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The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation.

Noise, vibration and harshness (NVH) is one of the most important performance evaluation aspect of electric motors. Among the different causes of the NVH issues of electrical drives, the high-frequency spatial and temporal harmonics of the electrical drive system is of great importance. To reduce the tonal noise of the electric motors, harmonic injection methods can be applied. However, a lot of the existing related work focuses more on improving the optimization process of the parameter settings of the injected current/flux/voltage, which are usually limited to some specific working conditions. The applicability and effectivity of the algorithm to the whole frequency/speed range are not investigated. In this paper, a multi-domain pipeline of harmonic injection controller design for a permanent magnet synchronous motor (PMSM) is proposed.

Due to increasingly strict emission regulations, the demand for internal combustion engine performance has enhanced. Combustion stability is one of the main research focuses due to its impacts on the emission level. Moreover, the combustion instability becomes more significant under the lean combustion concept, which is an essential direction of internal combustion engine development. The combustion instability is represented as the cycle-to-cycle variation. This paper presents a quasi-dimensional model system for predicting the cycle-to-cycle variation in 0D/1D simulation. The modeling is based on the cause-and-effect chain of cycle-to-cycle variation of spark ignition engines, which is established through the flow field analysis of large eddy simulation results [1]. In the model system, varying parameters are turbulent kinetic energy, the distribution of air-to-fuel equivalence ratio, and the in-cylinder velocity field.

Numerical analyses of the liquid fuel injection and subsequent fuel-air mixing for a high-tumble direct injection engine with an active pre-chamber ignition system at operation conditions of 2000 RPM are presented. The Navier-Stokes equations for compressible in-cylinder flow are solved numerically using a hierarchical Cartesian mesh based finite-volume method. To determine the fuel vapor before ignition large-eddy flow simulations are two-way coupled with the spray droplets in a Lagrangian Particle Tracking (LPT) formulation. The combined hierarchical Cartesian mesh ensures efficient usage of high performance computing systems through solution adaptive refinement and dynamic load balancing. Computational meshes with approximately 170 million cells and 1.0 million spray parcels are used for the simulations.

Pre-ignition in a boosted spark-ignition engine can be triggered by several mechanisms, including oil-fuel droplets, deposits, overheated engine components and gas-phase autoignition of the fuel-air mixture. A high pre-ignition resistance of the fuel used mitigates the risk of engine damage, since pre-ignition can evolve into super-knock. This paper presents the pre-ignition propensities of 11 RON 89-100+ gasoline fuel blends in a single-cylinder research engine. Albeit the addition of two high-octane components (methanol and reformate) to a toluene primary reference fuel improved the pre-ignition resistance, one high-RON fuel experienced runaway pre-ignition at relatively low boost pressure levels. A comparison of RON 96 blends showed that the fuel composition can affect pre-ignition resistance at constant RON.

Due to increasingly strict emission regulations, lean combustion concept has become an essential direction of internal combustion engine development to reduce engine emissions. However, lean combustion will lead high combustion instability and unpredictive engine emissions. The combustion instability is represented as the high cycle-to-cycle variation. Therefore, understanding the mechanism of cycle-to-cycle variation is crucial for the internal combustion engine design. This paper investigates the cause-and-effect chain of cycle-to-cycle variation of spark ignition engines using 3D CFD simulations with CONVERGE v3.0. The cyclic variations were simulated through Large Eddy Simulations, and the simulations based on Reynolds-averaged Navier–Stokes were used as supplements. The analysis focuses on two key factors that determine the combustion process: the turbulent intensity and the homogeneity of the air/fuel mixture.

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.

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.

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.

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.

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.

The increasingly stringent limits on pollutant emissions from internal combustion engine-powered vehicles require the optimization of advanced combustion systems by means of virtual development and simulation tools. Among the gaseous emissions from spark-ignition engines, the unburned hydrocarbon (HC) emissions are the most challenging species to simulate because of the complexity of the multiple physical and chemical mechanisms that contribute to their emission. These mechanisms are mainly three-dimensional (3D) resulting from multi-phase physics - e.g., fuel injection, oil-film layer, etc. - and are difficult to predict even in complex 3D computational fluid-dynamic (CFD) simulations. Phenomenological models describing the relationships between the physical-chemical phenomena are of great interest for the modeling and simplification of such complex mechanisms.

In the field of gasoline powertrain calibration, the challenges are growing due to ever shorter time-to-market requirements and a simultaneous increase in powertrain complexity. In addition, the great variety of vehicle variants requires an increasing number of prototypes for calibration and validation tasks within the framework of the current Real Driving Emissions (RDE) regulations and the expected Post Euro 6 emission standards. Hardware-in-the-Loop (HiL) simulations have been introduced successfully to support the calibration tasks in parallel to the conventional vehicle development activities. The HiL approach enables a more reliable compliance with emission limits and improves the quality of calibrations, while reducing the number of prototype vehicles, test resources and thus overall development costs.

Knock control is one of the most vital functions for safe and fuel-efficient operation of gasoline engines. However, all knock control strategies rely on accurate knock detection to operate the engine close to the optimal set point. Knock detection is usually calibrated on the engine test bench, requiring the engine to run with knocking combustion in a time-consuming multi-stage campaign. Model-based calibration significantly reduces calibration loops on the test bench. However, this method requires a large effort in building and validating the model, which is often limited by the lack of function documentation, available measurements or hardware representation. As the software models are often not available, function structures vary between manufacturers and sub model functions are often documented as black boxes. Hence, using the model-based approach is not always possible.

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.

For the regeneration of the Lean NOx Trap (LNT) a rich air-to-fuel ratio must be generated. This operation is very critical and has low combustion stability, especially in low load operation. A certain minimum engine load is always required for the regeneration phase. In the Real Driving Emissions this minimum engine load can be undercut over a long period of time. Hence, a reliable regeneration phase is not possible. The aim of these investigations is to extend the engine map range in which regeneration is possible towards lower loads. This is done by means of a variable valve train with second exhaust valve lift, which increases the internal residual gas amount. This in turns increases the temperature at start of combustion in the cylinder. Especially at low load and low combustion stability this leads to a stabilization of the combustion process. This advantage in combustion stability can be used for a reduction of the minimum engine load.