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A numerical investigation on a series of Diesel spray experiments in constant-volume vessels is proposed. Non reacting conditions were used to assess the spray models and to determine the grid size required to correctly predict the fuel-air mixture formation process. To this end, not only computed liquid and vapor penetrations were compared with experimental data, but also a detailed comparison between computed and experimental mixture fraction distributions was performed at different distances from the injector. Grid dependency was reduced by introducing an Adaptive Local Mesh Refinement technique (ALMR) with an arbitrary level of refinement. Once the capabilities of the current implemented spray models have been assessed, reacting conditions at different ambient densities and temperatures were considered. A Perfectly Stirred Reactor (PSR) combustion model, based on a direct integration of complex chemistry mechanisms over a homogenous cell, was adopted.

Intensive R&D is currently performed worldwide on hybrid and electric vehicles. For full electric vehicles the driving range is limited by the capacity of currently available batteries. If such a vehicle shall increase its driving range some range extending backup system should be available. Such a Range Extender is a small system of combustion engine and electric generator which produces the required electricity for charging the batteries in time. Since the acoustic response of an electric motor driving the vehicle and of a combustion engine as part of a Range Extender is very different by nature an extensive acoustic tuning of the Range Extender is necessary to meet the requirements of exterior vehicle noise and passenger comfort. This paper describes the NVH (noise, vibration & harshness) development work of a range extender within the AVL approach of an electrically driven passenger car with range extender.

In this work a multilevel CFD analysis have been applied for the design of an intake air-box with improved characteristics of noise reduction and fluid dynamic response. The approaches developed and applied for the optimization process range from the 1D to fully 3D CFD simulation, exploring hybrid approaches based on the integration of a 1D model with quasi-3D and 3D tools. In particular, the quasi-3D strategy is exploited to investigate several configurations, tailoring the best trade-off between noise abatement at frequencies below 1000 Hz and optimization of engine performances. Once the best configuration has been defined, the 1D-3D approach has been adopted to confirm the prediction carried out by means of the simplified approach, studying also the impact of the new configuration on the engine performances.

Global warming is the driver for introduction of CO2 and fuel consumption legislation worldwide. Indian truck manufacturers are facing the introduction of Indian fuel efficiency norms. In the European Union the CO2 emission monitoring phase of the most relevant truck classes was completed in June 2020 by usage of the Vehicle Energy Consumption Calculation TOol VECTO. Indian rule makers are currently considering an adaptation of VECTO for the usage in India, too. Indian truck market has always been very cost sensitive. Introduction of Bharat Stage VI Phase I has already led to a significant cost increase for emission compliance. Therefore, it will be of vital importance to keep the additional product costs for achievement of future fuel consumption legislation as low as possible as long as the real-world operation will not be promoted by the government.

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.

Grown interest in complex modern Hybrid Electric Vehicle (HEV) concepts has raised new challenges in the field of NVH. The switch between the Internal Combustion Engine (ICE) and the Electric Motor (EM) at low speeds produces undesirable vibrations and a sudden raise of noise levels that effects the sound quality and passenger comfort achieved by the close-to-silent electric powertrain operation. Starting the ICE in the most suitable driving situation to create a seamless transition between driving modes can be the key to minimize the NVH quality impact in driver and passenger’s perception in HEVs. To integrate this important aspect in the early stages of the development and design phase, simulation technologies can be used to address the customer acceptance. By analyzing NVH measurements, the different noise components of the vehicle operation can be separated into ICE-related noise, EM-related noise and driving noise.

Cycle-To-Cycle Variability (CCV) must be properly considered when modeling the ignition process in SI engines operating with ultra-lean mixtures. In this work, a strategy to model the impact of the ignition type on the CCV was developed using the RANS approach for turbulence modelling, performing multi-cycle simulations for the power-cycle only. The spark-discharge was modelled through a set of Lagrangian particles, introduced along the sparkgap and interacting with the surrounding Eulerian gas flow. Then, at each discharge event, the velocity of each particle was modified with a zero-divergence perturbation of the velocity field with respect to average conditions. Finally, the particles velocity was evolved according to the Simplified Langevin Model (SLM), which keeps memory of the initial perturbation and applies a Wiener process to simulate the stochastic interaction of each channel particle with the surrounding gas flow.

