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The ongoing energy transition will have a profound impact on future mobility, with electrification playing a key role. Battery electric vehicles (EVs) are the dominant technology, relying on the conversion of alternating current (AC) from the grid to direct current (DC) to charge the traction battery. This process involves power electronic components such as rectifiers and DC/DC converters operating at high switching frequencies in the kHz range. Fast switching is essential to minimize losses and improve efficiency, but it might also generate electromagnetic interferences (EMI). Hence, electromagnetic compatibility (EMC) testing is essential to ensure reliable system operations and to meet international standards. During DC charging, the AC/DC conversion takes place off-board in the charging station, allowing for better cooling and larger components, resulting in increased power transfer, currently up to 350 kW.

Test of autonomous systems is mostly brute force and ad-hoc thus being neither efficient nor transparent. Though requirements invite for a situational transparency, a framework is missing to judge quality of requirements and derived test-cases. Practical challenges are state explosion, difficulty to derive corner cases, no systematic safety of the intended functionality as specified, lack of accepted KPI, etc. Maintaining a valid safety case is hardly possible with such adaptive systems and continuous software updates. To achieve trusted autonomous vehicles, test cases must be generated automatically while at same time providing coverage (e.g., indicating progress with KPI), efficiency (e.g., limiting the amount of regression testing) and transparency (e.g., showing how specific corner cases are tested in case of accidents). This paper provides a method for automatically generating test cases for AI-based autonomous systems and compares it with existing testing methods.

Urban air mobility (UAM) is a fast-growing industry that utilizes electric vertical take-off and landing (eVTOL) technologies to operate in densely populated urban areas with limited space. However, atmospheric icing serves as a limitation to its operational envelope as in-flight icing can happen all year round anywhere around the globe. Since icing in smaller aviation systems is still an emerging topic, there is a necessity to study icing of eVTOL rotors specifically. Two rotor geometries were chosen for this study. A small 15-inch rotor was selected to illustrate a multirotor UAV drone, while a large 80-inch rotor was chosen to represent a UAM passenger aircraft. The ice accretion experiments were conducted in an icing wind tunnel on the small 15-inch rotor. The icing simulations were performed using FENSAP-ICE. The ice accretion simulations of the 15-inch rotor sections at –5 °C show a large, rather streamlined ice shape instead of the expected glaze ice characteristics.

Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains.

Knocking is one of today’s main limitations in the ongoing efforts to increase efficiency and reduce emissions of spark-ignition engines. Especially for synthetic fuels or any alternative fuel type in general with a much steeper increase of the knock frequency at the KLSA, such as hydrogen, precise knock prediction is crucial to exploit their full potential. This paper therefore proposes a post-processing tool enabling further investigations to continuously gain better understanding of the knocking phenomenon. In this context, evaluation of local auto-ignitions preceding knock is crucial to improve knowledge about the stochastic occurrence of knock but also identify critical engine design to further optimize the geometry. In contrast to 0D simulations, 3D CFD simulations provide the possibility to investigate local parameters in the cylinder during the combustion.

Predictive physical modeling is an established method used in the development process for automotive components and systems. While accurate predictions can be issued after tuning model parameters, long computation times are expected depending on the complexity of the model. As requirements for components and systems continuously increase, new optimization approaches are constantly being applied to solve multidimensional objectives and resulting conflicts optimally. Some of those approaches are deemed not feasible, as the computational times for required single predictions using conventional simulation models are too high. To address this issue it is proposed to use data-driven model such as neural networks. Previous efforts have failed due to sparse data sets and resulting poor predictive ability. This paper introduces an AI Toolchain used for data-driven modeling of combustion engine components. Two methods for generating scalable and fully variable datasets will be shown.

In this paper, the differences between a production car of the 2018 A-class and an early stage vehicle model with a mostly similar outer skin are examined experimentally and numerically. The aerodynamic development of vehicles at Mercedes-Benz is divided into several phases. When comparing force coefficients differences can be observed between these distinct hardware stages as well as when comparing steady state simulations to wind tunnel measurements. In early phases when prototype vehicles are not yet available, so-called aero foam models are used. These are well-defined full-sized vehicle models, as the outer skin is milled from Polyurethane. Important aerodynamic characteristics such as a motor compartment with a cooling module, deflecting axles with rotatable wheels and underbody covers are represented.

