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In order to improve the efficiency of passenger cars, developments focus on decreasing their aerodynamic drag, part of which is caused by cooling air. Thus, car manufacturers try to seal the cooling air path to prevent leakage flows. Nevertheless, gaps between the single components of the cooling air path widen due to the deformation of components under aerodynamic load. For simulating the cooling airflow utilization ratio (CAUR), computational fluid dynamics (CFD) simulations are used, which neglect component deformation. In this paper, a computational method aiming at sufficient gap resolution and determining the CAUR of passenger cars under the consideration of component deformation is developed. Therefore, a partitioned approach of fluid structure interaction (FSI) simulations is used. The fluid field is simulated in OpenFOAM, whereas the structural simulations are conducted using Pam-Crash.

Dust testing of vehicles on unpaved roads is crucial in the development process for automotive manufacturers. These tests aim to ensure the functionality of locking systems in dusty conditions, minimize dust concentration inside the vehicle, and enhance customer comfort by preventing dust accumulation on the car body. Additionally, deposition on safety-critical parts, such as windshields and sensors, can pose threats to driver vision and autonomous driving capabilities. Currently, dust tests are primarily conducted experimentally at proving grounds. In order to gain early insights and reduce the need for costly physical tests, numerical simulations are becoming a promising alternative. Although simulations of vehicle contamination by dry dust have been studied in the past, they have often lacked detailed models for tire dust resuspension. In addition, few publications address the specifics of dust deposition on vehicles, especially in areas such as door gaps and locks.

The improvement of vehicle soiling behavior has increasing interest over the past few years not only to satisfy customer requirements and ensure a good visibility of the surrounding traffic but also for autonomous vehicles, for which soiling investigation and improvement are even more important due to the demands of the cleanliness and induced functionality of the corresponding sensors. The main task is the improvement of the soiling behavior, i.e., reduction or even prevention of soiling of specific surfaces, for example, windows, mirrors, and sensors. This is mostly done in late stages of vehicle development and performed by experiments, e.g., wind tunnel tests, which are supplemented by simulation at an early development stage. Among other sources, the foreign soiling on the side mirror and the side window depend on the droplet detaching from the side mirror housing.

Meeting strict current and future emissions legislation necessitates development of computational tools capable of predicting the behaviour of combustion and emissions with an accuracy sufficient to make correct design decisions while keeping computational cost of the simulations amenable for large-scale design space exploration. While detailed kinetics modelling is increasingly seen as a necessity for accurate simulations, the computational cost can be often prohibitive, prompting interest in simplified approaches allowing fast simulation of reduced mechanisms at coarse grid resolutions appropriate for internal combustion engine simulations in design context. In this study we present a simplified Well-stirred Reactor (WSR) implementation coupled with 3D CFD Ricardo VECTIS solver.

Raising demands towards lightweight design paired with a loss of originally predominant engine noise pose significant challenges for NVH engineers in the automotive industry. From an aeroacoustic point of view, low frequency buffeting ranks among the most frequently encountered issues. The phenomenon typically arises due to structural transmission of aerodynamic wall pressure fluctuations and/or, as indicated in this work, through rear vent excitation. A possible workflow to simulate structure-excited buffeting contains a strongly coupled vibro-acoustic model for structure and interior cavity excited by a spatial pressure distribution obtained from a CFD simulation. In the case of rear vent buffeting no validated workflow has been published yet. While approaches have been made to simulate the problem for a real-car geometry such attempts suffer from tremendous computation costs, meshing effort and lack of flexibility.

The Noise Vibration and Harshness (NVH) characteristics and requirements of vehicles are changing as the automotive manufacturers turn their focus from developing and producing cars propelled by internal combustion engines (ICE) to electrified vehicles. This new strategic orientation enables them to offer products that are more efficient and environmentally friendly. Although electric powertrains have many advantages compared to their established predecessors they also bring new challenges that increase the difficulty of matching the high quality requirements of premium car producers especially regarding NVH. Electric motors are one of the most important sources of vibrations in electric vehicles.

Driving cycles are valuable tools for emissions calibration at engine and powertrain test beds. While generic velocity profiles were sufficient in the past, legislative changes and increasing complexity of powertrain and exhaust aftertreatment systems require a new approach: Realistically transient cycles - which include critical driving maneuvers and can be tailored to a specific powertrain configuration - are needed to optimize the emission behavior of the said powertrain. For the generation of realistic velocity profiles, the Markov chain approach has been widely used and described in literature. However, this approach, so far, has only been used to generate cycles that are statistically representative of a large database of real driving trips, which is typically not available during the early stages of development of a new powertrain.

