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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).

An advanced multi-layer material model has been developed to simulate the complex behavior in case-carburized gears where hardness dependent strength and elastic-plastic behavior is characterized. Also, an advanced fatigue model has been calibrated to material fatigue tests over a wide range of conditions and implemented in FEMFAT software for root bending fatigue life prediction in differential gears. An FEA model of a differential is setup to simulate the rolling contact and transient stresses occurring within the differential gears. Gear root bending fatigue life is predicted using the calculated stresses and the FEMFAT fatigue model. A specialized rig test is set up and used to measure the fatigue life of the differential over a range of load conditions. Root bending fatigue life predictions are shown to correlate very well with the measured fatigue life in the rig test.

In the face of growing concerns about environmental sustainability and urban congestion, the integration of eco-driving strategies has emerged as a pivotal solution in the field of the urban transportation sector. This study explores the potential benefits of a CAV functioning as a virtual eco-driving controller in an urban traffic scenario with a group of following human-driven vehicles. A computationally inexpensive and realistic powertrain model and energy management system of the Chrysler Pacifica PHEV are developed with the field experiment data and integrated into a forward-looking vehicle simulator to implement and validate an eco-driving speed planning and energy management strategy assuming longitudinal automation. The eco-driving algorithm determines the optimal vehicle speed profile and energy management strategy.

In view of BS-VI emission norms implementation in Commercial Vehicle (CV) application, the emissions are not only confirmed in standard condition but also in non-standard condition including different combinations of ambient temperature and pressure especially for checking the emission in WNTE cycle. However, achieving the emissions in different environmental conditions require physical emission calibration to be performed in those conditions. Hence, engine must be calibrated in climatic test chambers to ensure emission in different climatic conditions leading to multifold increase in the calibration effort. With addition of BS-VI emission regulation, After Treatment System (ATS) is a mandatory requirement to reduce the tail pipe emissions. Efficient functioning of ATS requires enough heating to convert the engine out emissions. Vehicle level Real Drive Emission (RDE) measurement without Conformity Factor (CF) limitation are added as an important legislative requirement.

The major objective of this paper is to develop thermal management strategy targeting optimum performance of Selective Catalytic Reduction (SCR) catalyst in a Medium Duty Diesel Engine performing in BS6 emission cycles. In the current scenario, the Emissions Norms are becoming more stringent and with the introduction of Real Drive Emission Test (RDE) and WHTC test comprising of both cold and hot phase, there is a need to develop techniques and strategies which are quick to respond in real time to cope with emission limit especially NOx. SCR seems to be suitable solution in reducing NOx in real time. However, there are limitations to SCR operating conditions, the major being the dosing release conditions which defines the gas temperature at which DEF (Diesel Exhaust Fluid) can be injected as DEF injection at lower gas temperatures than dosing release will lead to Urea deposit formation and will significantly hamper the SCR performance.

Nowadays numerical simulations play a major role in the development of future sustainable powertrain thanks to their capability of investigating a wide spectrum of innovative technologies with times and costs significantly lower than a campaign of experimental tests. In such a framework, this paper aims to assess the predictive capabilities of an 1D-CFD engine model developed to support the design and the calibration of the innovative highly efficient spark ignition engine of the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) EU H2020 project. As a matter of fact, the availability of a reliable simulation platform is crucial to achieve the project target of 47% peak indicating efficiency, by synergistically exploiting the combination of innovative in-cylinder charge motion, Miller cycle with high compression ratio, lean mixture with cooled Exhaust Gas Recirculation (EGR) and electrified turbocharger.

The role of numerical simulations in the development of innovative and sustainable powertrains is constantly growing thanks to their capabilities to significantly reduce the calibration efforts and to point out potential synergies among different technologies. In such a framework, this paper describes the development of a fully physical 1D-CFD engine model to support the calibration of the highly efficient spark ignition engine of the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) EU H2020 project. The availability of a reliable simulation platform is essential to effectively exploit the combination of the several features introduced to achieve the project target of 47% peak gross indicated efficiency, such as SwumbleTM in-cylinder charge motion, Miller cycle combined with high Compression Ratio (CR), lean mixture exploiting cooled low pressure Exhaust Gas Recirculation (EGR) and electrified turbocharging.

In a military off-road vehicle, generally designed to operate in an aggressive operating environment, the typical comfort requirements for trucks and passenger cars are revised for robustness, safety and security. An example is the cabin space optimisation to provide easy access to many types of equipment required on-board. In this field, racks hung to the cabin chassis are generally used to support several electronic systems, like radios. The dynamic loads on a rack can reach high values in the operative conditions of a military vehicle. Rack failures should be prevented for the safety of driver, crew and load and the successful execution of a mission. Therefore, dynamic and durability tests of these components, including the fixtures to the vehicle, are required.

The concept of autonomous driving is becoming increasingly familiar in the automotive and “in-door” automation systems fields. Furthermore, the industrial development is focusing its efforts on industry 4.0, whose some main features are data transfer, programming, systems interconnection and automation. The agricultural sector just recently has experienced the first examples of autonomous agricultural vehicles, although agricultural mechanization has reached a good level of automation. Indeed, many examples of automatic machineries are already present in the market such as little robots for the execution of some operations. This work focuses on modelling and simulation of a self-driving orchard tractor. The main goal was to reproduce the behaviour of the specialized vehicle, moving in an orchard or a vineyard and conducting automatic or semi-automatic operations.

