Search Results

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.

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).

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.

Both, the continuous strengthening of the exhaust emission legislation and the striving for a substantial reduction of carbon dioxide output in the traffic sector depict substantial requirements for the development of future diesel engines. These engines will comprise not only the mandatory diesel oxidation catalyst (DOC) and particulate filter DPF but a NOx aftertreatment system as well - at least for heavier vehicles. The oxidation catalysts as well as currently available NOx aftertreatment technologies, i.e., LNT and SCR, rely on sufficient exhaust gas temperatures to achieve a proper conversion. This is getting more and more critical due to the fact that today's and future measures for CO₂ reduction will result in further decrease of engine-out temperatures. Additionally this development has to be considered in the light of further engine electrification and hybridization scenarios.

Tightening of emission norms necessitate intensified research in the field of emissions reduction. Fuel research opens up a vast area of potential improvement, since combustion behavior and the nature of the combustion products can be heavily influenced by fuel composition. In this paper, the effects of fuel properties on combustion and emissions shall be discussed, based on the study of standard diesel fuel, two types of diesel-like fuels and a kerosene fuel. Investigations were conducted on a single cylinder heavy duty direct-injected diesel engine operating under part-homogeneous combustion in the part-load operating range. For this purpose, a statistical design of experiments method (DOE) was utilized in order to evaluate the influence of each fuel property and, thus, develop a model for all selected fuels. Variation in EGR rates, injection and air patterns have significant effects on the combustion in the fuels under investigation.

Lignocellulosic biomass consists of (hemi-) cellulose and lignin. Accordingly, an integrated biorefinery will seek to valorize both streams into higher value fuels and chemicals. To this end, this study evaluated the overall combustion performance of both cellulose- and lignin derivatives, namely the high cetane number (CN) di-n-butyl ether (DnBE) and low CN anisole, respectively. Said compounds were blended both separately and together with EN590 diesel. Experiments were conducted in a single cylinder compression ignition engine, which has been optimized for improved combustion characteristics with respect to low emission levels and at the same time high fuel efficiency. The selected operating conditions have been adopted from previous “Tailor-Made Fuels from Biomass (TMFB)” work.