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With the law on autonomous driving and the associated ordinance, it has been possible in Germany since June 2022 to grant operating permission to vehicles with automated driving function and to admit them to road traffic. For public transport, automated shuttle buses offer the opportunity to maintain the existing service despite a shortage of personnel and to close gaps in supply. The safety of vehicle occupants, other road users and third parties is a key concern in the operation of automated shuttle buses. As part of the project RABus “Real laboratory for automated bus operation in public transport in urban and rural areas”, which is funded by the Ministry of Transport Baden-Württemberg, the operational reliability of automated shuttle buses (SAE Level 4) in public transport is being scientifically investigated. In the implementation of automated driving, obstacle detection/avoidance is a safety-critical driving function.

Whether as an optimization problem or a development tool, neural networks help engineers to work more efficiently. This paper’s central aspect is to add metadata to the core files of the project simulation data. To understand the project and its simulation models, a pre-processing methodology and convolutional neural network architecture are presented. With the added labels, it is possible to access the content of the model files of an engine performance simulation tool without examining them. At first, a pre-processing approach and its design are introduced to extract and filter the desired data from the XML data structure. Then, the data is split into sequences and paired with labels. Expert knowledge is used to label the models. These labels are further paired with the extracted sequences.

A computational study of the vehicle aerodynamics influenced by the wake of the rotating wheel taking into account a detailed rim geometry is presently performed. The car configuration corresponds to a full-scale (1:1) notchback configuration of the well-known ‘DrivAer’ vehicle model, Heft et al. [1]. The objective of the present work is to investigate the performance of some popular turbulence models in conjunction with different methods for handling the wheel rotation – rotating wall velocity, ‘multiple reference frame’ and ‘sliding grid algorithm’. The specific focus hereby is on a near-wall RANS eddy-viscosity model based on elliptic-relaxation, sensitized to resolve fluctuating turbulence by introducing a specifically modeled production term in the scale-supplying equation, motivated by the Scale-Adaptive Simulation approach (SAS, [2]), proposed by Krumbein et al. [3].

High-efficient simulations are mandatory to manage the ever-increasing complexity of automotive powertrain system and reduce development time and costs. Integrating AI methods into the development process provides an ideal solution thanks to massive increase in computational power. Based on an 1D physical engine model of a turbo-charged direct injection gasoline engine with variable valve timing (VVT), a high-performance hybrid simulation model has been developed for increasing computing performance. The newly developed model is made of a physics-based low-pressure part including intake and exhaust peripheries and a neural-network-based high-pressure part for combustion chamber calculations. For the training and validation of the combustion chamber neural networks, a data set with 10.5 million operating points was generated in a short time thanks to the parallelizable combustion chamber simulations in stand-alone mode.

In use cars often drive through the wakes of other vehicles. It has long been appreciated that this imposes a fluctuating onset flow which can excite a structural response in vehicle panels, particularly the bonnet. This structure must be designed to be robust to such excitation to guarantee structural integrity and maintain customer expectations of quality. As we move towards autonomous vehicles and exploit platoons for drag reduction, this onset flow condition merits further attention. The work reported here comprises both measurements and simulation capturing the unsteady pressure distribution over the bonnet of an SUV following a similar vehicle at high speed and in relatively close proximity. Measurements were taken during track testing and include 48 static measurement locations distributed over the bonnet where the unsteady static pressures were recorded.

Natural wind, roadside obstacles, terrain roughness, and traffic influence the incident flow of a vehicle driven on public roads. These transient on-road conditions differ from the idealized statistical steady-state flow environment utilized in CFD simulations and wind tunnel experiments. To understand these transient on-road conditions better, measurements were performed on German public highways and on a test site. A compact car was equipped with a measurement system that is capable of determining the transient airflow around the vehicle and the vehicle’s actual driving state. This vehicle was driven several times on a predefined 200 km long route to investigate different traffic densities on public highways in southern Germany. During the tests the transient incident flow and pressure distribution on the vehicle surface were measured.

Automotive vehicles operate in complex, transient aerodynamic conditions that can potentially influence their operational efficiency, performance and safety. A moving-model facility combined with a wind-tunnel is an experimental methodology that can be utilized to model some of these transient aerodynamic conditions. This experimental methodology is an alternative to wind-tunnel experiments with additional crosswind generators or actively yawing models, and has the added benefit of modelling the correct relative motion between the vehicle and the ground/infrastructure. Experiments using a VW Golf 7 were performed with a 1:10 scale model at the moving-model facility at DLR, Göttingen and a full-scale, operational vehicle at the BMW Ascheim side-wind facility.

