was created from real data and response time measured in acquisitions of a first prototype system built. The model objective was to determine values for damper coefficient and spring mass rate to be used in the design of a pair of springs with enough stiffness to achieve system response time desired. With prototype data and mathematical model developed, predictions about response time of a final system were performed and a spring rate value was determined to satisfy with response time requested. From model data, numerical structural analyses were performed in order to predict eventual final system failures due to the stiffness values involved.

Several projects in engineering involve rotating parts submitted to bending loads, which can result in the material heating. This thermal load happens due to energy loss caused by the material damping. This heat source can be great enough to make the component reach high temperatures and, consequently, risk its performance or even its resistance. A theoretical approach, considering that part of the mechanical energy is converted to thermal energy, implies that the maximum temperature found in a uniform rotary beam is linear dependent with the rotating speed and is directly proportional to the square of the applied load. This work intends to present some results acquired from an experiment performed in a fatigue test machine and also validate the theoretical formulation. Stainless steel (316L) specimens were painted with matte black ink to improve their emissivity. The temperature was measured via a FLIR thermographic infrared camera.

The paper deals with the methodology implemented by Magneti Marelli and Politecnico di Torino Vehicle Dynamics Research group to develop and verify the software of active chassis and powertrain control systems through a Hardware-In-the-Loop automated procedure. It is a general procedure which can be adopted for all the active chassis control systems, not only for their development but also for the verification of their reliability. The steps of the procedure are described in the first part of the paper. The specific application on which this paper is focused concerns robotized gearboxes.

The paper presents the main results obtained by developing and critically comparing different evaporative emissions leak detection diagnostic systems. Three different leak detection methods have been analyzed and developed by using a model-based approach: depressurization, air and fuel vapor compression, and natural vacuum pressure evolution. The methods have been developed to comply with the latest OBD II requirement for 0.5 mm leak detection. Detailed grey-box models of both the system (fuel tank, connecting pipes, canister module, engine intake system) and the components needed to perform the diagnostic test (air compressor or vacuum pump) have been used to analyze in a simulation environment the critical aspects of each of the three methods, and to develop “optimal” diagnostic model-based algorithms.

The design of a complex real-time embedded system requires the specification of its functionality, the design of the hardware and software architectures, the implementation of hardware and software components and finally the system validation. The designer, starting from the specification, refines the solution trying to minimize the system cost while satisfying functional and non functional requirements. The automatic code generation from models and the introduction of the platform-based design methodology can drastically improve the design efficiency of the software partition, while maintaining acceptable the cost overhead of the final system. In this approach, both top-down and bottom-up aspects are considered and solutions are found by a meet-in-the-middle approach that couples model refinement and platform modeling.

Dual Mass Flywheel (DMF) systems are today widely adopted in compression ignition automotive powertrains, due to the well-known positive effects on vehicle drivability and fuel consumption. This work deals with the analysis of undesirable effects that the installation of a DMF may cause to engine and transmission dynamics, with the objective of understanding the causes and of determining possible solutions to be adopted. The main results of an experimental and simulation analysis, focused on the rotational dynamics of a powertrain equipped with a DMF system, are presented in the paper. A mathematical model of the physical system has been developed, validated, and used to investigate, in a simulation environment, the anomalous behavior of the powertrain that had been experimentally observed under specific conditions. Particular attention has been devoted to two aspects that are considered critical: engine cranking phase; interactions between powertrain dynamics and idle speed control.

The paper describes suited methodologies for developing Recurrent Neural Networks (RNN) aimed at estimating NOx emissions at the exhaust of automotive Diesel engines. The proposed methodologies particularly aim at meeting the conflicting needs of feasible on-board implementation of advanced virtual sensors, such as neural network, and satisfactory prediction accuracy. Suited identification procedures and experimental tests were developed to improve RNN precision and generalization in predicting engine NOx emissions during transient operation. NOx measurements were accomplished by a fast response analyzer on a production automotive Diesel engine at the test bench. Proper post-processing of available experiments was performed to provide the identification procedure with the most exhaustive information content. The comparison between experimental results and predicted NOx values on several engine transients, exhibits high level of accuracy.

Design and optimization of intake and exhaust systems and valve timing is crucial in development of a naturally aspirated engine. Nowadays numerical simulation is a fundamental tool for this area. Unfortunately to perform an optimization of engine performance by setting even only a few parameters needs great effort in terms of time and engineering resources even with simple architecture engines. To overcome this problem the authors have developed an optimization methodology: the use of a 1_D simulation code allows one to build a neural network (NN) that characterizes engine working conditions for several input data variations (such as intake/exhaust systems and valve timing). A genetic algorithm (GA) coupled with the neural network is used to carry-out the multi-parameter optimization of engine performance.