The automotive industry uses supercharging in combination with various EGR strategies to meet the increasing demand for Diesel engines with high efficiency and low engine emissions. The charge air is heated by the EGR and the compression in the turbocharger to such an extent that high NOx emissions and a reduction in engine performance occurs. For this reason, the charge air cooler cools down the charge air before it enters the air intake manifold. In case of low pressure EGR, the charge air possesses a high moisture content and under certain operating conditions an accumulation of condensate takes place within the charge air cooler. During demanding engine loads, the condensate is entrained from the charge air cooler into the combustion chamber, resulting in misfiring or severe engine damage.

Further improvements of internal combustion engines to reduce fuel consumption and to face future legislation constraints are strictly related to the study of mixture formation. The reason for that is the desire to supply the engine with homogeneous charge, towards the direction of a global stoichiometric blend in the combustion chamber. Fuel evaporation and thus mixture quality mostly depend on injector atomization features and charge motion within the cylinder. 3D-CFD simulations offer great potential to study not only injector atomization quality but also the evaporation behavior. Nevertheless coupling optical measurements and simulations for injector analysis is an open discussion because of the large number of influencing parameters and interactions affecting the fuel injection’s reproducibility. For this purpose, detailed numerical investigations are used to describe the injection phenomena.

The industry is working intensively on the precision of thermal management. By using complex thermal management strategies, it is possible to make engine heat distribution more accurate and dynamic, thereby increasing efficiency. Significant efforts are made to improve the cooling efficiency of the engine water jacket by using 3D CFD. As well, 1D simulation plays a significant role in the design and analysis of the cooling system, especially for considering transient behaviour of the engine. In this work, a practice-oriented universal method for creating a 1D water jacket model is presented. The focus is on the discretization strategy of 3D geometry and the calculation of heat transfer using Nusselt correlations. The basis and reference are 3D CFD simulations of the water jacket. Guidelines for the water jacket discretization are proposed. The heat transfer calculation in the 1D-templates is based on Nusselt-correlations (Nu = Nu(Re, Pr)), which are derived from 3D CFD simulations.

As a consequence of reduced fuel consumption, direct injection gasoline engines have already prevailed against port fuel injection. However, in-cylinder fuel homogenization strongly depends on charge motion and injection strategies and can be challenging due to the reduced available time for mixture formation. An insufficient homogenization has generally a negative impact on the combustion and therefore also on efficiency and emissions. In order to reach the targets of the intensified CO2 emission reduction, further increase in efficiency of SI engines is essential. In this connection, 0D/1D simulation is a fundamental tool due to its application area in an early stage of development and its relatively low computational costs. Certainly, inhomogeneities are still not considered in quasi dimensional combustion models because the prediction of mixture formation is not included in the state of the art 0D/1D simulation.

Engine valve flow coefficients are used to describe the flow throughput performance of engine valve/port designs, and to model gas exchange in 0D/1D engine simulation. Valve flow coefficients are normally determined at a stationary flow test bench, separately for intake and exhaust side, in the absence of the piston. However, engine operation differs from this setup; i. a. the piston might interact with valve flow around scavenging top dead center, and instead of steady boundary conditions, valve flow is nearly always subjected to pressure pulsations, due to pressure wave reflections within the gas exchange ports. In this work the influences of piston position and flow pulsation on valve flow coefficients are investigated for different SI engine geometries by means of 3D CFD and measurements at an enhanced flow test bench.

In the automotive industry, there is a continued need to improve the development process and handle the increasing complexity of the overall vehicle system. One major step in this process is a comprehensive and complementary approach to both simulation and testing. Knowledge of the overall dynamic vehicle behavior is becoming increasingly important for the development of new control concepts such as integrated vehicle dynamics control aiming to improve handling quality and ride comfort. However, with current well-established test systems, only separated and isolated aspects of vehicle dynamics can be evaluated. To address these challenges and further merge the link between simulation and testing, the Institute of Internal Combustion Engines and Automotive Engineering (IVK), University of Stuttgart is introducing a new Handling Roadway (HRW) Test System in cooperation with The Research Institute of Automotive Engineering and Vehicle Engines Stuttgart (FKFS) and MTS Systems Corporation.

