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This paper describes the development of a 0-D-sulfur poisoning model for a NOx storage catalyst (NSC). The model was developed and calibrated using findings and data obtained from a passenger car diesel engine used on testbed. Based on an empirical approach, the developed model is able to predict not only the lower sulfur adsorption with increasing temperature and therefore the higher SOx (SO2 and SO3) slip after NSC, but also the sulfur saturation with increasing sulfur loading, resulting in a decrease of the sulfur adsorption rate with ongoing sulfation. Furthermore, the 0-D sulfur poisoning model was integrated into an existing 1-D NOx storage catalyst kinetic model. The combination of the two models results in an “EAS Model” (exhaust aftertreatment system) able to predict the deterioration of NOx-storage in a NSC with increasing sulfation level, exhibiting higher NOx-emissions after the NSC once it is poisoned.

The optimization of engine NVH is still an important aspect for vehicle interior and exterior noise radiation. To optimize the engine noise / vibration contribution to the vehicle, a complete understanding of the excitation mechanism, the vibration transfer in the engine structure and the radiation efficiency of the individual engine components is required. Concerning the excitation within the engine, a very efficient analysis methodology for the combustion- and mechanical excitation within gasoline and diesel engines has been developed. Out of this methodology a software tool has been designed for a fast, efficient and detailed evaluation of the combustion- and mechanical excitation content of total engine noise. Recently this software tool has been successfully applied in engine NVH optimization work for defining the best optimization strategies for engine NVH reduction and noise quality improvement especially with respect to combustion excitation.

The paper outlines and discusses the possibilities of a new instrumentation tool for the analysis of engine and gearbox noise radiation and the prediction of pass-by noise from powertrain test cell measurements. Based on a 32 channel data acquisition board, the system is intended to be quick and easy to apply in order to support engineers during their daily work in the test cell. The pass-by prediction is a purely experimental approach with test cell recordings being weighted by measured transfer functions (from the powertrain compartment to the pass-by point).

The paper examines the application of six vehicle safety rating systems to a common crash database, for the purpose of making a comparison of the rating results produced by each system and to develop an understanding of the differences which emerge. The rating results are compared based on rank order of crashworthiness of vehicle models, and relationships between each pair of results. Finally, the results with their respective confidence limits are used to classify each vehicle model as having inferior, not defined or superior crashworthiness, and the classification is used to compare the relative discrimination of the methods.

Combustion of premixed stoichiometric charge is free of soot particle formation. Consequently, the development of direct injection (DI) spark ignition (SI) engines aims at providing premixed charge to avoid or minimize soot formation in order to meet particle emissions targets. Engine development methods not only need precise engine-out particle measurement instrumentation but also sensors and measurement techniques which enable identification of in-cylinder soot formation sources under all relevant engine test conditions. Such identification is made possible by recording flame radiation signals and with analysis of such signals for premixed and diffusion flame signatures. This paper presents measurement techniques and analysis methods under normal engine and vehicle test procedures to minimize sooting combustion modes in transient engine operation.

The effectiveness of ADAS addressing property damage has an increasing impact on car manufacturers, insurers and customers, as accident avoidance or mitigation can lead to loss reduction. In order to obtain benefits, it is essential that ADAS primarily address monetarily relevant accident scenarios. Furthermore, sensor technologies and algorithms have to be configured in a way that relevant accident situations can be sufficiently avoided at reasonable system costs. A new methodology is developed to identify and configure monetarily effective parameters for ADAS during parking and maneuvering. ADAS parameters e.g. relevant accident scenarios, required crash avoidance speeds and different sensor layouts are analyzed and evaluated using a real-world in-depth accident database of insurance claims provided by Allianz Center for Technology and Allianz Automotive Innovation Center. For this purpose, a sensitivity analysis is conducted to identify most monetarily effective accident scenarios.

An optical sensor system provides access to standard SI engine combustion chambers via the cylinder head gasket. Flame radiation within the plane of the gasket is observed with optical fibers which are arranged to allow the tomographic reconstruction of flame distribution. The effect of convective in-cylinder air motion generated by variations of inlet ports and combustion chamber geometries on flame propagation is directly visible. A high degree of correlation between flame intensity distribution and NOx emission levels yields a useful assessment of combustion chamber configurations with minimum emission levels. The location of knock centers is identified.

A reliable environment perception is a requirement for safe automated driving. For evaluating and demonstrating the reliability of the vehicle’s environment perception, field tests offer testing conditions that come closest to the vehicle’s driving environment. However, establishing a reference ground truth in field tests is time-consuming. This motivates the development of a procedure for learning the vehicle’s perception reliability from fleet data without the need for a ground truth, which would allow learning the perception reliability from fleet data. In Berk et al. (2019), a method based on Bayesian inference to determine the perception reliability of individual sensors without the need for a ground truth was proposed. The model utilizes the redundancy of sensors to learn the sensor’s perception reliability. The method was tested with simulated data.

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.

A key criterion for launching autonomous vehicles on real roads is the knowledge of their capability to ensure traffic safety. In contrast to ADAS, deriving this measure of safety is difficult to achieve as the functional scope of an autonomous driving function exceeds by far the one of ADAS. As a consequence, real-world testing solely is not sufficient enough to cover the required test volume. This assessment problem imposes new requirements on a valid test concept for automated driving. A possible solution represents simulation by enabling it to generate reliable test kilometers. As a first step, we discuss in this paper the feasibility of simulation frameworks to re-simulate a real-world test in certain scenarios. We will demonstrate that even with ground truth information of the vehicle odometry and corresponding environment model an acceptable accordance of functional behavior is not guaranteed.

As an alternative to the element based methods, recently a wave based technique (WBT) has been developed. Since it is based on the indirect Trefftz approach, exact solutions of the governing differential equation are used to approximate the dynamic field variables. This paper discusses the extensions of the WBT for the analysis of engine noise radiation problems in 3 dimensions under anechoic conditions. Furthermore, necessary extensions of shape functions, numerical integration and a methodology to create a WBT radiation models are described. The performance of the method compared to a commercial BEM solution is demonstrated with a real engine example.

The introduction of autonomous vehicles has gained significant attention due to its potential to revolutionize mobility and safety. A critical aspect underpinning the functionality of these autonomous vehicles is their sensor perception system. Demonstrating the reliability of the environment perception sensors and sensor fusion algorithms is, therefore, a necessary step in the development of automated vehicles. Field tests offer testing conditions that come closest to the environment of an automated vehicle in the future. However, a significant challenge in field tests is to obtain a reference truth of the surrounding environment. Here, we propose a pipeline to assess the sensor reliabilities without the need for a reference truth. The pipeline uses a model to estimate the reliability of redundant sensors. To do this, it relies on a binary representation of the surrounding area, which indicates either the presence or absence of an object.