A wheel able to measure the generalized forces at the hub of a race motorcycle has been developed and used. The wheel has a very limited mass. It is made from magnesium with a special structure to sense the forces and provide the required level of stiffness. The wheel has been tested both indoor for preliminary approval and on the track. The three forces and the three moments acting at the hub can be measured with a resolution of 1N and 0.3Nm respectively. A specifically programmed DSP (Digital Signal Processor) embedded in the sensor allows real-time acquisition and processing of the six signals of forces/torques components. The signals are sent via Bluetooth to an onboard receiver connected to the vehicle CAN (Controller Area Network) bus. Each signal is sampled at 200Hz. The wheel can be used to derive the actual tyre characteristics or to record the loads acting at the hub.

Spark Ignited (SI) combustions engines in combination with different degrees of hybridization are expected to play a major role in future vehicle propulsion. Due to the combustion principle and the related thermodynamic efficiency, it is especially challenging to meet future CO2 targets. The layout and optimization of the overall system requires novel methods in the development process which feature a seamless transition between real and virtual prototypes. Herein, engine models need to predict the entire engine operating range in steady-state and transient conditions and must respond to all relevant control inputs. In addition, the model must feature true real-time capability. This work presents a holistic and modular modeling framework, which considers all relevant processes in the complex chain of physical effects in SI combustion.

The thorough understanding of the effects due to the fuel direct injection process in modern gasoline direct injection engines has become a mandatory task to meet the most demanding regulations in terms of pollutant emissions. Within this context, computational fluid dynamics proves to be a powerful tool to investigate how the in-cylinder spray evolution influences the mixture distribution, the soot formation and the wall impingement. In this work, the authors proposed a comprehensive methodology to simulate the air-fuel mixture formation into a gasoline direct injection engine under multiple operating conditions. At first, a suitable set of spray sub-models, implemented into an open-source code, was tested on the Engine Combustion Network Spray G injector operating into a static vessel chamber. Such configuration was chosen as it represents a typical gasoline multi-hole injector, extensively used in modern gasoline direct injection engines.

Today, Direct-Injection systems are widely used on Spark-Ignition engines in combination with turbo-charging to reduce the fuel-consumption and the knock risks. In particular, the spread of Gasoline Direct Injection (GDI) systems is mainly related to the use of new generations of multi-hole, high-pressure injectors whose characteristics are quite different with respect to the hollow-cone, low-pressure injectors adopted in the last decade. This paper presents the results of an experimental campaign conducted on the spray produced by a GDI six-holes injector into a constant volume vessel with optical access. The vessel was filled with air at atmospheric pressure. Different operating conditions were considered for an injection pressure ranging from 3 to 20 MPa. For each operating condition, spray images were acquired by a CCD camera and then post processed to evaluate the spray penetration and cone angles.

Many studies have been carried out to optimize the aerodynamic performances of a single car or a single vehicle. In present days the traffic increases and sophisticated technologies are developing to guarantee the drivers safety, to minimize the fuel consumption and be more environmentally friendly. Within this research area a new technique that is being studied is Platooning: this means that different vehicles travel in a configuration that minimizes the aerodynamic drag and therefore the fuel consumption and the longitudinal space. In the present study platoons with different vehicles and configurations are taken into account, to analyze the influence of car shape and relative distance between the vehicles. The research has been carried out using CFD techniques to investigate the different flow fields around different platoons, while wind tunnel tests have been used to validate the results of the CFD simulations.

The automotive domain has seen safety engineering at the forefront of the industry’s priorities for the last decade. Therefore, additional safety engineering efforts, design approaches, and well-established safety processes have been stipulated. Today many connected and automated vehicles are available and connectivity features and information sharing are increasingly used. This increases the attractiveness of an attack on vehicles and thus introduces new risks for vehicle cybersecurity. Thus, just as safety became a critical part of the development in the late 20th century, the automotive domain must now consider cybersecurity as an integral part of the development of modern vehicles. Aware of this fact, the automotive industry has, therefore, recently taken multiple efforts in designing and producing safe and secure connected and automated vehicles.

