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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.

Software complexity of vehicles is constantly growing especially with additional autonomous driving features being introduced. This increases the risk for bugs in the system, when the car is delivered. According to a car manufacturer, more than 90% of availability problems corresponding to Electronic Control Unit (ECU) functionality are either caused by software bugs or they can be resolved by applying software updates to overcome hardware issues. The main concern are sporadic errors which are not caught during the development phase since their trigger condition is too unlikely to occur or is not covered by the tests. For such systems, there is a need of safe and secure infield diagnosis. In this paper we present a tool software architecture with remote access, which facilitates standard read/write access, an efficient channel interface for communication and file I/O, and continuous trace.

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.

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.

The European Commission has released strict emission regulations for passenger cars in the past decade in order to improve air quality in cities and limit harmful emission exposure to humans. In the near future, even stricter regulations containing more realistic/demanding driving scenarios and covering more exhaust gas components are expected to be released. Passenger cars fueled with gasoline are one contributor to unhealthy air conditions, due to the fact that gasoline engines emit harmful air pollutants. One option to minimize harmful emissions would be to utilize specifically tailored, low emission synthetic fuels or fuel blends in internal combustion engines. Methyl formate and dimethyl carbonate are two promising candidates to replace or substitute gasoline, which in previous studies have proven to significantly decrease harmful pollutants.

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).

Existing electronic combustion engine control systems only guarantee a desired air-to-fuel-ratio λ in stationary operation. In order to achieve the desired λ also in in-stationary use of the engine, it is necessary to use new-technology-based control systems. Artificial Intelligence provides methods to cope with difficulties like wide operation range, unknown nonlinearities and time delay. We will propose a strategy for control of a Spark Ignition Engine to determine the mass of air inside the combustion chambers with the highest accuracy. Since Neural Networks are universal approximators for multidimensional nonlinear static functions they can be used effectively for identification and compensation purposes of unknown nonlinearities in closed control loops.

Among the issues affecting the design process of a vehicle, there is the lack of multidisciplinary knowledge among the different teams involved. This often leads to the risk of loosing important key points from the initial concept idea of designers to the final vehicle package definition made by engineers. Therefore, this study builds up a method based on parameters defining exterior aesthetic priorities according to car segment to support engineers involved in the automotive design process. In particular, during the early design-engineering phase, this method should help them to understand better vehicle proportions defined by designers. This work is currently used in the course of design and simulation of road vehicles (Chair of Automotive Technology, TUM) to explain fundamentals of automotive design.

With increasing levels of driving automation, the perception provided by automotive environment sensors becomes highly safety relevant. A correct assessment of the sensors’ perception reliability is therefore crucial for ensuring the safety of the automated driving functionalities. There are currently no standardized procedures or guidelines for demonstrating the perception reliability of the sensors. Engineers therefore face the challenge of setting up test procedures and plan test drive efforts. Null Hypothesis Significance Testing has been employed previously to answer this question. In this contribution, we present an alternative method based on Bayesian parameter inference, which is easy to implement and whose interpretation is more intuitive for engineers without a profound statistical education. We show how to account for different environmental conditions with an influence on sensor performance and for statistical dependence among perception errors.

The objective of this study was to reduce pollutant emissions during cold start conditions in a spark-ignited direct injection engine, by exploring the potential of oxygenated fuels. With their high oxygen content and lack of direct C-C bonds, they effectively reduce particle number (PN) and NOx emissions under normal conditions. Methanol was chosen due to its wide availability. As methanol is toxic to humans and associated with cold-start issues, a second promising synthetic fuel was selected to be benchmarked against gasoline, comprising 65 vol% of dimethyl carbonate and 35 vol% of methyl formate (C65F5). Currently, there is a lack of detailed investigations on the cold start performance for both oxygenated fuels utilizing today’s injector capabilities. Spray measurements were caried out in a constant volume chamber to assess the spray of C65F35. Reduced fuel temperature increased spray-penetration length and compromised fast vaporization.

This study discusses model-based injection rate estimation in common rail diesel injectors exhibiting aging phenomena. Since they result in unexpected injection behavior, aging effects like coking or cavitation may impair combustion performance, which justifies the need for new modeling and estimation approaches. To predict injection characteristics, a simulation model for the bottom section of the injector is introduced, with a main focus on modeling the hydraulic components. Using rail pressure and control piston lift as inputs, a reduced model is then derived in state-space representation, which may be used for the application of an observer in hardware-in-the-loop (HIL) environments. Both models are compared and validated with experimental data, with which they show good agreement. Aging effects and nozzle wear, which result in model uncertainties, are considered using a fault model in combination with an extended Kalman filter (EKF) observer scheme.

