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Electrified vehicles are particularly quiet, especially at low speeds due to the absence of combustion noises. This is why there are laws worldwide for artificial driving sounds to warn pedestrians. These sounds are generated using a so-called Acoustic Vehicle Alerting System (AVAS) which must maintain certain minimum sound pressure levels in specific frequency ranges at low speeds. The creation of the sound currently involves an iterative and sometimes time-consuming process that combines composing the sound on a computer with measuring the levels with a car on an outside noise test track. This continues until both the legal requirements and the subjective demands of vehicle manufacturers are met. To optimize this process and reduce the measurement effort on the outside noise test track, the goal is to replace the measurement with a simulation for a significant portion of the development.

The following paper aims to bring the topics of connected testing and emission measurements together. It is an introduction of connected bench testing with the aim to characterize brake particle emissions with a special focus on the impact of regenerative braking by simulating the real behavior of a premium BEV SUV. Such an approach combines the advantages of a brake dynamometer including an emission testing setup and a HiL setup to allow a much more precise testing of brake particle emissions under the impact of regen braking compared to the current recommendations of the Global Technical Regulation (GTR) on brake particle emissions. It is shown for the very first time, how interactions between the vehicle motion system work. The study includes one physical front brake corner as well as one physical rear brake corner. The regen functionalities are simulated by a real ESC-ECU which is the core of the HiL test setup.

In order to correctly predict the impact of tire dimensions and properties on ride comfort in the early phases of the vehicle development process, it is necessary to fully understand their influence on the dynamic tire behavior. The currently existing models for reproducing tire forces often need many measurements for parametrization, simplify physical properties by empiric functions, or have an insufficient simulation speed to analyze many variants in the short periods of early process phases. In the following analysis, a tire concept model is presented, which utilizes relations between the static and dynamic behavior of tires in order to efficiently predict the dynamic forces in the vertical and longitudinal direction during obstacle crossing. The model allows for efficient parametrization by minimizing the number of parameters as well as measurements and ensures a high simulation speed. To realize this, initially, a selection of tires is measured on a tire test rig.

The deposition behavior of brake wear particles on the surface of a wheel and the mechanisms on it have not been fully understood. In addition, the proportion of brake wear particles deposited on the wheel surface compared to the total emitted particles is almost unknown. This information is necessary to evaluate the number- and mass-related emission factors measured on the inertia dynamometer and to compare them with on-road and vehicle-related emission behaviour. The aim of this study is to clarify the deposition behavior of brake particles on the wheel surface. First, the real deposition behaviour is determined in on-road tests. For particle sampling, collection pads are adapted at different positions of a front and rear axle wheel. In addition to a Real Driving Emissions (RDE)-compliant test cycle, tests are performed in urban, rural and motorway sections to evaluate speed-dependent influences.

Particulate emissions from gasoline direct injection (GDI) engines continue to be a topic of substantial research interest. Forthcoming regulation both in the USA and the EU will further reduce their emission and drive innovation. Substantial research effort is spent undertaking experiments to understand, characterize, and research particle number (PN) emissions from engines and vehicles. Recent advances in computing power, data storage, and understanding of artificial intelligence algorithms now mean that these are becoming an important tool in engine research. In this work a random forest (RF) algorithm is used for the prediction of PN emissions from a highly boosted (up to 32 bar BMEP) GDI engine. Particle size, concentration, and the accumulation mode geometric standard deviation (GSD) are all predicted by the model. The results are analysed and an in depth study on parameter importance is carried out.

Accurate modelling for spray vapour fields is critical to enable adequate predictions of spray ignition and combustion characteristics of non-premixed reacting diesel sprays. Spray vapour characteristics are in turn controlled by liquid atomization and the KH-RT liquid jet break-up model is regularly used to predict this: with the KH model used for predicting primary break-up given its definition as a surface wave growth model, and the RT model used for predicting secondary break-up due to it being a drag based, stripping model. This paper investigates how the alteration of the switching position of the KH and RT sub-models within the KH-RT model impacts the resulting vapour field and ignition characteristics. The combustion prediction is handled by the implementation of a 54 species, 269 reaction skeletal mechanism utilising a Well Stirred Reactor model within the Star-CD CFD code.

E-mobility is a game changer for the automotive domain. It promises significant reduction in terms of complexity and in terms of local emissions. With falling prices and recent technological advances, the second generation of electric vehicles (EVs) that is now in production makes electromobility an affordable and viable option for more and more transport mission (people, freight). Current e-vehicle platforms still present architectural similarities with respect to combustion engine vehicle (e.g., centralized motor). Target of the European project EVC1000 is to introduce corner solutions with in-wheel motors supported by electrified chassis components (brake-by-wire, active suspension) and advanced control strategies for full potential exploitation. Especially, it is expected that this solution will provide more architectural freedom toward “design-for-purpose” vehicles built for dedicated usage models, further providing higher performances.