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.

To avoid frequent regeneration intervals leading to expeditious ageing of the catalyst and substantial fuel penalty for the owner, it is always desired to estimate the soot coming from diesel exhaust emission, the soot accumulated and burnt in the Diesel Particulate Filter (DPF). Certain applications and vehicle duty cycles cannot make use of the differential pressure sensor for estimating the soot loading in the DPF because of the limitations of the sensor tolerance and measurement accuracy. The physical soot model is always active and hence a precise and more accurate model is preferred to calibrate & optimize the regeneration interval. This paper presents the approach to estimate the engine-out soot and the accumulated soot in the DPF using a graphical calculation tool (AVL Concerto CalcGraf™).

Current developments in automotive industry such as hybrid powertrains and the continuously increasing demands on emission control systems, are pushing complexity still further. Validation of such systems lead to a huge amount of test cases and hence extreme testing efforts on the road. At the same time the pressure to reduce costs and minimize development time is creating challenging boundaries on development teams. Therefore, it is of utmost importance to utilize testing and validation prototypes in the most efficient way. It is necessary to apply high levels of instrumentation and collect as much data as possible. And a streamlined data pipeline allows the fleet managers to get new insights from the raw data and control the validation vehicles as well as the development team in the most efficient way. In this paper we will demonstrate a data-driven approach for validation testing.

Flow noise produced by the turbulent motion of the exhaust gases is one of the main contributions to the noise generation for a heavy-duty vehicle. The exhaust system has therefore to be optimized since the early stages of the design to improve the engine’s Noise Vibration Harshness (NVH) performance and to comply with legislation noise limits. In this context, the availability of reliable Computational Aero-Acoustics (CAA) methodologies is crucial to assess the noise mitigation potential of different exhaust system designs. In the present work, a characterization of the sound generation in a heavy-duty exhaust system was carried out evaluating the noise attenuation potential of a design modification, by means of a hybrid CAA methodology.

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.

In vehicle dynamics, simple and fast vehicle models are required, especially in the framework of real-time simulations and autonomous driving software. Therefore, a trade-off between accuracy and simulation speed must be pursued by selecting the appropriate level of detail and the corresponding simplifying assumptions based on the specific purpose of the simulation. The aim of this study is to develop a methodology for map and parameter estimation from multibody simulation results, to be used for simplified vehicle modelling focused on handling performance. In this paper, maneuvers, algorithms and results of the parameter estimation are reported, together with their integration in single track models with increasing complexity and fidelity. The agreement between the multibody model, used as reference, and four single track models is analyzed and discussed through the evaluation of the correlation index.

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.

The latest generation of internal combustion engines may emit significant levels of sub-23 nm particles. The main objective of the Horizon 2020 “DownToTen” project was to develop a robust methodology and provide policy recommendations towards the particle number (PN) emissions measurements in the sub-23 nm region. In order to achieve this target, a new portable exhaust particle sampling system (PEPS) was developed, being capable of measuring exhaust particles down to at least 10 nm under real-world conditions. The main design target was to build a system that is compatible with current PMP requirements and is characterized by minimized losses in the sub-23 nm region, high robustness against artefacts and high flexibility in terms of different PN modes investigation, i.e. non-volatile, volatile and secondary particles.

Gearbox behavior is strictly affected by gears, shaft, bearings and casing stiffnesses. As a matter of fact, their contribution to gear dynamics is fundamental for mechanical transmissions design. In this paper a semi-analytical model developed for the estimation of the dynamic behavior of two mating gears is presented and tested on two case studies. Starting with the estimation of the Static Transmission Error, intended as the difference between the theoretical and actual angular position between the two mating gears, the dynamic behavior of the mating elements is estimated by means of a Dynamic Model. The Dynamic Model takes into account the gears, the contact between teeth exchanging loads and the other mechanical elements reduced by means of a DOF reduction technique. Based on the block-oriented approach, Dynamic Model allows the user to easily manage the complexity of the system with further or less elements by adding or removing DOFs.

Fuel injection is a key process influencing the performance of Gasoline Direct Injection (GDI) Engines. Injecting fuel at elevated temperature can initiate flash boiling which can lead to faster breakup, reduced penetration, and increased spray-cone angle. Thus, it impacts engine efficiency in terms of combustion quality, CO2, NOx and soot emission levels. This research deals with modelling of flash boiling processes occurring in gasoline fuel injectors. The flashing mass transfer rate is modelled by the advanced Hertz-Knudsen model considering the deviation from the thermodynamic-equilibrium conditions. The effect of nucleation-site density and its variation with degree of superheat is studied. The model is validated against benchmark test cases and a substantiated comparison with experiment is achieved.

The aim of this paper is to study in deep the peculiar test-rigs and experimental procedures adopted to the fulfilment of the principal requirements of automotive steel wheels, in particular regarding fatigue damaging. In the discussion, the standard requirements, the OEM specifications and the dimensional and geometric tolerances are approached. As result of an increasingly necessity to improve the performance of the components, innovative virtual test benches are presented. Differently from their traditional precursors, virtual test-rigs give an extended view of the physical behaviour of the component as the possibility to monitor stress-strain distribution in deep. In the first section, the state of the art and the specifications are listed. Secondly, the adopted hardware test-rigs as the experimental tests are described in detail. In the third one, proposed virtual test-rig is discussed.