A wide variety of applications such as driver assistant and energy management systems are researched and developed in virtual test environments. The safe testing of the applications in early stages is based on parameterizable and reproducible simulations of different driving scenarios. One possibility is modeling the microscopic driving behavior to simulate the longitudinal vehicle dynamics of individual vehicles. The currently used driver models are characterized by a conflict regarding comprehensibility, accuracy and calibration effort. Due to the importance for further analyses this conflict of interests is addressed by the presentation of a new microscopic driver model in this paper. The proposed driver model stores measured driving behaviors with its statistical distributions in maps. Thereby, the driving task is divided into free flow, braking in front of stops and following vehicles ahead. This makes it possible to display the driving behavior in its entirety.

State-of-the-art avionics systems are standardized, e.g. the computing system of the flying vehicle is composed of pre-defined and pre-qualified modules of a standardized avionics platform. Integrated Modular Avionics (IMA) is the most popular representative, but not the only one. Two challenges of standardized avionics platform are system design and configuration. Since the high numbers of functions, modules, and constraints for modern air vehicles, bringing up the optimal system architecture is a difficult job if carried out manually. The subsequent process of creating millions of configuration parameters is time consuming and error prone. Both issues are similar and are, in general, processable by algorithms. Algorithms proved to provide significant support for current system design issues and might be mandatory in future, when avionics become self-organizing and the design and configuration are derived by the platform itself.

The development and qualification of distributed and highly safety-critical avionics systems implicate high efforts and risks. The resulting costs usually limit implementations like fly-by-wire systems to the military or commercial airliner domains. The aim of previous and ongoing research at the Institute of Aircraft Systems at University of Stuttgart is the reduction of these costs and therefore open up their benefits, inter alia, to general aviation, remotely piloted or unmanned aircraft. An approach for an efficient development is the application of a platform based development which supports the reuse of software and hardware components. The Flexible Platform adopts this approach. It is accompanied by a tool suite which automates the design and parameter instantiation, documentation generation and the generation of verification artifacts for a platform instance. This paper presents the approach for the requirement document generation compliant to ARP4754A and DO-178C.

Engine valve flow coefficients are not only used to characterize the performance of valve/port designs, but also for modelling gas exchange in 0D/1D engine simulation. Flow coefficients are usually estimated with small pressure ratios and at ambient air conditions. In contrast, the ranges for pressure ratio, pressure and temperature level during engine operation are much more extensive. In this work the influences of these three parameters on SI engine poppet valve flow coefficients are investigated using 3D CFD and measurements for validation. While former investigations already showed some pressure ratio dependencies by measurement, here the use of 3D CFD allows a more comprehensive analysis and a deeper understanding of the relevant effects. At first, typical ranges for the three mentioned parameters during engine operation are presented.

A simulation approach to predict the amount of snow which is penetrating into the air filter of the vehicle’s engine is important for the automotive industry. The objective of our work was to predict the snow ingress based on an Eulerian/Lagrangian approach within a commercial CFD-software and to compare the simulation results to measurements in order to confirm our simulation approach. An additional objective was to use the simulation approach to improve the air intake system of an automobile. The measurements were performed on two test sites. On the one hand we made measurements on a natural test area in Sweden to reproduce real driving scenarios and thereby confirm our simulation approach. On the other hand the simulation results of the improved air intake system were compared to measurements, which were carried out in a climatic wind tunnel in Stuttgart.

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.

Longitudinal models are used to evaluate different vehicle-engine concepts with respect to driving behavior and emissions. The engine is generally map-based. An explicit calculation of both fluid dynamics inside the engine air path and cylinder combustion is not considered due to long computing times. Particularly for dynamic certification cycles (WLTC, US06 etc.), dynamic engine effects severely influence the quality of results. Hence, an evaluation of transient engine behavior with map-based engine models is restricted to a certain extent. The coupling of detailed 1D-engine models is an alternative, which rapidly increases the model computation time to approximately 300 times higher than that of real time. In many technical areas, the Fourier transformation (FT) method is applied, which makes it possible to represent superimposed oscillations by their sinusoidal harmonic oscillations of different orders.