With battery electric vehicles (BEV), due to the absence of the combustion process, the rolling noise comes even more into play. The BEV technology also leads to different concepts of how to mount the electric engine in the car. Commonly, also applied with the Audi e-tron, the rear engine is mounted on a subframe, which again is connected to the body structure. This concept leads to a better insulation in the high frequency range, yet it bears some problems in designing the mounts for ride comfort (up to 20Hz) or body boom (up to 70Hz). Commonly engine mounts are laid-out based on driving dynamics and driving comfort (up to 20Hz). The current paper presents a new method to find an optimal mount design (concerning the stiffness) in order to reduce the dynamic chassis forces which are transferred to the body (>20Hz). This directly comes along with a reduction of the sound pressure level for the ‘body boom’ phenomena.

The statistical energy analysis (SEA) is widely used to support the development of the sound package of cars. This paper will present the preparation of a model designed to investigate the sound package of the new Audi A3 and associated correlation against measurements. Special care was given during the creation of the model on the representation of the structure to enable the analysis of structure borne energy flow on top of the classical airborne analysis usually done with SEA. The sound package was also detailed in the model to allow further optimization and analysis of its performance. Two real life load cases will be presented to validate the model with measurements. First, the dominating powertrain and a second load case with dominating rolling noise. An analysis of the contribution of the different source components and a way to diagnose the weak paths of the vehicle will be presented. The focus of this investigation is the application of optimally adjusted treatment.

The flows in-cylinder have a profound effect on the mixture preparation and subsequent combustion in all engines. These flows are highly three-dimensional in nature and information from multiple planes is required to characterise the flow dynamics. The flow measurements reported here are from three orthogonal planes in an optical access engine that is based on the Jaguar Land Rover AJ200 Gasoline Direct Injection (GDI) engine. Particle Image Velocimetry (PIV) measurements have been taken every 5°CA from the start of induction to the end of compression. Data have been obtained from 300 cycles for separate experiments measuring flows in the tumble plane, the swirl plane and the cross-tumble plane. Vector comparison metrics are used to quantitatively compare ensemble averaged PIV flow fields to Computational Fluid Dynamics (CFD) simulations across each plane in terms of both the velocity magnitude and direction.

Wheels on passenger vehicles cause about 25% of the aerodynamic drag. The interference of rims and tires in combination with the rotation result in strongly turbulent wake regions with complex flow phenomena. These wake structures interact with the flow around the vehicle. To understand the wake structures of wheels and their impact on the aerodynamic drag of the vehicle, the complexity was reduced by investigating a standalone tire in the wind tunnel. The wake region behind the wheel is investigated via Particle Image Velocimetry (PIV). The average flow field behind the investigated wheels is captured with this method and offers insight into the flow field. The investigation of the wake region allows for the connection of changes in the flow field to the change of tires and rims. Due to increased calculation performance, sophisticated computational fluid dynamics (CFD) simulations can capture detailed geometries like the tire tread and the movement of the rim.

This study looked into the application of active thermal coatings on the surfaces of the combustion chamber as a method of improving the thermal efficiency of internal combustion engines. The active thermal coating was applied to a production aluminium piston and its performance was compared against a reference aluminium piston on a single-cylinder diesel engine. The two pistons were tested over a wide range of speed/load conditions and the effects of EGR and combustion phasing on engine performance and tailpipe emissions were also investigated. A detailed energy balance approach was employed to study the thermal behaviour of the active thermal coating. In general, improvements in indicated specific fuel consumption were not statistically significant for the coated piston over the whole test matrix. Mean exhaust temperature showed a marginal increase with the coated piston of up to 6 °C.

In-cylinder flow motion has a significant effect on mixture preparation and combustion. Therefore, it is vital that CFD engine simulations are capable of accurately predicting the in-cylinder velocity fields. High-speed planar Particle Image Velocimetry (PIV) experiments have been performed on a single-cylinder GDI optical engine in order to validate CFD simulations for a range of engine conditions. Novel metrics have been developed to quantify the differences between experimental and simulated velocity fields in both alignment and magnitude. The Weighted Relevance Index (WRI) is a variation of the standard Relevance Index that accounts for the local velocity magnitudes to provide a robust comparison of the alignment between two vector fields. Similarly, the Weighted Magnitude Index (WMI) quantifies the differences in the local magnitudes of the two velocity fields.

