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The target of the upcoming automotive emission regulations is to promote a fast transition to near-zero emission vehicles. As such, the range of ambient and operating conditions tested in the homologation cycles is broadening. In this context, the proposed work aims to thoroughly investigate the potential of post-oxidation phenomena in reducing the light-off time of a conventional three-way catalyst. The study is carried out on a turbocharged four-cylinder gasoline engine by means of experimental and numerical activities. Post oxidation is achieved through the oxidation of unburned fuel in the exhaust line, exploiting a rich combustion and a secondary air injection dedicated strategy. The CFD methodology consists of two different approaches: the former relies on a full-engine mesh, the latter on a detailed analysis of the chemical reactions occurring in the exhaust line.

In order to achieve the climate targets, a mix of different powertrain technologies must be pursued to effectively reduce emissions. By producing hydrogen based on renewable energy sources, it becomes a reasonable choice for fueling internal combustion engines. The specific molecular properties of hydrogen thereby open up new possibilities for favorably influencing the combustion process of engines. The present paper deals with the analysis of a single-cylinder engine with passive pre-chamber ignition and a port fuel injection system, which was adapted for lean hydrogen operation. In this way, the test unit was operated in various load and speed ranges with lambda values from 1.5 to 2.5 and achieved up to 23 bar indicated mean effective pressure. The focus of this work is on the numerical investigation of the hydrogen combustion and its effects on the engine system. Special attention is hereby paid to the influence of different lambda operations.

There is a growing need for low-emissions concepts due to stricter emission regulations, more stringent homologation cycles, and the possibility of a ban on new engines by 2035. Of particular concern are the conditions during a cold start, when the Three-Way Catalyst is not yet heated to its light-off temperature. During this period, the catalyst remains inactive, thereby failing to convert pollutants. Reducing the time needed to reach this temperature is crucial to comply with the more stringent emissions standards. The post oxidation by means of secondary air injection, illustrated in this work, is a possible solution to reduce the time needed to reach the above-mentioned temperature. The strategy consists of injecting air into the exhaust manifold via secondary air injectors to oxidize unburned fuel that comes from a rich combustion within the cylinder.

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.

Numerical methodologies for aeroacoustic analyses are increasingly crucial for car manufacturers to optimize the effectiveness of vehicle development. In the present work, a hybrid numerical tool based on the combination of a delayed detached-eddy simulation and a finite element model, which relies on the Lighthill’s acoustic analogy and the acoustic perturbation equations, is presented. The computational aeroacoustics is performed by the software OpenFOAM and Actran, concerning respectively the CFD and the FEM. The aeroacoustic behavior of the SUV Lamborghini Urus at a cruising speed of 140 km/h has been investigated. The main aerodynamic noise phenomena occurring in the side mirror region in a frequency range up to 5 kHz are discussed. The numerical simulations have been verified against the measurements performed in the aeroacoustic wind tunnel of the University of Stuttgart, operated by FKFS.

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.

Prospective combustion engine applications require the highest possible energy conversion efficiencies for environmental and economic sustainability. For conventional Spark-Ignition (SI) engines, the quasi-hemispherical flame propagation combustion method can only be significantly optimized in combination with high excess air dilution or increased combustion speed. However, with increasing excess air dilution, this is difficult due to decreasing flame speeds and flammability limits. Pre-Chamber (PC) initiated jet ignition combustion systems significantly shift the flammability and flame stability limits towards higher dilution areas due to high levels of introduced turbulence and a significantly increased flame area in early combustion stages, leading to considerably increased combustion speeds and high efficiencies. By now, vehicle implementations of PC-initiated combustion systems remain niche applications, especially in combination with lean mixtures.

Future combustion engine applications require highest possible energy conversion efficiencies to reduce their environmental impact and be economically competitive. So far, spark-ignition (SI) engine combustion development mostly consisted of optimizing the hemispherical flame propagation combustion method. Thereby, a significant efficiency increase is only achievable in combination with high excess air dilution or increased combustion speed. However, with increasing excess air dilution, this is difficult due to decreasing flame speeds and flammability limits. Simultaneously, researchers have been investigating homogeneous charge compression ignition (HCCI) that achieves higher efficiencies due to its rapid volume reaction combustion and also enables high excess air dilution. However, the combustion is complex to control as it is initiated by auto-ignition (AI) processes. In-cylinder conditions reliably need to be reproduced to prevent damaging pre-ignitions.

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.

The development of future gasoline engines is dominated by the study of new technologies aimed at reducing the engine negative environmental impact and increase its thermal efficiency. One common trend is to develop smaller engines able to operate in stoichiometric conditions across the whole engine map for better efficiency, lower fuel consumption, and optimal conversion rate of the three-way catalyst (TWC). Water injection is one promising technique, as it significantly reduces the engine knock tendency and avoids fuel enrichment for exhaust temperature mitigation at high power operation. With the focus on reducing the carbon footprint of the automotive sector, another vital topic of research is the investigation of new alternative CO2-neutral fuels or so-called eFuels. Several studies have already shown how these new synthetic fuels can be produced by exploiting renewable energy sources and can significantly reduce engine emissions.

