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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.

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.

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.

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.

Studies of diesel system deposits continue to be the subject of interest and publications worldwide. The introduction of high pressure common rail systems resulting in high fuel temperatures in the system with the concomitant use of fuels of varying solubilizing ability (e.g. ULSD and FAME blends) have seen deposits formed at the tip of the injector and on various internal injector components. Though deposit control additives (DCAs) have been successfully deployed to mitigate the deposit formation, work is still required to understand the nature and composition of these deposits. The study of both tip and internal diesel injector deposits (IDID) has seen the development of a number of bench techniques in an attempt to mimic field injector deposits in the laboratory. One of the most used of these is the Jet Fuel Thermal Oxidation Tester or JFTOT (ASTM D3241).

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.

In order to achieve high customer satisfaction and to avoid high warranty costs caused by component failures of the power-train of hybrid electric vehicles (HEV), car manufacturers have to optimize the dimensioning of these elements. Hence, it is obligatory for them to gain knowledge about the different types of vehicle usage being predominant all over the world. Therefore, in this paper we present a Data Mining system that employs a Random Forest (RF) based dissimilarity measure in the dimensionality reduction technique t-Distributed Stochastic Neighbor Embedding (t-SNE) to automatically identify and visualize different types of vehicle usage by applying these methods to aggregated logged on-board data, i.e., load spectrum data. This kind of data is calculated and recorded directly on the control units of the vehicles and consists of aggregated numerical data, like the histogram of the velocity signal or the traveled distance of a vehicle.

As known de-icing methods use a high amount of energy or environmentally harmful chemicals, research has focused lately on passive de-icing by functional surfaces with an improved removal of ice (de-icing) or a reduced formation of it (anti-icing). Inspired by the Lotus plant leaf, a “superhydrophobic” surface can be produced by the combination of a hierarchical micro/nanoscale roughness and a hydrophobic surface coating. By a hot stamping process we have generated differently shaped microstructures (cylinders, ellipses) on polyurethane (PU) films which were afterwards coated by a plasma enhanced chemical vapor deposition (PECVD) process with thin, hydrophobic fluorocarbon films. This combination of methods could be a process for the production of large area functionalized films. PU films are suitable for outdoor use, because they are resistant against erosion and UV radiation. The films can be glued to different geometries and can easily be exchanged if damaged.

Today, LRI is a proven manufacturing technology for both small and large scale structures (e.g. sailboats) where, in most cases, experience and limited prototype experimentation is sufficient to get a satisfactory design. However, large scale aerospace (and other) structures require reproducible, high quality, defect free parts, with excellent mechanical performance. This requires precise control and knowledge of the preforming (draping and manufacture of the composite fabric preforms), their assembly and the resin infusion. The INFUCOMP project is a multi-disciplinary research project to develop necessary Computer Aided Engineering (CAE) tools for all stages of the LRI manufacturing process. An ambitious set of developments have been undertaken that build on existing capabilities of leading drape and infusion simulation codes available today. Currently the codes are only accurate for simple drape problems and infusion analysis of RTM parts using matched metal molds.

Gasoline direct injection engines can greatly improve the fuel economy, but the idea mixture distribution cannot be easily controlled. In this paper, the linearized instability sheet atomization (LISA) and large eddy simulation (LES) implemented into KIVA-3V code were used to study the gasoline hollow cone spray process for gasoline direct injection (GDI) in a constant volume vessel. The three-dimensional results show that the LISA model can effectively simulate the gasoline hollow cone spray and obtain the string structure compared to the experiment data. And the velocity interpolation method can reduce the grid dependency of spray simulation. Using dense grid (about 8 million cells) in LES and RANS all can obtain the good spray tip penetration and width. Unlike diesel spray, for gasoline spray there are not big difference between the results using LES and RANS. In additional the ambient pressure significantly influence the gasoline spray shape.

In this work, large eddy simulation (LES) with a K-equation subgrid turbulent kinetic energy model is implemented into the CFD code KIVA3V to study the features of liquid fuel spray and combustion using gradually varying grid in a constant volume chamber. The characteristic time-scale combustion model (CTC) incorporating a turbulent timescale is adopted to predict the combustion process and the SHELL auto-ignition model is used to predict auto-ignition. Combustion is also simulated using Parallel Detailed Chemistry with Lu's n-heptane reduced mechanism (58 species), which has been added into the KIVA3V-LES code. The computational results are compared with Sandia experimental data for non-reacting and reacting cases. As a result, LES can capture the complex structure of the spray and temperature distribution as well as the trend of ignition delay and flame lift-off length variations. Better results are obtained using the Parallel Detailed Chemistry than the CTC model.

More than 20 years after the first presentation of the heat transfer equation according to Bargende [1,2], it is time to introduce some useful additions and enhancements, with respect to new and advanced combustion principles like diesel- and gasoline- homogeneous charge compression ignition (HCCI). In the existing heat transfer equation according to Bargende the calculation of the actual combustion chamber surface area is formulated in accordance with the work of Hohenberg. Hohenberg found experimentally that in the piston top land only about 20-30% of the wall heat flux values from the combustion chamber are transferred to the liner and piston wall. Hohenberg explained this phenomenon that is caused by lower gas temperature and convection level in charge within the piston top land volume. The formulation just adds the existing piston top land surface area multiplied by a specified factor to the surface of the combustion chamber.

This paper presents a large eddy simulation study of the liquid spray mixing with hot ambient gas in a constant volume vessel under engine-like conditions with the injection pressure of 1500 bar, ambient density 22.8 kg/m₃, ambient temperature of 900 K and an injector nozzle of 0.09 mm. The simulation results are compared with the experiments carried out by Pickett et al., under similar conditions. Under modern direct injection diesel engine conditions, it has been argued that the liquid core region is small and the droplets after atomization are fine so that the process of spray evaporation and mixing with the air is controlled by the heat and mass transfer between the ambient hot gas and central fuel flow. To examine this hypothesis a simple spray breakup model is tested in the present LES simulation. The simulations are performed using an open source compressible flow solver, in OpenFOAM.

The paper describes a novel method for calculating the residual gas fraction and the trapping efficiency in a 2 stroke engine. Assuming one dimensional compressible flow through the inlet and exhaust ports, the method estimates the instantaneous mass flowing in and out from the combustion chamber; later the residual gas fraction and trapping efficiency are estimated combining together the perfect displacement and perfect mixing scavenging models. It is assumed that when the intake port opens, the fresh mixture is pushing out the burned charge without any mixing and after a multiple of the time needed for the largest eddy to perform one rotation, the two gasses are instantly mixed up together and expelled. The result is a very simple algorithm that does not require much computational time and is able to estimate with high level of precision the trapping efficiency and the residual gas fraction in 2 stroke engines.

It has been known from multi-zone simulations that HCCI combustion can be significantly affected by temperature stratification of the in-cylinder gas. With the same combustion timing (i.e. crank angles at 50% heat release, denoted as CA50), large temperature stratification tends to prolong the combustion duration and lower down the in-cylinder pressure-rise-rate. With low pressure-rise-rate HCCI engines can be operated at high load, therefore it is of practical importance to look into more details about how temperature stratification affects the auto-ignition process. It has been realized that multi-zone simulations can not account for the effects of spatial structures of the stratified temperature field, i.e. how the size of the hot and cold spots in the temperature field could affect the auto-ignition process. This question is investigated in the present work by large eddy simulation (LES) method which is capable of resolving the in-cylinder turbulence field in space and time.