In-cylinder temperatures and their cyclic variations strongly influence many aspects of internal combustion engine operation, from chemical reaction rates determining the production of NOx and particulate matter to the tendency for auto-ignition leading to knock in spark ignition engines. Spatially resolved measurements of temperature can provide insights into such processes and enable validation of Computational Fluid Dynamics simulations used to model engine performance and guide engine design. This work uses a combination of Two-Colour Planar Laser Induced Fluorescence (TC-PLIF) and Laser Induced Grating Spectroscopy (LIGS) to measure the in-cylinder temperature distributions of a firing optically accessible spark ignition engine. TC-PLIF performs 2-D temperature measurements using fluorescence emission in two different wavelength bands but requires calibration under conditions of known temperature, pressure and composition.

Knowledge of fuel mass injected in an individual cycle is important for engine performance and modelling. Currently direct measurements of fuel flow to individual cylinders of an engine are not possible on-engine or in real-time due to a lack of available appropriate measurement techniques. The objective of this work was to undertake real-time Coriolis fuel flow measurement using GDI injectors on a rig observing fuel mass flow rate within individual fuel injections. This paper evaluates the potential of this technology - combining Coriolis Flow Meters (CFMs) with Prism signal processing together known as Fast Next Generation Coriolis (Fast NGC), and serves as a basis for future transitions on-engine applications. A rig-based feasibility study has been undertaken injecting gasoline through a GDI injector at 150 bar in both single shot mode and at a simulated engine speeds of 1788 and 2978 rpm. The results show that these injections can, in principle, be observed.

Particulate emissions from Gasoline Direct Injection (GDI) engines have been an important topic of recent research interest due to their known environmental effects. This review paper will characterise the influence of different gasoline direct injection fuel systems on particle number (PN) emissions. The findings will be reviewed for engine and vehicle measurements with appropriate driving cycles (especially real driving cycles) to evaluate effects of the fuel injection systems on PN emissions. Recent technological developments alongside the trends of the influence of system pressure and nozzle design on injector tip wetting and deposits will be considered. Besides the engine and fuel system it is known that fuel composition will have an important effect on GDI engine PN emissions. The evaporation qualities of fuels have a substantial influence on mixture preparation, as does the composition of the fuel itself.

The acoustic trim components play an essential role in NVH behavior by reducing both the structure borne and airborne noise transmission while participating to the absorption inside the car. Over the past years, the interest for numerical solutions to predict the noise transmission through trim packages has grown, leading to the development of dedicated CAE tools. The incrementally restrictive weight and space constraints force today CAE engineers to seek for optimized trim package solution. This paper presents a two-steps process which aims to reduce the structure borne road noise due to floor panel using a coupled simulation with MSC NASTRAN and Actran. The embossment of the supporting steel structure, the material properties of porous layers and the thickness of visco-elastic patches are the design variables of the optimization process.

Knowledge of fuel mass injected in an individual cycle is important for engine performance and modeling. At the moment, such measurements are not possible on engine or in real time. In this article, a new method using Coriolis flow meters (CFMs) and a new, patented, signal processing technique, known as the Prism, are introduced. CFMs are extensively used for flow measurement both in the automotive industry and further afield and, when coupled with the Prism, have the potential to make these challenging high-speed measurements. A rig-based feasibility study was conducted injecting very small quantities of diesel (3 mg) at pressures of up to 1000 bar at simulated engine speeds of up to 4000 rpm. The results show that these small quantities can in principle be measured. The results also reveal a previously unknown behavior of CFMs when measuring very low flow rates at high speed.

Accurate modeling of the initial transient period of spray development is critical within diesel engines, as it impacts on the amount of vapor penetration and hence the combustion characteristics of the spray. In addition, in multiple injection schemes shorter injections will be mostly, if not totally, within the initial transient period. This paper investigates how two different commercially available Computational Fluid Dynamics (CFD) codes (hereafter noted as Code 1 and Code 2) simulate transient diesel spray atomization, in a non-combusting environment. The case considered for comparison is a single-hole injection of n-dodecane representing the Engine Combustion Network’s ‘Spray A’ condition. It was identified that the different spray break-up models used by the codes (Reitz-Diwakar for Code 1, Kelvin-Helmholtz/Rayleigh-Taylor (KH-RT) for Code 2) had a significant impact on the transient liquid penetration.

A wide-ranging investigation into the sensitivity of the hybrid RANS-LES based OpenFOAM CFD process at Audi was undertaken. For a range of cars (A1, TT, Q3 & A4) the influence of the computational grid resolution, turbulence model formulation and spatial & temporal discretization is assessed. It is shown that SnappyHexMesh, the Cartesian-prismatic built-in OpenFOAM mesher is unable to generate low y+ grids of sufficient quality for the production Audi car geometries. For high y+ grids there was not a consistent trend of additional refinement leading to improved correlation between CFD and experimental data. Similar conclusions were found for the turbulence models and numerical schemes, where consistent improvements over the baseline setup for all aerodynamic force coefficients were in general not possible. The A1 vehicle exhibited the greatest sensitivity to methodology changes, with the TT showing the least sensitivity.