In this paper, a serial/parallel hybrid electric vehicle with a 17 kWh battery and 400 V voltage level is simulated. The vehicle is a C-segment vehicle, which has optimized driving resistances. It also has an external recharge possibility, which enables fully electric driving. The vehicle uses an Otto-engine concept as well as two electric motors. One motor is a permanent magnet synchronous motor and can be used as traction motor or generator, the other one is an induction motor used as main traction motor for the vehicle. The vehicle uses a 2-speed gearbox, where the electric motors are mounted in P2-configuration. To reach optimal results for the fuel consumption, an operating strategy based on the Equivalent Consumption Minimization Strategy (ECMS) is introduced and implemented in the vehicle simulation.

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.

Automotive engineers use the wind tunnel to improve a vehicle’s aerodynamic properties on the road. However, a car driving on the road does not experience the steady-state, uniform flow characteristic of the wind tunnel. Wind, terrain and traffic all cause the flow experienced by the vehicle to be highly transient. Therefore, it is imperative to understand the effects of forces acting on the vehicle resulting from unsteady flow. To this end, the FKFS swing® installed in the University of Stuttgart’s model scale wind tunnel was used to create 36 different incident flow signals with time-resolved yaw angles. The cD values of five different 25% vehicle models, each with a notchback and a squareback configuration, were measured while under the influence of the aforementioned signals. The vehicle models were chosen to ensure a variety of different geometries, but at the same time also to enable isolated comparison of specific geometric properties.

Synthetic fuels from renewable energy sources can be a significant contribution on the roadmap to sustainable mobility. Porsche sees electro-mobility as the top priority, but eFuels produced by renewable electricity are an effective addition to support the defossilization of the transportation sector. In addition to the sustainability aspect, the composition and properties of eFuels can be optimized via the synthetic fuel production path. The use of optimized fuel formulations has a direct influence on combustion and emission behavior. The latter is one focus of the development of internal combustion engines in the wake of constantly tightening emissions legislation. The increasing restrictions on vehicles with internal combustion engines require the reduction of emissions. Particulate matter emissions are among others the focus of criticism. The composition and properties of fuels can reduce particulate emissions and the formation of unburned hydrocarbons to a high degree.

In order to operate effectively, exhaust gas aftertreatment (EAT) systems require a certain temperature level. The trend towards higher grades of hybridisation causes longer switch-off phases of the internal combustion engine (ICE) during which the EAT components cool down. Additionally, efficiency enhancements of the ICE result in lower exhaust gas temperatures. In combination with further strengthening of the legal requirements regarding tailpipe emissions, new approaches are desired to ensure reliable emission reductions under all conditions. One possibility to achieve a faster warm-up of the EAT system is to place it upstream of the turbine, where temperatures are higher. Although, the extra thermal inertia and larger volume upstream of the turbine delay the throttle response, even a light hybridisation is sufficient for compensating the dynamic loss.

The introduction of real driving emission measurements increases the need of improved transient engine behavior while keeping the emissions to a minimum. A possible way of enhancing the transient engine behavior is the targeted usage of scavenging. Scavenging is realized by an inlet- and exhaust-valve overlap. Fresh scavenging air flows directly from intake manifold through the cylinder into the exhaust manifold. Therefore, the mass flow at the turbine increases and causes a reduced turbo lag, which results in a more dynamic engine behavior. The unburned oxygen causes a decrease of the three-way catalyst (TWC) conversion rate. To keep the TWC operation close to stoichiometry, a rich combustion is performed. The rich combustion products (most notably carbon monoxide) mix in the exhaust manifold and react with oxygen so that the conversion rate of the TWC is ensured.

The wind tunnel is the standard tool in the development and improvement of vehicle aerodynamics. Usually, automotive wind tunnels contain an open test section, which results in a shear layer developing on the edge of the jet. This shear layer brings instabilities that can lead to resonance effects in the wind tunnel influencing the pressure distribution in the test section. To investigate the resonance effects, the classic wind tunnel corrections were applied to averaged drag measurements recorded in a resonance and nonresonance configuration of the model scale wind tunnel of the University of Stuttgart. The Mercker-Wiedemann-Method shows good compensation for the differing pressure gradients. Pressure measurements on the surface of the DrivAer Notchback model show different separation points on the rear window for measurements in resonance and nonresonance configuration. This means that the resonance effects can influence the separation significantly.

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 reliability of electronic components is of increasing importance for further progress towards automated driving. Thermal aging processes such as electromigration is one factor that can negatively affect the reliability of electronics. The resulting failures depend on the thermal load of the components within the vehicle lifetime - called temperature collective - which is described by the temperature frequency distribution of the components. At present, endurance testing data are used to examine the temperature collective for electronic components in the late development stage. The use of numerical simulation tools within Vehicle Thermal Management (VTM) enables lifetime thermal prediction in the early development stage, but also represents challenges for the current VTM processes [1, 2]. Due to the changing focus from the underhood to numerous electronic compartments in vehicles, the number of simulation models has steadily increased.