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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.

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.

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.

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.

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.

In recent years, driving simulators have become a valuable tool in the automotive design and testing process. Yet, in the field of vehicle dynamics, most decisions are still based on test drives in real cars. One reason for this situation can be found in the fact that many driving simulators do not allow the driver to evaluate the handling qualities of a simulated vehicle. In a driving simulator, the motion cueing algorithm tries to represent the vehicle motion within the constrained motion envelope of the motion platform. By nature, this process leads to so called false cues where the motion of the platform is not in phase or moving in a different direction with respect to the vehicle motion. In a driving simulator with classical filter-based motion cueing, false cues make it considerably more difficult for the driver to rate vehicle dynamics.

The aerodynamic development of the new Audi Q5 (released in 2017) is described. In the course of the optimization process a number of different tools has been applied depending on the chronological progress in the project. During the early design phase, wind tunnel experiments at 1:4 scale were performed accompanied by transient DES and stationary adjoint simulations. At this stage the model contained a detailed underbody but no detailed engine bay for underhood flow. Later, a full scale Q5 model was built up for the aerodynamic optimization in the 1:1 wind tunnel at Audi AG. The model featured a detailed underbody and engine bay including original parts for radiators, engine, axles and brakes from similar vehicles. Also the 1:1 experiments were accompanied by transient DES and stationary adjoint simulations in order to predict optimization potential and to better understand the governing flow.

During series production of modern combustion engines a major challenge is to ensure the correct operation of every engine part. A common method is to test engines in end-of-line (EOL) cold test stations, where the engines are not fired but tugged by an electric motor. In this work we present a physically based 0D model for dynamic simulation of combustion engines under EOL test conditions. Our goals are the analysis of manufacturing faults regarding their detectability and the enhancement of test procedures under varying environmental conditions. Physical experiments are prohibitive in production environments, and the simulative approach reduces them to a minimum. This model is the first known to the authors exploring advanced engine test methods under production conditions. The model supports a wide range of manufacturing faults (with adjustable magnitude) as well as error-free production spread in engine components.

The aerodynamic optimization of an AUDI Q5 vehicle is presented using the continuous adjoint approach within the OpenFOAM framework. All calculations are performed on an unstructured automatically generated mesh. The primal flow, which serves as input for the adjoint method, is calculated using the standard CFD process at AUDI. It is based on DES calculations using a Spalart-Allmaras turbulence model. The transient results of the primal solution are time averaged and fed to a stationary adjoint solver using a frozen turbulence assumption. From the adjoint model, drag sensitivity maps are computed and measures for drag reduction are derived. The predicted measures are compared to CFD simulations and to wind tunnel experiments at 1:4 model scale. In this context, general challenges, such as convergence and accuracy of the adjoint method are discussed and best practice guidelines are demonstrated.

The aim of this investigation is the improvement of the lateral vehicle dynamics by optimizing the rim width. For that purpose, the rim width is considered as a development tool and configured with regard to specified targets. Using a specifically developed method of simulation, the influence of the rim width is analysed within different levels - starting at the component level “tyre” and going up to the level of the whole vehicle. With the help of substantial simulations using a nonlinear two-track model, the dimensioning of the rim width is brought to an optimum. Based on both, tyre and vehicle measurements, the theoretical studies can be proved in practice. As a result, the rim width has a strong influence on the behaviour of the tyre as well as on the overall vehicle performance, which emphasises its importance as a potential development tool within the development of a chassis.

Precise three-dimensional dummy head trajectories during crash tests are very important for vehicle safety development. To determine precise trajectories with a standard deviation of approximately 5 millimeters, three-dimensional video analysis is an approved method. Therefore the tracked body is to be seen on at least two cameras during the whole crash term, which is often not given (e.g. head dips into the airbag). This non-continuity problem of video analysis is surmounted by numerical integration of differential un-interrupted electrical rotation and acceleration sensor signals mounted into the tracked body. Problems of this approach are unknown sensor calibration errors and unknown initial conditions, which result in trajectory deviations above 10 centimeters.

In recent years, we see more and more ECUs integrating a huge number of application software components. This process mostly results from the increasing amount of so called in-house software in various fields like electric-drive, chassis and driver assistance systems. The software development for these systems is partially moved from the supplier to the car manufacturers. Another important trend is the introduction of new network architectures intending to meet the growing communication requirements. For such ECUs the software integration scenarios become more complicated, as more quality of service requirements with regards to timing, safety and security need to be considered [2]. Multi-core microcontrollers offer even more potential variants for integration scenarios. Understanding the interaction between the different software components, not only from a functional, but also from a timing view, is a key success factor for modern electronic systems [6,7,8,9].

Since the last decade, the automotive industry has expressed the need to better understand how the different trim parts interact together in a complete car up to 400 Hz for structureborne excitations. Classical FE methods in which the acoustic trim is represented as non-structural masses (NSM) and high damping or surface absorbers on the acoustic cavity can only be used at lower frequencies and do not provide insights into the interactions of the acoustic trims with the structure and the acoustic volume. It was demonstrated in several papers that modelling the acoustic components using the poroelastic finite element method (PEM) can yield accurate vibro-acoustic response such as transmission loss of a car component [1,2,3]. The increase of performance of today's computers and the further optimization of commercial simulation codes allow computations on full vehicle level [4,5,6] with adequate accuracy and computation times, which is essential for a car OEM.

The optimization of the flow field around new vehicle concepts is driven by aerodynamic and thermal demands. Even though aerodynamics and thermodynamics interact, the corresponding design processes are still decoupled. Objective of this study is to include a thermal model into the aerodynamic design process. Thus, thermal concepts can be evaluated at a considerably earlier design stage of new vehicles, resulting in earlier market entry. In a first step, an incompressible CFD code is extended with a passive scalar transport equation for temperature. The next step also accounts for buoyancy effects. The simulated development of the thermal boundary layer is validated on a hot flat plate without pressure gradient. Subsequently, the solvers are validated for a heated block with ground clearance: The flow pattern in the wake and integral heat transfer coefficients are compared to wind tunnel simulations. The main section of this report covers the validation on a full-scale production car.

Open jet wind tunnels are normally tuned to measure “correct” results without any modifications to the raw data. This is an important difference to closed wall wind tunnels, which usually require wind tunnel corrections. The tuning of open jet facilities is typically done experimentally using pilot tunnels and adding final adjustments in the commissioning phase of the full scale tunnel. This approach lacked theoretical background in the past. There is still a common belief outside the small group of people designing and using open jet wind tunnels, that - similar to closed wind tunnels, which generally measure too high aerodynamic forces and moments without correction - open jet wind tunnels measure coefficient too low compared to the real world. The paper will try to show that there is a solid physical foundation underlying the experimental approach and that the expectation to receive self-correcting behavior can be supported by theoretical models.