The reduction of both harmful emissions (CO, HC, NOx, etc.) and gases responsible for greenhouse effects (especially CO2) are mandatory aspects to be considered in the development process of any kind of propulsion concept. Focusing on ICEs, the main development topics are today not only the reduction of harmful emissions, increase of thermodynamic efficiency, etc. but also the decarbonization of fuels which offers the highest potential for the reduction of CO2 emissions. Accordingly, the development of future ICEs will be closely linked to the development of CO2 neutral fuels (e.g. biofuels and e-fuels) as they will be part of a common development process. This implies an increase in development complexity, which needs the support of engine simulations. In this work, the virtual modeling of real fuel behavior is addressed to improve current simulation capabilities in studying how a specific composition can affect the engine performance.

The current trend in automobiles is towards electrical vehicles, but for the most part these vehicles still require an internal combustion engine to provide additional range and flexibility. These engines are under stringent emissions regulations, in particular, for the reduction of CO2. Gas engines which run lean burn combustion systems provide a viable route to these emission reductions, however designing these engines to provide sustainable and controlled combustion under lean conditions at λ=2.0 is challenging. To address this challenge, it is possible to use a scavenged Pre-Chamber Ignition (PCI) system which can deliver favorable conditions for ignition close to the spark plug. The lean charge in the main combustion chamber is then ignited by flame jets emanating from the pre-chamber nozzles. Accurate prediction of flame kernel development and propagation is essential for the analysis of PCI systems.

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.

Over the past years, the question as to what may be the powertrain of the future has become ever more apparent. Aiming to improve upon a given technology, the internal combustion engine still offers a number of development paths in order to maintain its position in public and private mobility. In this study, an innovative combustion process is investigated with the goal to further approximate the ideal Otto cycle. Thus far, similar approaches such as Homogeneous Charge Compression Ignition (HCCI) shared the same objective yet were unable to be operated under high load conditions. Highly increased control efforts and excessive mechanical stress on the components are but a few examples of the drawbacks associated with HCCI. The approach employed in this work is the so-called Spark Assisted Compression Ignition (SACI) in combination with a pre-chamber spark plug, enabling short combustion durations even at high dilution levels.

Model Order Reduction (MOR) methods such as Component Mode Synthesis (CMS) have been used in order to simulate large linear dynamic systems for many years and have reached a considerable level of saturation. These reduction methods have many advantages such as minimizing computational costs but also have restrictions. One of their disadvantages is that material damping characteristics can only be defined in form of Rayleigh damping. Another disadvantage is that the reduced order model can only represent one state of the structure determined in the generation process of the reduced matrices. In this paper we present a way to consider material damping in reduced matrices that contain one or more materials having different damping characteristics without the disadvantages of using Rayleigh damping.

Electromechanical steering systems (EPS) provide assisting steering force through an electric motor, often paired with a screw drive. The combination of an electric motor and a screw drive lead to high inertia and thus to a reduced feedback of tire force behavior at the steering wheel. This force behavior contains information about driving conditions and road surface. However, the electric motor can be used to actively enhance and manipulate steering feedback. This article describes the driver perception of modified steering feedback. The presented data is collected carrying out a driving simulator study with average drivers as test subjects. In this study the driver experiences a modified steering feedback at a change of road friction coefficient. Based on the test subjects ratings the perception, acceptance and controllability of the presented steering feedback modifications are assessed.

Two optical techniques were developed and combined with a CFD simulation to obtain spatio-temporally resolved information on air/fuel mixing in the cylinder of a methane-fueled, fired, optically accessible engine. Laser-induced fluorescence (LIF) of anisole (methoxybenzene), vaporized in trace amounts into the gaseous fuel upstream of the injector, was captured by a two-camera system, providing one instantaneous image of the air/fuel ratio per cycle. Broadband infrared (IR) absorption by the methane fuel itself was measured in a small probe volume via a spark-plug integrated sensor, yielding time-resolved quasi-point information at kHz-rates. The simulation was based on the Reynolds-averaged Navier-Stokes (RANS) approach with the two-equation k-epsilon turbulence model in a finite volume discretization scheme and included the port-fuel injection event. Commercial CFD software was used to perform engine simulations close to the experimental conditions.