In motorsports, aerodynamic development processes target to achieve gains in performance. This requires a comprehensive understanding of the prevailing aerodynamics and the capability of analysing large quantities of numerical data. However, manual analysis of a significant amount of Computational Fluid Dynamics (CFD) data is time consuming and complex. The motivation is to optimize the aerodynamic analysis workflow with the use of deep learning architectures. In this research, variants of 3D deep learning models (3D-DL) such as Convolutional Autoencoder (CAE) and U-Net frameworks are applied to flow fields obtained from Reynolds Averaged Navier Stokes (RANS) simulations to transform the high-dimensional CFD domain into a low-dimensional embedding. Consequently, model order reduction enables the identification of inherent flow structures represented by the latent space of the models.

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.

Aerodynamic design and thermal management are some of the most important tasks when developing new concepts for the flow around tractor-trailers. Today, both experimental and numerical studies are an integral part of the aerodynamic and thermal design processes. A variety of studies have been conducted how the aerodynamic design reduces the drag coefficient for fuel efficiency as well as for the construction of radiators to provide cooling on tractor-trailers. However, only a few studies cover the combined effect of the aerodynamic and thermal design on the air temperature of the under hood flow [8, 13, 16, 17, 20]. The objective of this study is to analyze the heat transfer through forced convection for a scaled Cab-over-Engine (CoE) tractor-trailer model with under hood flow. Different design concepts are compared to provide low under hood air temperature and efficient cooling of the sub components.

Temperature rise in traction contact areas is one important factor that influences traction coefficient. For examining the influence of temperature rise on the traction coefficient, it is necessary to first clarify temperature rise in the traction contact area. In this article, temperature rise in the traction contact areas is discussed in three major parts. First, measured temperature distributions on the traction contact surface under conditions of high rolling speed and minute amounts of sliding and spinning, such as those which are found in a toroidal CVT, using a twin-disc test machine and thin-film platinum sensors are shown. Second, the above experimental results are compared with results from a traction analysis program (REIB99). Characteristics of calculated results were qualitatively in good agreement with measured results.

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.

Finite element simulation (FE) makes it possible to analyze the structural dynamic behavior of vehicle seat structures in early design phases to meet Noise-Vibration-Harshness (NVH) requirements. For this purpose, linear simulations are usually used, which neglect many nonlinear mechanical properties of the real structure. These models are trimmed to fit global vibration behavior based on the complex description of contact or jointed definitions. Targeted design is therefore only possible to a limited extent. The aim of this work is to characterize the entire seat structure and its sub-components in order to identify the main contributors using experimental and simulative data. The Lagrange Multiplier Frequency Based Substructuring (LM-FBS) method is used for this purpose. Therefore, the individual subsystems of seat frame, seat backrest and headrest are characterized under different conditions.

Transfer path analyses of vehicle bodies are widely considered as an important tool in the noise, vibration and harshness design process, as they enable the identification of the dominating transfer paths in vibration problems. It is highly beneficial to model uncertain parameters in early development stages in order to account for possible variations on the final component design. Therefore, parameter studies are conducted in order to account for the sensitivities of the transfer paths with respect to the varying input parameters of the chassis components. To date, these studies are mainly conducted by performing sampling-based finite element simulations. In the scope of a sensitivity analysis or parameter studies, however, a large amount of large-scale finite element simulations is required, which leads to extremely high computational costs and time expenses. This contribution presents a method to drastically reduce the computational burden of typical sampling-based simulations.

Aging effects such as coking or cavitation in the nozzle of common rail (CR) diesel injectors deteriorate combustion performance. This is of particular relevance when it comes to complying with emission legislation and demonstrates the need for detecting and compensating aging effects during operation. The first objective of this paper is to analyze the influence of worn nozzles on the injection rate. Therefore, measurements of commercial solenoid common rail diesel injectors with different nozzles are carried out using an injection rate analyzer of the Bosch type. Furthermore, a fault model for typical aging effects in the nozzle of the injector is presented together with two methods to detect and identify these effects. Both methods are based on a multi-domain simulation model of the injector. The needle lift, the control piston lift and the pressure in the lower feed line are used for the fault diagnosis.

A main challenge when developing next generation architectures for automated driving ECUs is to guarantee reliable functionality. Today’s fail safe systems will not be able to handle electronic failures due to the missing “mechanical” fallback or the intervening driver. This means, fail operational based on redundancy is an essential part for improving the functional safety, especially in safety-related braking and steering systems. The 2-out-of-2 Diagnostic Fail Safe (2oo2DFS) system is a promising approach to realize redundancy with manageable costs. In this contribution, we evaluate the reliability of this concept for a symmetric and an asymmetric Electronic Power Steering (EPS) ECU. For this, we use a Markov chain model as a typical method for analyzing the reliability and Mean Time To Failure (MTTF) in majority redundancy approaches. As a basis, the failure rates of the used components and the microcontroller are considered.