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The main objective of the FVV-project “Cylinder Module” was the development of a profoundly modular designed concept for object-oriented modeling of in-cylinder processes of internal combustion engines. It was designed in such a way, that it can either be used as a stand-alone real working-process calculation tool or in tools for whole vehicle simulations. It is possible to run the “Cylinder Module”-code inside the FVV-“GPA”-software for transient vehicle and driving cycle simulations and it is possible to use the graphical user interface “ATMOS” of the “GPA”-project. The code can also be used as a user-subroutine in 1-D-flow simulation codes. Much effort was spent on the requirements of flexibility and expandability in order to be well prepared to cope with the diversity of both today's and future tasks. The code is freely available for members of the German Research Association for Combustion Engines (FVV).

This paper presents an integrated numerical and experimental approach to take best possible advantage of the common development tools at hand (1D, CFD and wind tunnel) to determine the cooling air mass flow at the different vehicle development stages. 1D tools can be used early in development when neither 3D data nor wind tunnel models with detailed underhood flow are available. A problem that has to be resolved is the dependency on input data. In particular, the pressure coefficients on the outer surface (i.e. at the air inlet and outlet region) and the pressure loss data of single components are of great importance since the amount of cooling air flow is directly linked to these variables. The pressure coefficients at the air inlet and outlet are not only a function of vehicle configuration but also of driving velocity and fan operation. Both, static and total pressure coefficient, yield different advantages and disadvantages and can therefore both be used as boundary conditions.

The adequate dimensioning of drive train components such as gearbox, clutch and driveshaft presents a major technical task. The one of manual transmissions represents a special significance due to the customer’s ability of inducing high force, torque and thermic energy into the powertrain through direct mechanical interconnection of gearstick, clutch pedal and gearbox. Out of this, the question about how to capture behavior and strain of the components during real operation, as well as their objective evaluation evolves. Furthermore, the gained insights must be considered for designing and development. As a basis for the examination, measuring data from imposing driving tests are adduced. Therefore, a trial study has been conducted, using a representative circular course in the metropolitan area of Stuttgart, showing the average German car traffic. The more than 40 chosen drivers constitute the average driver in Germany with respect to age, gender and annual mileage.

In addition to the analysis of human driving behavior or the development of new advanced driver assistance systems, the high simulation quality of today’s driving simulators enables investigations of selected topics pertaining to driving dynamics. With high reproducibility and fast generation of vehicle variants the subjective evaluation process leads to a better system understanding in the early development stages. The transfer of the original on-road test run to the virtual reality of the driving simulator includes the full flexibility of the vehicle model, the maneuver and the test track, which allows new possibilities of investigation. With the opportunity of a realistic whole-vehicle simulation provided by the Stuttgart Driving Simulator new analysis of the human’s thresholds of perception are carried out.

The hybrid power train technology offers various prospects to optimize the engine efficiency in order to minimize the CO₂ emissions of an internal-combustion-engine-powered vehicle. Today different types of hybrid architectures like parallel, serial, power split or through-the-road concepts are commonly known. To achieve lowest fuel consumption the following hybrid electric vehicle drive modes can be used: Start/Stop, pure electric/thermal driving, recuperation of brake energy and the hybrid mode. The high complexity of the interaction between those power sources requires an extensive investigation to determine the optimal configuration of a natural-gas-powered SI engine within a parallel hybrid power train. Therefore, a turbocharged 1.0-liter 3-cylinder CNG engine was analyzed on the test bench. Using an optimized combustion strategy, the engine was operated at stoichiometric and lean air/fuel ratio applying both high- and low-pressure EGR.

Turbocharged SI-DI-engines in combination with a reduction of engine displacement (“Downsizing”) offer the possibility to remarkably reduce the overall fuel consumption. In charged mode it is possible to scavenge fresh unburnt air into the exhaust system if a positive slope during the overlap phase of the gas exchange occurs. The matching of the turbo system in SI-engines always causes a trade-off between low-end torque and high power output. The higher mass flow at low engine speeds of an engine using scavenging allows a partial solution of this trade-off. Thus, higher downsizing grades and fuel consumption reduction potential can be obtained. Through scavenging the global fuel to air ratio deviates from the local in-cylinder fuel to air ratio. It is possible to use a rich in-cylinder fuel to air ratio, whereas the global fuel to air ratio remains stochiometrical. This could be very beneficial to reduce the effect of catalytic aging on the one hand and engine knock on the other hand.

In this paper, a serial/parallel hybrid electric vehicle with a 17 kWh battery and 400 V voltage level is simulated. The vehicle is a C-segment vehicle, which has optimized driving resistances. It also has an external recharge possibility, which enables fully electric driving. The vehicle uses an Otto-engine concept as well as two electric motors. One motor is a permanent magnet synchronous motor and can be used as traction motor or generator, the other one is an induction motor used as main traction motor for the vehicle. The vehicle uses a 2-speed gearbox, where the electric motors are mounted in P2-configuration. To reach optimal results for the fuel consumption, an operating strategy based on the Equivalent Consumption Minimization Strategy (ECMS) is introduced and implemented in the vehicle simulation.

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.

Driving simulators allow the testing of driving functions, vehicle models and acceptance assessment at an early stage. For a real driving experience, it's necessary that all immersions are depicted as realistically as possible. When driving manually, the perceived haptic steering wheel torque plays a key role in conveying a realistic steering feel. To ensure this, complex multi-body systems are used with numerous of parameters that are difficult to identify. Therefore, this study shows a method how to generate a realistic steering feel with a nonlinear open-loop model which only contains significant parameters, particularly the friction of the steering gear. This is suitable for the steering feel in the most driving on-center area. Measurements from test benches and real test drives with an Electric Power Steering (EPS) were used for the Identification and Validation of the model.