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State-of-the-art spark-ignition engines mainly rely on the quasi-hemispherical flame propagation combustion method. Despite significant development efforts to obtain high energy conversion efficiencies while avoiding knock phenomena, achieved indicated efficiencies remain around 35 - 40 %. Further optimizations are enabled by significant excess air dilution or increased combustion speed. However, flammability limits and decreasing flame speeds with increasing air dilution prevent substantial improvements. Pre-Chamber (PC) initiated jet ignition combustion systems improve flame stability and shift flammability limits towards higher dilution levels due to increased turbulence and a larger flame area in the early Main-Chamber (MC) combustion stages. Simultaneously, the much-increased combustion speed reduces knock tendency, allowing the implementation of an innovative combustion method: PC-initiated jet ignition coupled with Spark-Assisted Compression Ignition (SACI).

For metrological traceability of pressure sensors, static calibration procedures are standard. If these sensors are used in dynamic systems, unexpected phenomena or deviations occur in the recorded signal characteristics. By setting up a dynamic pressure calibration facility, it is possible to investigate this dynamic behavior and learn about the interactions between sensor and investigated system. To be able to identify the disturbing influences and interactions occurring during calibration and in subsequent measurement use, it is necessary to increase the existing understanding of the system. In the context of the contribution, the calibration procedure used, its properties such as repeatability, reproducibility as well as the system interaction of the influencing variables are analyzed. Special attention is paid to the effects of varying gas content in the calibration medium, its influence on the system and on the observed phenomena occurring.

The proportion of new registrations with battery-electric and hybrid powertrains is rising steadily. This shows the strong trend in the automotive industry away from conventional powertrains with internal combustion engines. The aim is to reduce the transport sector's contribution to CO2 emissions. However, it should be noted that this only applies when renewable energy is used. Studies show the relevance of the system boundaries under consideration, which makes the application of Life Cycle Assessment indispensable. According to these studies, the various types of powertrains differ only slightly in their greenhouse gas impact. Rather, the energy supply chain plays a significant role. Moreover, a ban on combustion engines would lead to an additional increase in cumulative CO2 emissions. An important aspect on the way to sustainable mobility solutions is addressing the existing fleet.

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.

Ignition delay times are major information needed to allow the simulation of auto-ignition and knocking combustion in internal combustion engines (ICEs). Due to their variance over changing boundary conditions (BC) and limitations of measurement processes, a common way to obtain them is via reaction kinetic simulations. To facilitate and accelerate the simulation process with varying operating conditions and gas composition definitions, an efficient tool that uses Cantera’s Python interface has been created. It allows the end-user to easily calculate the ignition delay data needed for engine simulation without the necessity for in-depth knowledge of the underlying processes. All calculations are based on the creation of a homogeneously mixed gaseous mixture corresponding to engine-based environmental conditions. Depending on the desired fuel, oxidizer, temperature, pressure, water, and exhaust gas recirculation (EGR) rate, the resulting reactant composition is computed.

Knocking is still one of today’s major limitations regarding efficiency-increasing measures for SI combustion engines. Due to the complex stochastic nature of the phenomenon, not only prediction and consideration within the engine development is of relevance. A further challenge is control of the phenomenon during engine operation, with the aim of maximizing the efficiency while preventing engine damage and maintaining the driver comfort. Conventional knock control is characterized by detecting knock events and subsequently adjusting the spark timing depending on whether knock was detected. This paper proposes a new knock control concept based on the prediction and direct control of the knock frequency, compared to the conventional reactive and indirect control of the knock frequency. For the prediction of the knock frequency, a calculation approach based on three different parameters is utilized.

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.

The European Union has defined legally binding CO2-fleet targets for new cars until 2030. Therefore, improvement of fuel economy and carbon dioxide emission reduction is becoming one of the most important issues for the car manufacturers. Today’s conventional car powertrain systems are reaching their technical limits and will not be able to meet future CO2 targets without further improvement in combustion efficiency, using low carbon fuels (LCF), and at least mild electrification. This paper demonstrates a highly efficient and performant combustion engine concept with a passive pre-chamber spark plug, operating at stoichiometric conditions and powered with liquefied petroleum gas (LPG). Even from fossil origin, LPG features many advantages such as low carbon/hydrogen ratio, low price and broad availability. In future, it can be produced from renewables and it is in liquid state under relatively low pressures, allowing the use of conventional injection and fuel supply components.

Increasing combustion pressure, low viscosity oils, less oil supply and the increasing stress due to downsizing of internal combustion engines (ICE) lead to higher loads within the bearing. As the mechanical and tribological loads on the piston pin bearings have a direct impact on the service life and function of the overall engine system, it is necessary to develop a robust tribological design approach. Regarding the piston pin bearing of a diesel engine, this study aims to describe the effects of different parameters on a DLC-coated piston pin within the bearing. Therefore, an external engine part test rig, which applies various forces to the connecting rod and measures the torque on a driven pin, is used to carry out validation measurements. The special feature of the test bench is the way the piston is beared. For the first experiments, the piston crown is placed against a plate (plate-bearing); later, this plate-bearing is replaced by a hydrostatic bearing.

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.

The increasingly stringent targets for the automotive industry towards sustainability are being addressed not only with the improvement of engine efficiency, but also with growing research about alternative, synthetic, and CO2-neutral fuels. These fuels are produced using renewable energy sources, with the goal of making them CO2-neutral and also to reduce a significant amount of engine emissions, especially particulate matter (PM) and total hydrocarbon (THC). The objective of this work is to study the behavior and the potential of an eFuel developed by Porsche, called POSYN (POrscheSYNthetic) and to compare it with a standard gasoline.

The goal of reducing the impact of road transportation on the environment can be reached by different approaches. The use of non-fossil synthetic fuels from renewable energy sources in the entire fleet of internal combustion engine vehicles is only one promising pathway to minimize the vehicle’s carbon footprint during the use phase. The steadily tightening emissions legislation confront the developers of future combustion engines with major challenges: Historically, the chemical and physical improvement of the combustion process, tail pipe emissions reduction and the development of optimized after-treatment systems were linked to improvements in fuel quality. In order to further decrease exhaust gas emissions, the optimization of the chemical composition of renewable fuels are a basic requirement.

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.

Intensified emission regulations as well as consumption demands lead to an increasing significance of carbon monoxide (CO) emissions for diesel engines. On the one hand, the quantity of CO raw emissions is important for emission predictions as well as for the exhaust gas after treatment. On the other hand, CO emissions are also important for predicting combustion efficiency and thus fuel consumption, since a part of unreleased chemical energy of the fuel is still bound in the CO molecules. Due to these reasons, a simulation model for predicting CO raw emissions was developed for diesel engines based on a phenomenological two-zone model. The CO model takes three main sources of CO emissions of diesel engines into account: Firstly, it contains a sub model that describes CO from local understoichiometric areas. Secondly, CO emissions from overmixed regions are considered.

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.

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.