This paper summarizes the development of a wireless message from infrastructure-to-vehicle (I2V) for safety applications based on Dedicated Short-Range Communications (DSRC) under a cooperative agreement between the Crash Avoidance Metrics Partners LLC (CAMP) and the Federal Highway Administration (FHWA). During the development of the Curve Speed Warning (CSW) and Reduced Speed Zone Warning with Lane Closure (RSZW/LC) safety applications [1], the Basic Information Message (BIM) was developed to wirelessly transmit infrastructure-centric information. The Traveler Information Message (TIM) structure, as described in the SAE J2735, provides a mechanism for the infrastructure to issue and display in-vehicle signage of various types of advisory and road sign information. This approach, though effective in communicating traffic advisories, is limited by the type of information that can be broadcast from infrastructures.

This paper summarizes the validation of prototype vehicle-to-infrastructure (V2I) safety applications based on Dedicated Short Range Communications (DSRC) in the United States under a cooperative agreement between the Crash Avoidance Metrics Partners LLC (CAMP) and the Federal Highway Administration (FHWA). After consideration of a number of V2I safety applications, Red Light Violation Warning (RLVW), Curve Speed Warning (CSW) and Reduced Speed Zone Warning with Lane Closure Warning (RSZW/LC) were developed, validated and demonstrated using seven different vehicles (six passenger vehicles and one Class 8 truck) leveraging DSRC-based messages from a Road Side Unit (RSU). The developed V2I safety applications were validated for more than 20 distinct scenarios and over 100 test runs using both light- and heavy-duty vehicles over a period of seven months. Subsequently, additional on-road testing of CSW on public roads and RSZW/LC in live work zones were conducted in Southeast Michigan.

Battery electric vehicles (BEVs) are equipped with Mobile Air Conditioning systems (MACs) to ensure a comfortable cabin temperature in all climates and ambient conditions as well as the optional conditioning of the traction battery. An assessment of the global electrical energy consumption of various MACs has been derived, where the basis of the assessment procedure is the climate data GREEN-MAC-LCCP 2007 (Global Refrigerants Energy & Environmental - Mobile Air Condition - Life Cycle Climate Performance) and the improved LCCP2013 (Life Cycle Climate Performance. The percentage driving time during 6 AM and 24 PM is divided into six different temperature bins with the solar radiation and relative humidity for 211 cities distributed over Europe, North, Central, and South America, Asia, South West Pacific, and Africa. The energy consumption of the MACs is determined by a thermal vehicle simulation. In this work, four different MACs are simulated and compared.

The Deep Orange program immerses automotive engineering students into the world of an OEM as part of their 2-year graduate education. In support of developing the program’s seventh vehicle concept, the students studied the sponsoring brand essence, conducted market research, and made a heuristic assessment of competitor vehicles. The upfront research lead to the definition of target customers and setting vehicle level targets that were broken down into requirements to develop various vehicle sub-systems. The powertrain team was challenged to develop a scalable propulsion concept enabled by a common vehicle architecture that allowed future customers to select (at the point of purchase) among various levels of electrification best suiting their needs and personal desires. Four different configurations were identified and developed: all-electric, two plug-in hybrid electric configurations, and an internal combustion engine only.

Water vapor is, aside from carbon dioxide, the major fossil fuel combustion by-product. Depending on its concentration in the exhaust gas mixture as well as on the exhaust gas pressure, its condensation temperature can be derived. For typical gasoline engine stoichiometric operating conditions, the water vapor dew point lies at about 53 °C. The exhaust gas mixture does however contain some pollutants coming from the fuel, engine oil, and charge air, which can react with the water vapor and affect the condensation process. For instance, sulfur trioxide present in the exhaust, reacts with water vapor forming sulfuric acid. This acid builds a binary system with water vapor, which presents a dew point often above 100 °C. Exhaust composition after leaving the combustion chamber strongly depends on fuel type, engine concept and operation point. Furthermore, the exhaust undergoes several chemical after treatments.

Highly Automated Driving (HAD) opens up new middle-term perspectives in mobility and is currently one of the main goals in the development of future vehicles. The focus is the implementation of automated driving functions for structured environments, such as on the motorway. To achieve this goal, vehicles are equipped with additional technology. This technology should not only be used for a limited number of use cases. It should also be used to improve Active Safety Systems during normal non-automated driving. In the first approach we investigate the usage of machine learning for an autonomous emergency braking system (AEB) for the active pedestrian protection safety. The idea is to use knowledge of accidents directly for the function design. Future vehicles could be able to record detailed information about an accident. If enough data from critical situations recorded by vehicles is available, it is conceivable to use it to learn the function design.