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The study of oil emission is of essential interest for the engine development of modern cars, as well as for the understanding of hydrocarbon emissions especially during cold start conditions. A laser mass spectrometer has been used to measure single aromatic hydrocarbons in unconditioned exhaust gas of a H2-fueled engine at stationary and transient motor operation. These compounds represent unburned oil constituents. The measurements were accompanied by FID and GC-FID measurements of hydrocarbons which represent the burned oil constituents. The total oil consumption has been determined by measuring the oil sampled by freezing and weighing. It has been concluded that only 10 % of the oil consumption via exhaust gas has burned in the cylinders. A correlation of the emission of single oil-based components at ppb level detected with the laser mass spectrometer to the total motor oil emission has been found.

A new analytical method for the automobile industry has been developed using a pulsed tunable laser system and a Reflectron time-of-flight mass spectrometer. The goal was to achieve the following conditions: High time resolution (〈100ms), high sensitivity (down to 1 ppm), high accuracy (10%) and applicability to most exhaust emission components. The main problem is the large number of components with very different and fast varying concentrations. For a preliminary list of 25 exhaust emission components, all necessary parameters have been determined. First results obtained from a real exhaust gas sample will be presented.

Time-resolved concentrations of formaldehyde and acetaldehyde in the exhaust gas have been investigated during transient motor operation, such as sudden change of speed and load, misfiring and switching off the fuel mixture control. To this purpose, a new laser mass spectrometer has been applied which is capable of measuring the concentrations of individual exhaust compounds with 1 ppm sensitivity at a sampling rate of 50 Hz corresponding to a sampling period of 20 ms. At sudden speed changes, high concentrations of aldehydes are observed, in particular during the phase of decreasing speed, i.e. after closing the throttle valve.

In this study, a fully optically accessible single-cylinder research engine is the basis for the visualization and generation of extensive knowledge about the in-cylinder processes of mixture formation, ignition and combustion of oxygenated synthetic fuels. Previous measurements in an all-metal engine showed promising results by using a mixture of dimethyl carbonate and methyl formate as a fuel substitute in a DISI-engine. Lower THC and NOx emissions were observed along with a low PN-value, implying low-soot combustion. The flame luminosity transmitted via an optical piston was split in the optical path to simultaneously record the natural flame luminosity with an RGB high-speed camera. The second channel consisted of OH*-chemiluminescence recording, isolated by a bandpass filter via an intensified monochrome high-speed camera.

Fast and exact measurement of engine oil consumption is a very difficult task. Our aim is to achieve this measurement at a common test bed without engine modifications. We resolved this problem with a new technique using Laser Mass Spectrometry to detect appropriate tracers in the raw engine exhaust. The tracers are added to the engine oil. to the engine oil. For detection of these tracers we use a Laser Mass Spectrometer (LAMS). This is a combination of resonant laser ionization (with an all-solid-state laser) and Time-of-Flight Mass Spectrometry. Currently this is the only way to detect oil originated molecules (like our tracers) in the raw exhaust very fast (50 Hz) and sensitive (ppb-region). Thus, engine mapping of oil consumption over load and speed can be performed in 1-2 days with about 90 measurements. Even measurement during dynamic engine operation is possible, but not quantitative (due to the lack of information about dynamic exhaust gas mass flow).

Laser induced fluorescence (LIF) is used to investigate oil transport mechanisms under real engine conditions. The engine oil is mixed with a dye that can be induced by a laser. The emitted light intensity from the dye correlates with the residual oil at the sensor position and the resulting oil film thicknesses can be precisely determined for each crank angle. However, the general expectation is not always achieved, e.g. an exact representation of piston ring barrel shapes. In order to investigate the responsible lubrication effects of this behavior, a new cavitation algorithm for the Reynolds equation has been developed. The solution retains the mass conservation and does not use any switch function in its mathematical approach. In contrast to common approaches, no vapor-liquid ratio is used, but one or several bigger bubbles are approximated, as have been observed in other experiments already.

Deep neural network models have been widely used for environment perception of intelligent vehicles. However, due to models’ innate probabilistic property, the lack of transparency, and sensitivity to data, perception results have inevitable uncertainties. To compensate for the weakness of probabilistic models, many pieces of research have been proposed to analyze and quantify such uncertainties. For safety-critical intelligent vehicles, the uncertainty analysis of data and models for environment perception is especially important. Uncertainty estimation can be a way to quantify the risk of environment perception. In this regard, it is essential to deliver a comprehensive survey. This work presents a comprehensive overview of uncertainty estimation in deep neural networks for environment perception of intelligent vehicles.

On the way to a climate-neutral mobility, synthetic fuels with their potential of CO2-neutral production are currently in the focus of internal combustion research. In this study, the C1-fuels methanol (MeOH), dimethyl carbonate (DMC), and methyl formate (MeFo) are tested as pure fuel mixtures and as blend components for gasoline. The study was performed on a single-cylinder engine in two configurations, thermodynamic and optical. As pure C1-fuels, the previously investigated DMC/MeFo mixture is compared with a mixture of MeOH/MeFo. DMC is replaced by MeOH because of its benefits regarding laminar flame speed, ignition limits and production costs. MeOH/MeFo offers favorable particle number (PN) emissions at a cooling water temperature of 40 °C and in high load operating points. However, a slight increase of NOx emissions related to DMC/MeFo was observed. Both mixtures show no sensitivity in PN emissions for rich combustions. This was also verified with help of the optical engine.

The performance of environment perceiving sensors such as e.g. lidar, radar, camera and ultrasonic sensors is safety critical for automated driving vehicles. Therefore, one has to assess the sensors’ performance to assure the automated driving system’s safety. The performance of these sensors is however to some degree sensitive towards adverse weather conditions. A challenge is to quantify the effect of adverse weather conditions on the sensor’s performance early in the development of an automated driving system. This challenge is addressed in this work for lidar sensors. The lidar equation was previously employed in this context to derive estimates of a lidar’s maximum range in different weather conditions. In this work, we present a stochastic simulation framework based on a probabilistic extension of the lidar equation, to quantify the effect of adverse rainfall conditions on a lidar’s raw detection performance.