Search Results

The introduction of real driving emissions cycles and increasingly restrictive emissions regulations force the automotive industry to develop new and more efficient solutions for emission reductions. In particular, the cold start and catalyst heating conditions are crucial for modern cars because is when most of the emissions are produced. One interesting strategy to reduce the time required for catalyst heating is post-oxidation. It consists in operating the engine with a rich in-cylinder mixture and completing the oxidation of fuel inside the exhaust manifold. The result is an increase in temperature and enthalpy of the gases in the exhaust, therefore heating the three-way-catalyst. The following investigation focuses on the implementation of post-oxidation by means of scavenging in a four-cylinder, turbocharged, direct injection spark ignition engine. The investigation is based on detailed measurements that are carried out at the test-bench.

As a consequence of reduced fuel consumption, direct injection gasoline engines have already prevailed against port fuel injection. However, in-cylinder fuel homogenization strongly depends on charge motion and injection strategies and can be challenging due to the reduced available time for mixture formation. An insufficient homogenization has generally a negative impact on the combustion and therefore also on efficiency and emissions. In order to reach the targets of the intensified CO2 emission reduction, further increase in efficiency of SI engines is essential. In this connection, 0D/1D simulation is a fundamental tool due to its application area in an early stage of development and its relatively low computational costs. Certainly, inhomogeneities are still not considered in quasi dimensional combustion models because the prediction of mixture formation is not included in the state of the art 0D/1D simulation.

Intensified emission regulations as well as consumption demands lead to an increasing significance of unburned hydrocarbon (UHC) emissions for diesel engines. On the one hand, the quantity of hydrocarbon (HC) raw emissions is important for emission predictions as well as for the exhaust after treatment. On the other hand, HC emissions are also important for predicting combustion efficiency and thus fuel consumption, since a part of unreleased chemical energy of the fuel is still bound in the HC molecules. Due to these reasons, a simulation model for predicting HC raw emissions was developed for diesel engines based on a phenomenological two-zone model. The HC model takes three main sources of HC emissions of diesel engines into account: Firstly, it contains a sub-model that describes the fuel dribble out of the injector after the end of injection. Secondly, HC emissions from cold peripheral zones near cylinder walls are determined in another sub-model.

As a promising solution to improve fuel efficiency of a long-haul heavy duty truck with diesel engine, organic Rankine cycle (ORC) based waste heat recovery system (WHR) by utilizing the exhaust gas from internal combustion engine has continuously drawn attention from automobile industry in recent years. The most attractive concept of ORC-based WHR system is the conversion of the thermal energy of exhaust gas recirculation (EGR) and exhaust gas from Tailpipe (EGT) to kinetic energy which is provided to the engine crankshaft. Due to a shift of the operating point of the engine by applying WHR system, the efficiency of the overall system increases and the fuel consumption reduces respectively. However, the integration of WHR system in truck is challenging by using engine cooling system as heat sink for Rankine cycle. The coolant mass flow rate influences strongly on the exhaust gas bypass which ensures a defined subcooling after condenser to avoid cavitation of pump.

Fulfilling exhaust emissions regulations and meet customer performance needs mainly drive the current engine development. Turbocharging system plays a key role for that. Currently turbocharging should provide highest engine power density at high engine speed by also allowing a very responsive performance at low end. This represents a trade-off in turbocharger development. A large scaled turbine allows having moderate exhaust gas back pressure for peak power region, but leading to loss of torque in low engine speed. In the last years of engine development scavenging helped to get away a bit from this trade-off as it increases the turbine mass flow and also reduces cylinder internal residual gas at low engine speed. The mostly in-use lean strategy runs air fuel ratios of closed to stoichiometric mixture in cylinder and global (pre catalyst) of λ = 1.05 to l = 1.3. This will be out of the narrow air fuel ratio band of λ = 1 to ensure NOx conversion in the 3-way-catalyst.

Until a few years ago the discussion of reduction of CO₂ emissions was completely out of place in motorsports. Nowadays, also in this field, car manufacturers want to investigate different approaches towards a more responsible and sustainable concept. For this target an interesting and feasible solution is the use of methane as an alternative fuel. At the 2009 edition of the 24-hour endurance race of the Nürburgring the Volkswagen Motorsport GmbH, in addition to vehicles powered by gasoline engines, introduced two vehicles powered by turbocharged CNG engines. The aim was to prove that also an "environment-friendly" concept is able to provide the required efficiency, dynamic and reliability for a successful participation in motorsports. After the success in the 2009 edition the engagement has been continued in 2010; this time exclusively with CNG vehicles.

