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The thorough understanding of the effects due to the fuel direct injection process in modern gasoline direct injection engines has become a mandatory task to meet the most demanding regulations in terms of pollutant emissions. Within this context, computational fluid dynamics proves to be a powerful tool to investigate how the in-cylinder spray evolution influences the mixture distribution, the soot formation and the wall impingement. In this work, the authors proposed a comprehensive methodology to simulate the air-fuel mixture formation into a gasoline direct injection engine under multiple operating conditions. At first, a suitable set of spray sub-models, implemented into an open-source code, was tested on the Engine Combustion Network Spray G injector operating into a static vessel chamber. Such configuration was chosen as it represents a typical gasoline multi-hole injector, extensively used in modern gasoline direct injection engines.

The underlying basic model represents a powertrain with automatic transmission including a torque converter. It is based on a greybox-modeling approach, which refers to ordinary differential equations with identified parameters and characteristic curves. The validated basic model is extended in order to reproduce the system behavior and especially the side shaft torque during a gear shift process. Therefore the model is extended by a transmission model with clutches for gear shifting in order to simulate specific powertrain dynamics additionally. The parameters have already been determined for the basic model using a method for isolated and structured parameter identification based on measurement data of an automotive powertrain test bench. A comparable structured parameter identification method is applied to obtain the parameters of the extended model.

A unique concept for a multi-body test rig enabling the simulation of longitudinal, steering and vertical dynamics was developed at the Institute for Mechatronic Systems (IMS) at TU Darmstadt. A prototype of this IMS test rig is currently being built. In conjunction with the IMS test rig, the Vehicle Terrain Performance Laboratory (VTPL) at Virginia Tech further developed a full car, seven degree of freedom (7 DOF) simulation model capable of accurately reproducing measured displacement, pitch, and roll of the vehicle body due to terrain excitation. The results of the 7 DOF car model were used as the reference input to the multi-body IMS test rig model. The goal of the IMS/VTPL joint effort was to determine whether or not a controller for the IMS test rig vertical actuator could accurately reproduce wheel displacements due to different measured terrain constraints.

In this investigation two different nonlinear dynamic black box modelling approaches are compared. The purpose of the models is to reproduce the transient gearshift process. The models are used to compute the torque at the sideshafts, which is highly correlated to the gearshift comfort. The first model is a Gaussian process (GP) model. The GP is a probabilistic, non-parametric approach, which is additionally capable to compute the confidence interval of the simulated output signal. The second black box model uses the artificial neural net (ANN) approach. In addition to training algorithms the resulting model configurations for both black box approaches are shown in this investigation. Furthermore the empirical error of both modeling approaches is compared to the predictive variance of the GP model and to the intrinsic uncertainty of the gearshift process.

Developing an energy-efficient powertrain system is a solution for environment-friendly vehicles. Furthermore, it also enhances the performance of vehicles. In powertrain system, transmission plays an important role in terms of vehicle dynamic performance and energy consumption. Therefore, a lot of researches have been conducted on modelling power losses inside the transmission. Basically, the power losses in transmission consist of bearing losses, drag torque losses on gear blank that is immersed in the oil and gear mesh losses due to the sliding frictional force on gear flank. According to some experiments in the latest literatures, power losses of synchronizers cannot be neglected, when its shift sleeve is in neutral position. Principally, power losses of synchronizers in neutral position mainly come from load independent drag torque.

In this investigation an innovative signal generator will be introduced, which enables the generation of transient control signals for the gearshift process. The signals are generated merely depending on scalar transmission control unit (TCU) calibration parameters. The signal generator replaces the comprehensive TCU software within the simulation environment. Thus no extensive residual bus simulation is required. Multiple experimental models represent the core part of the signal generator. To predict the system behavior of the underlying system, the models are trained using measured data from a powertrain with automatic transmission mounted on a test rig. The results demonstrate that the introduced signal generator is suitable to predict transient control signals for the gearshift operation accurately. In combination with an additional powertrain model it is possible to simulate the gearshift process and subsequently to evaluate the gearshift comfort.

The scope of this work is to propose a methodology to define multicomponent surrogate mixtures which describe the main evaporation characteristics of real gasoline fuels. Since real fuels are commonly complex mixtures with hundreds or thousands of hydrocarbons, their exact composition is generally not known. Only global characteristics are standardized. An accurate modeling of such complex mixtures in 3D-CFD requires the definition of a suitable surrogate. So far, surrogate mixtures have mostly been defined based on their combustion properties, such as ignition delay or burning velocity, irrespective of their evaporation characteristics. For this reason, in this work, a systematic study is carried out to develop a methodology to define mixtures of representative components that mimic the evaporation behavior of real fuels.

Magnetic Resonance Velocimetry (MRV) measurements are performed in 1:1 scale models of a single-cylinder optical engine to investigate the differences in the inlet flow due to geometrical changes of the cylinder head. The models are steady flow water-analogue of the optical IC engine with a fixed valve lift of 9.21 mm to simulate the induction flow at 270° bTDC. The applicability of MRV to engine flows despite the differences in experimental operating parameters between the steady flow model and the optical IC engine are demonstrated and well addressed in this manuscript and in a previous work [1]. To provide trust into the MRV measurements, the data is validated with phase-averaged particle image velocimetry (PIV) measurements performed within the optical engine. The main geometrical changes between the cylinder heads include a variation of intake valve diameter and slight modifications to the exit of the intake port.

Besides optimal engine systems, high-efficiency vehicle transmissions are generally also required to improve fuel economy in automotive applications. For the energy loss analysis in transmissions, most research focused on the major mechanical components, such as gears, bearings and seals, while the other mechanical losses, like synchronizer losses, were usually not considered. With increasing number of synchronizers in modern transmissions, a recent study indicates that the power loss analysis of synchronizers should also be developed and appended for a more accurate investigation on overall power losses in transmissions. The function of synchronizer is to equalize the different rotational speeds of shafts and gear wheels by frictional torques, for which the synchronizer must be cooled and lubricated in order to enhance the service life. With the supplement of lubricants, fluid friction is generated due to the differential speed, when the synchronizer is in neutral position.