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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.

Computational models directly derived from data gained increased interest in recent years. Data-driven approaches have brought breakthroughs in different research areas such as image-, video- and audio-processing. Often denoted as Machine Learning (ML), today these approaches are not widely applied in the field of vehicle Noise, Vibration and Harshness (NVH). Works combining ML and NVH mainly discuss the topic with respect to psychoacoustics, traffic noise, structural health monitoring and as improvement to existing numerical simulation methods. Vehicle interior noise is a major quality criterion for today’s automotive customers. To estimate noise levels early in the development process, deterministic system descriptions are created by utilizing time-consuming measurement techniques. This paper examines whether pattern-recognizing algorithms are suitable to conduct the prediction process for a steering system.

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.

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.

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 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.

In times when automated driving is becoming increasingly relevant, dynamic simulators present an appropriate simulation environment to faithfully reproduce driving scenarios. A realistic replication of driving dynamics is an important criterion to immerse persons in the virtual environments provided by the simulator. Motion Cueing Algorithms (MCAs) compute the simulator’s control input, based on the motions of the simulated vehicle. The technical restrictions of the simulator’s actuators form the main limitation in the execution of these input commands. Typical dynamic simulators consist of a hexapod with six degrees of freedom (DoF) to reproduce the vehicle motion in all dimensions. Since its workspace dimensions are limited, significant improvements in motion capabilities can be achieved by expanding the simulator with redundant DoF by means of additional actuators.

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.

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.

In this paper the design of a robust control system for a parallel hybrid drivetrain is presented The drivetram is based on a continuously variable transmission (CVT) and is therefore a highly nonlinear multiple-input-multiple-output system (MIMO-System) Input-Output-Linearization offers the possibility of linearizing and of decoupling the system Since for example the vehicle mass varies with the load and the efficiency of the gearbox depends strongly on the actual working point, an exact linearization of the plant will mostly fail Therefore a robust control algorithm based on sliding mode is used to control the drivetrain

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 order to reduce emissions of spare ignition engines using a three way catalyst, a stoichiometric air-fuel ratio must be guaranteed in stationary and transient operation of the engine. This aim can be reached by using a specific feed-forward structure for the control of the paths of air and fuel based on identification abilities of Artificial Intelligence. As approximators for multidimensional nonlinear static functions we will use specific Neural Networks (NN) together with sophisticated stability-proven learning structures. The acquired knowledge within the NN determines our control action mainly through using feed-forward structures. Our investigations are based on the so-called mean-value-modelling approach of SI engines; it is our aim to present this strategy.

The increasing complexity of automotive functions which are necessary for improved driving assistance systems and automated driving require a change of common vehicle architectures. This includes new concepts for E/E architectures such as a domain-oriented vehicle network based on powerful Domain Control Units (DCUs). These highly integrated controllers consolidate several applications on different safety levels on the same ECU. Hence, the functions depend on a strictly separated and isolated implementation to guarantee a correct behavior. This requires middleware layers which guarantee task isolation and Quality of Service (QoS) communication have to provide several new features, depending on the domain the corresponding control unit is used for. In a first step we identify requirements for a middleware in automotive DCUs. Our goal is to reuse legacy AUTOSAR based code in a multicore domain controller.

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.

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.

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.

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.

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.

Bicycle-drawn cargo trailers with an electric drive to enable the transportation of high cargo loads are used as part of the last-mile logistics. Depending on the load, the total mass of a trailer can vary between approx. 50 and 250 kg, potentially more than the mass of the towing bicycle. This can result in major changes in acceleration and braking behavior of the overall system. While existing systems are designed primarily to provide sufficient power, improvements are needed in the powertrain control system in terms of driver safety and comfort. Hence, we propose a novel prototype that allows measurement of the tensile force in the drawbar which can subsequently be used to design a superior control system. In this context, a sinusoidal force input from the cyclist to the trailer according to the cadence of the cyclist is observed. The novelty of this research is to analyze whether torque impulses of the cyclist can be reduced with the help of Model Predictive Control (MPC).