The field of vision of the driver during wet road conditions is essential for safety at all times. Additionally, the safe use of the increasing number of sensors integrated in modern cars for autonomous driving and intelligent driver assistant systems has to be ensured even under challenging weather conditions. To fulfil these requirements during the development process of new cars, experimental and numerical investigations of vehicle soiling are performed. This paper presents the surface contamination of self- and foreign-soiling tested in the wind tunnel. For these type of tests, the fluorescence method is state-of-the-art and widely used for visualizing critical areas. In the last years, the importance of parameters like the contact angle have been identified when designing the experimental setup. In addition, new visualization techniques have been introduced.

Due to the demands for today's passenger cars regarding fuel consumption and emissions, exhaust turbo charging has become a fundamental step in achieving these goals. Especially in upper and middle class vehicles it is also necessary to consider the noise comfort. Today, floating bushings are mainly used as radial bearings in turbochargers. In the conventional operating range of the turbocharger dynamic instability occurs in the lubrication films of the bearings. This instability is transferred by structure-borne noise into audible airborne sound and known as constant tone phenomenon. This phenomenon is not the major contributor of the engine noise but its tonal character is very unpleasant. In order to gain a more detailed understanding about the origin of this phenomenon, displacement sensors have been applied to the compressor- and the turbine-side of the rotor, to be able to determine the displacement path.

Whether as an optimization problem or a development tool, neural networks help engineers to work more efficiently. This paper’s central aspect is to add metadata to the core files of the project simulation data. To understand the project and its simulation models, a pre-processing methodology and convolutional neural network architecture are presented. With the added labels, it is possible to access the content of the model files of an engine performance simulation tool without examining them. At first, a pre-processing approach and its design are introduced to extract and filter the desired data from the XML data structure. Then, the data is split into sequences and paired with labels. Expert knowledge is used to label the models. These labels are further paired with the extracted sequences.

High-efficient simulations are mandatory to manage the ever-increasing complexity of automotive powertrain system and reduce development time and costs. Integrating AI methods into the development process provides an ideal solution thanks to massive increase in computational power. Based on an 1D physical engine model of a turbo-charged direct injection gasoline engine with variable valve timing (VVT), a high-performance hybrid simulation model has been developed for increasing computing performance. The newly developed model is made of a physics-based low-pressure part including intake and exhaust peripheries and a neural-network-based high-pressure part for combustion chamber calculations. For the training and validation of the combustion chamber neural networks, a data set with 10.5 million operating points was generated in a short time thanks to the parallelizable combustion chamber simulations in stand-alone mode.

This paper presents an autonomous cruise control for battery electric vehicles. The presented approach is based on the usage of predictive route data which is extracted out of a digital map and a wide range radar system in order to capture vehicles in front. By using the predictive route data and the information of the radar system, the autonomous cruise control can control the vehicle's speed over a wide range of driving situations without any driver interaction. The main aim of the presented autonomous cruise control is to optimize the battery electric vehicle's energy consumption. The main idea is to use predictive route data in order to calculate a consumption optimal vehicle speed trajectory by means of online optimization. The benefits of the autonomous cruise control are shown by means of real test drives and measured data evaluation.

In this work, a novel approach is introduced comprising a combination of unsupervised machine learning (ML) scheme and charging energy management of electric vehicles (EV). The main goal of this implementation is to reduce the load peak of charging EV’s, which are regular users of electric vehicle supply equipment (EVSE) of a certain building and, at the same time, to meet their electric and behavioral demands. The unsupervised ML considers certain features within the charging profiles in addition to the behavioral characteristics of the EV based on its intended use. Moreover, these features are extracted from large sets of history measurement data of the EVSE, which are stored in the data bank. The ML categorizes the EVs within certain clusters having defined specifications.

Predictive physical modeling is an established method used in the development process for automotive components and systems. While accurate predictions can be issued after tuning model parameters, long computation times are expected depending on the complexity of the model. As requirements for components and systems continuously increase, new optimization approaches are constantly being applied to solve multidimensional objectives and resulting conflicts optimally. Some of those approaches are deemed not feasible, as the computational times for required single predictions using conventional simulation models are too high. To address this issue it is proposed to use data-driven model such as neural networks. Previous efforts have failed due to sparse data sets and resulting poor predictive ability. This paper introduces an AI Toolchain used for data-driven modeling of combustion engine components. Two methods for generating scalable and fully variable datasets will be shown.

