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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.

Two substantially different methods have become popular in building fast computing catalyst models: physico-chemical approaches focusing on dimensionality reduction and machine learning approaches. Data driven models are known to be very fast computing and to achieve high accuracy but they can lack of extrapolation capability. Physico-chemical models are usually slower and less accurate but superior regarding robustness. The robustness can even be reinforced by implementing an extended Kalman filter, which enables the model to adapt its states based on actual sensor values, even if the sensors are drifting. The present study proposes a combination of both approaches into one hybrid model, keeping the robustness of the physico-chemical model in edge cases while also achieving the accuracy of the data based model in well-known regimes. The output of the hybrid model is controlled by an autoencoder, utilizing methods well known from the field of anomaly detection.

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.

CO2 emission constraints taking effect from 2020 lead to further investigations of technologies to lower knock sensitivity of gasoline engines, main limiting factor to increase engine efficiency and thus reduce fuel consumption. Moreover the RDE cycle demands for higher power operation, where fuel enrichment is needed for component protection. To achieve high efficiency, the engine should be run at stoichiometric conditions in order to have better emission control and reduce fuel consumption. Among others, water injection is a promising technology to improve engine combustion efficiency, by mainly reducing knock sensitivity and to keep high conversion rates of the TWC over the whole engine map. The comprehension of multiple thermodynamic effects of water injection through 3D-CFD simulations and their exploitation to enhance the engine combustion efficiency is the main purpose of the analysis.

Increasing the efficiency of modern gasoline engines (with direct injection and spark-ignition - DISI) requires innovative approaches. The reduction of the engine displacement, accompanied by an increase of the mean pressure, is limited by the tendency of increasing combustion anomalies. Conventional methods for knock mitigation, on the contrary, have a negative effect on consumption and efficiency. A promising technology to solve these conflicting objectives is the injection of water. Both the indirect and the direct water injection achieve a significant reduction in the load temperature. The fuel enrichment can be reduced, whereby the operating range of the exhaust aftertreatment can be extended. In addition, water injection paves the way for an increase in the geometric compression ratio, which leads to an efficiency advantage even at partial load.

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.

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.

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.

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.

The knowledge of thermal behavior of combustion engines is extremely important e.g. to predict engine warm up or to calculate engine friction and finally to optimize fuel consumption. Typically, thermal engine behavior is modeled using look-up tables or semi-physical models to calculate the temperatures of structure, coolant and oil. Using look-up tables can result in inaccurate results due to interpolation and extrapolation; semi-physical modeling leads to high computation time. This work introduces a new kind of model to calculate thermal behavior of combustion engines using an artificial neural network (ANN) which is highly accurate and extremely fast. The neural network is a multi-layered feed-forward network; it is trained by data generated with a validated semi-physical model. Output data of the ANN-based model are calculated with nonlinear transformation of input data and weighting of these transformations.

Since the mechanisms leading to cyclic combustion variabilities in direct injection gasoline engines are still poorly understood, advanced computational studies are necessary to be able to predict, analyze and optimize the complete engine process from aerodynamics to mixing, ignition, combustion and heat transfer. In this work the Scale-Adaptive Simulation (SAS) turbulence model is used in combination with a parameterized lagrangian spray model for the purpose of predicting transient in-cylinder cold flow, injection and mixture formation in a gasoline engine. An existing CFD model based on FLUENT v15.0 [1] has been extended with a spray description using the FLUENT Discrete Phase Model (DPM). This article will first discuss the validation of the in-cylinder cold flow model using experimental data measured within an optically accessible engine by High Speed Particle Image Velocimetry (HS-PIV).

Two-cylinder engines not only have special demands concerning uniformity and dynamics of oscillating masses and firing order, but also place very different demands on the turbocharger. With two-cylinder engines, the pulsating influence grows and changes the operation of the turbine. In this paper different boosting technologies are compared in small engine applications. Besides turbochargers the potentials and limits of superchargers and electric chargers are compared as well as their combinations. These technologies show differences concerning power supply, operation range and efficiency, and these effects have different implications in small engines. The efficiency of a turbo compressor, for example decreases, rapidly for small dimensions. Results from experiments and engine process simulations are shown based on a two-cylinder engine of 0.8l displacement. The operating condition of a turbocharger turbine in a two-cylinder engine is very specific due to exhaust pulsations.

Standardized driving cycles, such as the New European Driving Cycle (NEDC) in Europe or the Federal Test Procedure 75 (FTP-75) in the U.S. are an important tool to certify new vehicle models. They are used to estimate real world fuel consumption as well as real world emissions. The latter has recently become more important with the stronger focus on green driving, resulting in much stricter emission regulations, while fuel consumption still remains one of the most important aspects in terms of economy and long term costs for the vehicle owner. However these cycles do not reflect the actual behaviour of the driver or regional influences (i.e. topography). Therefore, manufacturers have developed their own usage and test cycles and are able to extract data from the vehicle to analyse the individual driving behaviour and vehicle usage. Apart from that, Naturalistic Driving Observation (NDO) is interested in understanding the driver.