The field of vision of the driver during wet road conditions is essential for safety at all times. Additionally, the safe use of the increasing number of sensors integrated in modern cars for autonomous driving and intelligent driver assistant systems has to be ensured even under challenging weather conditions. To fulfil these requirements during the development process of new cars, experimental and numerical investigations of vehicle soiling are performed. This paper presents the surface contamination of self- and foreign-soiling tested in the wind tunnel. For these type of tests, the fluorescence method is state-of-the-art and widely used for visualizing critical areas. In the last years, the importance of parameters like the contact angle have been identified when designing the experimental setup. In addition, new visualization techniques have been introduced.

The engine development process has been enhanced significantly by virtual engineering methods during the last decades. In terms of in-cylinder flow field, charge flow and combustion modelling, 3D-CFD (three dimensional) simulations enable detailed analysis and extended investigations in order to gain additional knowledge about design parameters. However, the computational time of the 3D-CFD is an obvious drawback that prevents a reasonable application for extensive analysis with varying speed, load and transient conditions. State-of-the-art 0D (zero dimensional) approaches close the gap between the demand of high computational efficiency and a satisfying accordance with experimental data. Recent improvements of phenomenological combustion approaches for gasoline spark ignition engines deal with the consideration of detailed flow parameters, the accuracy of the laminar flame speed calculation and the prediction of the knock limit.

Cooling air flow is an important factor when it comes to vehicle performance and operating safety. In addition, it is closely linked to vehicle aerodynamics. In recent years more and more effort is being spent to optimize the losses generated by the flow through the vehicle. Grille shutters, better sealing and ducting are only some examples for innovations in this field of work, resulting in a lower contribution of the cooling air flow to overall drag. But cooling air not only affects the internal flow of the vehicle but also changes the flow around it. This paper will show changes in the flow field around the generic DrivAer model resulting from cooling air flow, especially in the wake behind the car and in the region around the front wheels. The results were gathered using PIV measurements, multi-hole-probe measurements and pitot tube measurements in the 1:4 model scale wind tunnel of IVK University of Stuttgart.

Cooling air flow is an important factor when it comes to vehicle performance and operating safety. In addition, it is closely linked to vehicle aerodynamics. In recent years more and more effort is being spent to optimize the losses generated by the flow through the vehicle. Grille shutters, better sealing and ducting are only some examples for innovations in this field of work, resulting in a lower contribution of the cooling air flow to overall drag. When investigating those effects, both experiments and numerical simulations are commonly used in the automotive environment. Still, when comparing the results from both methods, differences in the effect of cooling air flow can often be observed. To better understand the effects of cooling air flow, the ECARA Subgroup CFD decided to establish a common design for a generic open source vehicle model with a detailed underhood compartment to lay the foundation for a common investigation model.

In the last years motorsport is facing a technical revolution concerning the engine technology in every category, from touring car championships up to the F1. The strategy of the car manufacturers to bring motorsport engine technology closer to mass production one (e.g. turbo-charging, downsizing and direct injection) allows both to reduce development costs and to create a better image and technology transfer by linking motorsport activities to the daily business. Under these requirements the so-called Global Race Engine (GRE) concept has been introduced, giving the possibility to use one unique engine platform concept as basis for different engine specifications and racing categories. In order to optimize the performance of this kind of engines, especially due to the highly complex mixture formation mechanisms related to the direct injection, it is nowadays mandatory to resort to reliable 3D-CFD simulations.

This paper presents an autonomous cruise control for battery electric vehicles. The presented approach is based on the usage of predictive route data which is extracted out of a digital map and a wide range radar system in order to capture vehicles in front. By using the predictive route data and the information of the radar system, the autonomous cruise control can control the vehicle's speed over a wide range of driving situations without any driver interaction. The main aim of the presented autonomous cruise control is to optimize the battery electric vehicle's energy consumption. The main idea is to use predictive route data in order to calculate a consumption optimal vehicle speed trajectory by means of online optimization. The benefits of the autonomous cruise control are shown by means of real test drives and measured data evaluation.

In this paper the effect of aerodynamic modifications that influence the unsteady aerodynamic properties of a vehicle on the response of the closed loop system driver-vehicle under side wind conditions is investigated. In today's aerodynamic optimization the side wind sensitivity of a vehicle is determined from steady state values measured in the wind tunnel. There, the vehicle is rotated with respect to the wind tunnel flow to create an angle of attack. In this approach however, the gustiness that is inherent in natural wind is not reproduced. Further, unsteady forces and moments acting on the vehicle are not measured due to the limited dynamic response of the commonly used wind tunnel balances. Therefore, a new method is introduced, overcoming the shortcomings of the current steady state approach. The method consists of the reproduction of the properties of natural stochastic crosswind that are essential for the determination of the side wind sensitivity of a vehicle.