Fuel cell electric vehicles are a promising technology to create CO2- neutral mobility. Model-based development approaches are key to reduce costs and to raise efficiencies. A model on vehicle system level is discussed that balances the need of physical depth and computational performance. The vehicle model comprises the domains of mechanics, electrics, thermodynamics, cooling and controls. Detailed models of the fuel cell and battery are presented as a part of the system model. The models apply electrochemical approaches and spatial resolutions up to 3D. The models of both components are validated via 3D reference simulations showing a seamless parameter transfer between system level and CFD-based simulations. The validity of the vehicle model, including the electrochemical components, is demonstrated by simulating the Toyota Mirai vehicle. Simulation results of an NEDC are compared to measurements.

In recent years, concerns for environmental pollution and oil price stimulated the demand for vehicles based on technologies alternative to traditional IC engines. Nowadays several carmakers include hybrid vehicles among their offer and first full electric vehicles appear on the market. Among the different layout of the electric power-train, four in-wheel motors appear to be one of the most attractive. Besides increasing the inner room, this architecture offers the interesting opportunity of easily and efficiently distribute the driving/braking torque on the four wheels. This characteristic can be exploited to generate a yaw moment (torque vectoring) able to increase lateral stability and to improve the handling of a vehicle. The present paper presents and compares two different torque vectoring control strategies for an electric vehicle with four in-wheel motors. Performances of the control strategies are evaluated by means of numerical simulations of open and closed loop maneuvers.

This paper is focused on the development and application of a CFD methodology that can be applied to predict the fuel-air mixing process in stratified charge, sparkignition engines. The Eulerian-Lagrangian approach was used to model the spray evolution together with a liquid film model that properly takes into account its effects on the fuel-air mixing process into account. However, numerical simulation of stratified combustion in SI engines is a very challenging task for CFD modeling, due to the complex interaction of different physical phenomena involving turbulent, reacting and multiphase flows evolving inside a moving geometry. Hence, for a proper assessment of the different sub-models involved a detailed set of experimental optical data is required. To this end, a large experimental database was built by the authors.

In the paper some methods are presented, with the corresponding practical examples, related to MonteCarlo (MC) techniques for thermal model/test correlation purposes. The MonteCarlo techniques applied to model correlation are intended to be used as an alternative to empirical ‘manual’ correlation techniques, gradients methods, matrix methods based on least square fit minimization. First of all, Design Of Experiments (DoE) tools are used to determine the model response to uncertain parameters and the confidence level of such a response. A sensitivity map is built, allowing the design of the test to maximize the response of the system to the uncertain parameters. Techniques derived from the extreme statistics are used to extrapolate data beyond test limits, with a sufficient confidence in the queue behaviour.

This paper exploits the possibilities of achieving an efficient performance optimization methodology to be applied to different spark ignition engine configurations. The objective of the task described here is to determine the combination of parameters which provides the highest volumetric efficiency and effective torque. The definition of general strategy requires first the identification and grouping of the geometric and operating variables to be optimized (duct diameters and lengths, valve timing, spark advance, etc…). The high number of possibilities entails critical choices to reduce, from an engineering design point of view before than from a mathematical point of view, the required computational time. Once proper thermo-fluid dynamic decisions are taken, the most efficient optimization methodology is required. The application of Design of Experiments techniques allows to screen the design space and give a first estimation of the optimal point.

Today's calibration process for ECU functions is often based on a wide variety of proprietary tools and individual expert knowledge of calibration engineers. Automatic calibration with an industrialized tool chain provides high potential to reduce testbed time, calibration time and project costs. Based on an efficient measurement procedure in combination with an offline calibration methodology the capability is validated, e.g. for calibrating the ECU function “Air Charge Determination” for SI engines. In this article the implementation, in a series production project of a major OEM, is shown. The whole workflow - which can also be applied to other calibration tasks - will be described in detail. Presented here will be how General Motors Corporation (GM) is able to speed up the calibration of the ECU functions, whilst maintaining at least the same quality of calibration as before, by the use of this tool chain.

Unstable combustion and high cyclic variations of the in-cylinder pressure associated with low engine running smoothness and high emissions are mainly caused by cyclic variations of the fresh charge composition, the variability of the ignition and the fuel mass. These parameters affect the inflammation, the burn rate and thus the whole combustion process. In this paper, the effects of fluctuating fuel mass on the combustion behavior are shown. Small two-stroke engines require special measuring and testing equipment, especially for measuring the fuel consumption at very low fuel flow rates as well as very low fuel supply pressures. To realize a cycle-resolved measurement of the injected fuel mass, fuel consumption measurement with high resolution and high dynamic response is not enough for this application.