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The engine acoustic character has always represented the product DNA, owing to its strong correlation with in-cylinder pressure gradient, components design and perceived quality. Best practice for engine acoustic characterization requires the employment of a hemi-anechoic chamber, a significant number of sensors and special acoustic insulation for engine ancillaries and transmission. This process is highly demanding in terms of cost and time due to multiple engine working points to be tested and consequent data post-processing. Since Neural Networks potentially predicting capabilities are apparently un-exploited in this research field, the following paper provides a tool able to acoustically estimate engine performance, processing system inputs (e.g. Injected Fuel, Rail Pressure) thanks to the employment of Multi Layer Perceptron (MLP, a feed forward Network working in stationary points).

The dynamic model is built in Siemens Simcenter Amesim platform and simulates the performances on track of JUNO, a low energy demanding Urban Concept vehicle to take part in the Shell Eco-Marathon competition, in which the goal is to achieve the lowest fuel consumption in covering some laps of a racetrack, with limitations on the maximum race time. The model starts with the longitudinal dynamics, analysing all the factors that characterize the vehicle’s forward resistance, like aerodynamic forces, altimetry changes and rolling resistance. To improve the correlation between simulation and track performances, the model has been updated with the implementation of a Single-Track Model, including vehicle rotation around its roll axis, and a 3D representation of the racetrack, with an automatic trajectory following control implemented. This is crucial to characterise the vehicle’s lateral dynamics, which cannot be neglected in simulating its performances on track.

The current electrification trend involving hybrid and electric vehicles requires accurate tools to evaluate performance and reliability of electric powertrains’ control systems. Thanks to Hardware in the Loop (HiL) technique, verification, validation and virtual calibration of Electronic Control Systems can be performed without physical plants, addressing the need of frontloading, cost and time reduction of new vehicles control systems development. However, HiL applications with power electronics controllers brings several concerns due to the extremely low timestep needed for accurate simulation of electromagnetic phenomena, making FPGA-based simulation the only option. Moreover, thermal aspects of electric motors are very important from the control perspective as complex thermal management control strategies are implemented to improve the efficiency and to prevent overheating that can cause permanent damage to the electrical machine.

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.

The current trend towards an increasing electrification of road vehicles brings to life a whole series of unprecedent design issues. Among these, the ageing process that affects the lifetime of lithium-ion based energy storage systems is of particular importance since it turns out to be extremely sensitive to the variation of battery operating conditions normally occurring especially in hybrid electric vehicles (HEVs). This paper aims at analyzing the impact of operating conditions on the predicted lifetime of a parallel-through-the-road plug-in HEV battery both from thermal and ageing perspectives. The retained HEV powertrain architecture is presented first and modeled, and the related energy management system is implemented. Dedicated numerical models are also discussed for the high-voltage battery pack that allow predicting its thermal behavior and cyclic ageing.

Nowadays control system development in the automotive industry is evolving rapidly due to several factors. On the one hand legislation tightening is asking for simultaneous emission reduction and efficiency increase, on the other hand the complexity of the powertrain is increasing due to the spreading of electrification. Those factors are pushing for strong design parallelization and frontloading, thus requiring engine calibration to be moved much earlier in the V-Cycle. In this context, this paper shows how, coupling well known physical 1D engine models featuring predictive combustion and emission models with a fully physical aftertreatment system model and longitudinal vehicle model, a powerful virtual test rig can be built. This virtual test rig can be used for powertrain virtual calibration activities with reduced requirement in terms of experimental data.

Diesel engines with their embedded control systems are becoming increasingly complex as the emission regulations tighten, especially concerning NOx pollutants. The combustion and emission formation processes are closely correlated to the intake manifold O2 concentration. Consequently, the performance of the engine controllers can be improved if a model-based or sensor-based estimation of the O2 concentration is available. The paper addresses the modeling of the O2 concentration in a turbocharged diesel engine. Dynamic models, compared to generally employed steady state maps, capture the dynamic effects occurring over transients, when the major deviations from the stationary maps are found. Dynamic models positively affect the control system making it more effective and, exploiting information coming from sensors, they provide a more robust prediction performance. Firstly, a Nonlinear Output Error model (NOE), with simulation focus, fed with four inputs is presented.

To comply with the stringent fuel consumption requirements, many automobile manufacturers have launched vehicle electrification programs which are representing a paradigm shift in vehicle design. Looking specifically at powertrain calibration, optimization approaches were developed to help the decision-making process in the powertrain control. Due to computational power limitations the most common approach is still the use of powertrain calibration tables in a rule-based controller. This is true despite the fact that the most common manual tuning can be quite long and exhausting, and with the optimal consumption behavior rarely being achieved. The present work proposes a simulation tool that has the objective to automate the process of tuning a calibration table in a powertrain model. To achieve that, it is first necessary to define the optimal reference performance.