In order to decarbonize and lower the overall emissions of the transport sector, immediate and cost-effective powertrain solutions are needed. Natural gas offers the advantage of a direct reduction of carbon dioxide (CO2) emissions due to its better Carbon to Hydrogen ratio (C/H) compared to common fossil fuels, e.g. gasoline or diesel. Moreover, an optimized engine design suiting the advantages of natural gas in knock resistance and lean mixtures keeping in mind the challenges of power density, efficiency and cold start manoeuvres. In the public funded project MethMag (Methane lean combustion engine) a gasoline fired three-cylinder-engine is redesigned based on this change of requirements and benchmarked against the previous gasoline engine.

In this paper the development approach and the results of numerical and experimental investigations on homogeneous charge compression ignition in a free piston engine are presented. The Free Piston Linear Generator (FPLG) is a new type of internal combustion engine designed for the application in a hybrid electric vehicle. The highly integrated system consists of a two-stroke combustion unit, a linear generator, and a mass-variable gas spring. These three subsystems are arranged longitudinally in a double piston configuration. The system oscillates linearly between the combustion chamber and the gas spring, while electrical energy is extracted by the centrally arranged linear generator. The mass-variable gas spring is used as intermediate energy storage between the downstroke and upstroke. Due to this arrangement piston stroke and compression ratio are no longer determined by a mechanical system.

The proposed paper deals with the development process and initial measurement results of an opposed-piston combustion engine for application in a Free-Piston Linear Generator (FPLG). The FPLG, which is being developed at the German Aerospace Center (DLR), is an innovative internal combustion engine for a fuel based electrical power supply. With its arrangement, the pistons freely oscillate between the compression chamber of the combustion unit and a gas spring with no mechanical coupling like a crank shaft. Linear alternators convert the kinetic energy of the moving pistons into electric energy. The virtual development of the novel combustion system is divided into two stages: On the one hand, the combustion system including e.g. a cylinder liner, pistons, cooling and lubrication concepts has to be developed.

Operating gasoline engines at part load in a so-called Gasoline-HCCI (gHCCI) combustion mode has shown promising results in terms of improved efficiency and reduced emissions. So far, research has primarily been focused on experimental investigations on the test bench, whereas fast, predictive burn rate models for use in process calculation have not been available. Such a phenomenological model is henceforth presented. It describes the current burn rate as the sum of a sequential self-ignition process on the one hand and a laminar-turbulent flame propagation on the other hand. The first mechanism is essentially represented by ignition delay calculation, in which the reaction rate is computed separately for some hundred groups of different temperatures based on the Arrhenius equation. Thermal inhomogeneity is described by a contaminated normal distribution which accounts for the influence of wall temperature on mixture close to the cylinder wall.

Aeroacoustics is important in the automotive industry, as it significantly influences driving comfort. Particularly in the case of battery electric vehicles (BEVs), the flow noise is already crucial at lower driving speeds, since these generate barely any drive noise and the masking effects produced by the engine are eliminated. Due to the increasing importance of drag minimization and elimination of the exhaust system, the underbody of BEVs is typically very streamlined and exhibits a low acoustic interference potential. However, even small geometric modifications to the vehicle can lead to changes in the flow around the vehicle and consequently to significant noise sources. Thus, significant flow resonances in the low frequency range below 30 Hz have been detected on certain vehicle configurations. Initial investigations have shown that the flow around the front wheel spoilers is relevant for the development of the flow phenomenon.

In the competition for the powertrain of the future the internal combustion engine faces tough challenges. Reduced environmental impact, higher mileage, lower cost and new technologies are required in order to maintain its global position both in public and private mobility. For a long time, researchers have been investigating the so called Homogeneous Charge Compression Ignition (HCCI) that promises a higher efficiency due to a rapid combustion - i.e. closer to the ideal thermodynamic Otto cycle - and therefore more work and lower exhaust gas temperatures. Consequently, a rich mixture to cool down the turbocharger under high load may no longer be needed. As the combustion does not have a distinguished flame front it is able to burn very lean mixtures, with the potential of reducing HC and CO emissions. However, until recently, HCCI was considered to be reasonably applicable only at part load operating conditions.

The development process of electronic control units (ECU) is increasingly supported by different tools. The target-specific code-generation for single micro-controllers becomes a standard technology. Thus a continuous tool support during the whole development cycle is possible. This extends from the specification of the functionality to the implementation of the software on the controller. The next generation of tool support is not only focused on single micro-controllers it also supports the design of systems consisting of different controllers connected via various communication entities. Thus the goal of the tool support is the automatic code-generation for such distributed embedded real-time systems including support of different communication buses (e.g., Controller Area Network CAN, Time Triggered Protocol TTP) and different processor targets.