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By variation of the compression ratio the fuel consumption of high boosted gasoline engines can be reduced, due to operating with higher compression ratios at low load compared to an engine with fixed compression ratio. The two-stage VCR-system enables a high share of fuel saving potential relative to full variable systems. Considering a low cost manufacturability and a beneficial integratability into common engine architectures the length-adjustable conrod using an eccentric piston pin in the small eye has proved as the best concept. The adjustment is performed by a combination of gas and mass forces. This article describes the design of such a two-stage VCR-system as well as the functional testing under motored and fired engine operating conditions.

Within a project of the Research Association for Combustion Engines e.V., different measures for rising the temperature of exhaust gas aftertreatment components of both a passenger car and an industrial/commercial vehicle engine were investigated on a test bench as well as in simulation. With the passenger car diesel engine and different catalyst configurations, the potential of internal and external heating measures was evaluated. The configuration consisting of a NOx storage catalyst (NSC) and a diesel particulate filter (DPF) illustrates the potential of an electrically heated NSC. The exhaust aftertreatment system consisting of a diesel oxidation catalyst (DOC) and a DPF shows in simulation how variable valve timing in combination with electric heated DOC can be used to increase the exhaust gas temperature and thus fulfill the EU6 emission limits.

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.

Measures to improve the thermal efficiency of heavy- duty commercial vehicle engines with compression ignition continue to be an important topic in research and development. Increasing the compression ratio (CR) of the engine is a direct way to increase the process efficiency. However, to ensure an optimum combustion and emission behavior at very high compression ratio is challenging. In addition, the combustion and emission behavior of heavy-duty compression ignition (CI) engines with compression ratios beyond 21:1 has hardly been reported in the literature. In this study, a combination of the experimental and 3D-CFD based numerical methods were applied to a high compression ratio heavy duty engine to analyze the combustion process and emissions so as to evaluate the thermal efficiency improvement potential.

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.

The dilution of the cylinder charge using excess air enables both an increase in the net indicated efficiency and a decrease in the engine-out emissions of nitrogen oxides. The maximum excess air dilution capability in a spark-ignition engine depends on both the ignition of the charge and the flame propagation. These two aspects can be influenced by the fuel properties, which draw attention to the laminar burning velocity of alternative fuels to extend the lean limit. Cyclopentanone and anisole show promising values regarding the laminar burning velocity. However, there is a lack of engine investigations using these two fuels. To this end, both fuels were assessed in an engine application using experimental and numerical investigations. Cyclopentanone and anisole were investigated as neat components and as mixtures with conventional gasoline fuel, resulting in seven investigated fuels.

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.

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.

Faced with one of the greatest challenges of humanity – climate change – the European Union has set out a strategy to achieve climate neutrality by 2050 as part of the European Green Deal. To date, extensive research has been conducted on the CO2 life cycle analysis of mobile propulsion systems. However, achieving absolute net-zero CO2 emissions requires the adjustment of the relevant key performance indicators for the development of mobile propulsion systems. In this context, research is presented that examines the ecological and economic sustainability impacts of a hydrogen-fueled mild hybrid vehicle, a hydrogen-fueled 48V hybrid vehicle, a methanol-fueled 400V hybrid vehicle, a methanol-to-gasoline-fueled plug-in hybrid vehicle, a battery electric vehicle, and a fuel cell electric vehicle. For this purpose, a combined Life-Cycle Assessment (LCA) and Life-Cycle Cost Assessment was performed for the different propulsion concepts.

Modeling thermal systems in Battery Electric Vehicles (BEVs) is crucial for enhancing energy efficiency through predictive control strategies, thereby extending vehicle range. A major obstacle in this modeling is the often limited availability of detailed system information. This research introduces a methodology using neural networks for system identification, a powerful technique capable of approximating the physical behavior of thermal systems with minimal data requirements. By employing black-box models, this approach supports the creation of optimization-based operational strategies, such as Model Predictive Control (MPC) and Reinforcement Learning-based Control (RL). The system identification process is executed using MATLAB Simulink, with virtual training data produced by validated Simulink models to establish the method's feasibility. The neural networks utilized for system identification are implemented in MATLAB code.

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.