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.

Flow control over aerodynamic shapes in order to achieve performance enhancements has been a lively research area for last two decades. Synthetic Jet Actuators (SJAs) are devices able to interact actively with the flow around their hosting structure by providing ejection and suction of fluid from the enclosed cavity containing a piezo-electric oscillating membrane through dedicated orifices. The research presented in this paper concerns the implementation of zero-net-mass-flux SJAs airflow control system on a NACA0015, low aspect ratio wing section prototype. Two arrays with each 10 custom-made SJAs, installed at 10% and 65% of the chord length, make up the actuation system. The sensing system consists of eleven acoustic pressure transducers distributed in the wing upper surface and on the flap, an accelerometer placed in proximity of the wing c.g. and a six-axis force balance for integral load measurement.

Emission requirements for diesel engines are becoming increasingly strict, leading to the increase of engine architecture complexity. This evolution requires a more systematic approach in the development of control systems than presently adopted, in order to achieve improved performances and reduction of times and costs in design, implementation and calibration. To this end, large efforts have been devoted in recent years to the application of advanced Model-Based MIMO control systems. In the present paper a new MIMO nonlinear feedback control is proposed, based on an innovative data-driven method, which allows to design the control directly from the experimental data acquired on the plant to be controlled. Thus, the proposed control design does not need the intermediate step of a reliable plant model identification, as required by Model-Based methods.

The vehicle sideslip angle is one of the most important variables for evaluating vehicle dynamics. The potential value of such a variable for obtaining significant improvements over current stability control systems is widely recognized. However, its direct measurement requires the use of complex and expensive devices which cannot be used in production cars. Large research efforts has been devoted to the problem of estimating the sideslip angle from other variables currently measured by standard Electronic Stability Control (ESC) sensors. However, at the best of author's knowledge, until now no application to production cars is known. In this paper, a new sideslip angle estimation technology is presented.

The paper presents a simulation methodology created to support an integrated safety system development process which was tested for the side impact collision load case. The methodology is based on the coupled and complementary use of two software packages: PreScan and Madymo. PreScan was utilized for designing two traffic scenarios and the sensing and control systems for the side collision recognition, while Madymo was utilized for assessing the effects of pre-crash deployment of thorax airbag. The collision conditions from the scenarios were used as input to define a Madymo side collision model of the host vehicle and to investigate and optimize several airbag deployment parameters: pre-crash deployment time, airbag permeability, vent hole size and vent hole opening time.

A prototype vehicle (PV) is equipped to test powertrain and active chassis systems with innovative control strategies for safety and energy saving. Additional sensors installed on-board allow the measurement and estimation of new information useful to the vehicle dynamic control. The PV was based on a serial production passenger car with Electronic Stability Control (ESC). Testing activities on Controller Area Network (CAN) and ESC Electronic Control Unit (ECU) are carried out to compare the vehicle dynamic performance obtainable using serial production rather than customized control strategies, while maintaining the same hardware. The PV is also utilized to provide reverse engineering analysis about the implemented control strategy for the ESC working in serial production mode.

Different EGR system layouts (a Long Route, a Short Route, and a combination of the two) were evaluated by means of both numerical simulation and experimental tests. In particular, a one-dimensional fluid-dynamic engine model was built in order to evaluate the potential of a Long Route EGR system as well as the potential of different EGR combinations between Long and Short Route. By means of the one-dimensional model, used as a virtual test bench, the estimations of the NOx emissions, based on the Extended Zeldovich Mechanism (EZM), for the different solutions, were compared and valuable information for the calibration of the coordinated EGR LR, EGR SR and Variable Geometry Turbine (VGT) control systems was obtained.

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.

In this study, a new EGR control technique, based on the estimate of the oxygen concentration in the intake manifold, was firstly investigated through numerical simulation and then experimentally tested, both under steady state and transient conditions. The robustness of the new control technique was also tested and compared with that of the conventional EGR control technique by means of both numerical simulation and experimental tests. Substantial reductions of the NOx emissions under transient operating conditions were achieved, and useful knowledge for controlling the EGR flow rate more accurately was obtained.

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.

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.

Chassis Control Systems development methodology is nowadays strongly based on analyzing performance by using PC vehicle dynamics simulation. Generally, the overall design, test bench and road validation process is continuously accompanied by simulation. The Base Model Simulator was developed by the Vehicle Dynamics Group at the Department of Mechanics of Politecnico di Torino both to satisfy this requirement and for educational purposes. It considers a complete vehicle dynamics mathematical model, including driver, powertrain, driveline, vehicle body, suspensions, steering system, brakes, tires. The Base Model Simulator takes in account the suspensions system elastokinematics, including, for example, automatic computation of camber variation during the vehicle roll motions. Tire model considered are either Pacejka's models or experimental data.