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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.

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.

The aeroacoustic development of vehicles is still mainly carried out in wind tunnels under steady flow conditions, although the real situation is different. However, as discussed in several earlier publications, a vehicle experiences unsteady, turbulent flow on road, which results for example from natural wind, wakes of other vehicles, or obstacles at the roadside in combination with side wind. The resulting temporal variations of the wind noise inside the cabin affect the passengers’ comfort and safety through fatigue. To be able to also consider the unsteady aeroacoustics in the vehicle development process, a comprehensive method has been developed that is presented in full for the first time in this paper. The on-road situation is simulated in a realistic and reproducible manner in the full-scale wind tunnel of the University of Stuttgart by means of an active turbulence generator, developed by FKFS.

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].

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 new CO2 and emissions limits imposed to European manufacturers require the adoption of different innovative solutions, such as the use of potentially CO2-neutral synthetic fuels alongside a tailored development of the internal combustion engine, as an excellent solution to accompany the hybridization of vehicles. Dr.Ing. h.c. F. Porsche AG and FKFS, already partners for the development of engines with eFuels, propose a new study carried out on a research engine, investigating the combination of Porsche synthetic gasoline (POSYN) with an engine with millerization and passive pre-chamber. The use of CO2-neutral fuels allow for an immediate reduction in CO2 emissions from all cars already on the market, particularly since Porsche is one of the manufacturers whose cars remain in use for the longest time. The data collected on a single-cylinder engine test bench, for different fuels, with conventional spark plug are used as input for the calibration of 3D-CFD simulations.

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.

As engine knock limits the efficiency of spark ignition engines and consequently further reduction of CO2 emissions, SI engines are typically designed to operate at the knock boundary. Therefore, a precise knock model is necessary to consider this phenomenon in an engine process simulation. The basis of the introduced 0D/1D knock model is to predict when the unburnt mixture auto-ignites, since auto-ignitions precede knocking events. The knock model further needs to evaluate the auto-ignition, because not every auto-ignition results in engine knock. As the introduced model’s prediction of the auto-ignition onset is already validated at extensive variations of operating conditions, this publication focusses on its evaluation. For this, two new, independent criteria are developed that take the pre-reactions of the unburnt mixture before the start of combustion into account to calculate a respective threshold for the auto-ignition onset at the knock boundary.

As the late combustion phase in SI engines is of high importance for a further reduction of fuel consumption and especially emissions, the impacts of unburnt mass, located in a small volume with a relatively large surface near the wall and in the top land volume, is of high relevance throughout the range of operation. To investigate and quantify the respective interactions, a state of the art Mercedes-Benz single cylinder research SI-engine was equipped with extensive measurement technology. To detect the axial and radial temperature distribution, several surface thermocouples were applied in two layers around the top land volume. As an additional reference, multiple surface thermocouples in the cylinder head complement the highly dynamic temperature measurements in the boundary zones of the combustion chamber.

Engine knock is limiting the efficiency of spark ignition engines and consequently further reduction of CO2 emissions. Thus, an combustion process simulation needs a well working knock model to take this phenomenon into account. As knocking events result from auto-ignitions, the basis of a knock model is the accurate modeling of the latter. For this, the introduced 0D/1D knock model calculates the Livengood-Wu integral to estimate the state of the pre-reactions of the unburnt mixture and considers the two-stage auto-ignition of gasoline fuels, which occurs at specific boundary conditions. The model presented in this publication is validated against measurement data of a single cylinder engine. For this purpose, more than 12 000 knocking working cycles are investigated, covering extensively varied operating conditions for a wide-ranging validation.

To meet the requirements of strict CO2 emission regulations in the future, internal combustion engines must have excellent efficiencies for a wide operating range. In order to achieve this goal, various technologies must be applied. Additionally, fuels other than gasoline should also be considered. In order to investigate the potential of the efficiency improvement, a SI engine was designed and optimized using 0D/1D methods. Some of the advanced features of this engine model include: High stroke-to-bore-ratio, variable valve timings with Miller cycle, EGR, cylinder deactivation, high turbulence concept, variable compression ratio and extreme downsizing. The fuel of choice was gasoline. With the proper application of technologies, the fuel consumption at the most relevant operating window could be decreased by approximately 10% in comparison to a state-of-the-art spark-ignited direct-injection four-cylinder passenger car engine.

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.