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Using a holistic 1D engine simulation approach for the modelling of full-transient engine operation, allows analyzing future engine concepts, including its exhaust gas aftertreatment technology, early in the development process. Thus, this approach enables the investigation of both important fields - the thermodynamic engine process and the aftertreatment system, together with their interaction in a single simulation environment. Regarding the aftertreatment system, the kinetic reaction behavior of state-of-the-art and advanced components, such as Diesel Oxidation Catalysts (DOC) or Selective Catalytic Reduction Soot Filters (SCRF), is being modelled. Furthermore, the authors present the use of the 1D engine and exhaust gas aftertreatment model on use cases of variable valve train (VVT) applications on passenger car (PC) diesel engines.

Gasoline Controlled Auto Ignition offers a high CO2 emission reduction potential, which is comparable to state-of-the-art, lean stratified operated gasoline engines. Contrary to the latter, GCAI low temperature combustion avoids NOX emissions, thereby trying to avoid extensive exhaust aftertreatment. The challenges remain in a restricted operation range due to combustion instabilities and a high sensitivity towards changing boundary conditions like ambient temperature, intake pressure or fuel properties. Once combustion shows instability, cyclic fluctuations are observed. These appear to have near-chaotic behavior but are characterized by a superposition of clearly deterministic and stochastic effects. Previous works show that the fluctuations can be predicted precisely when taking cycle-tocycle correlations into account. This work extends current approaches by focusing on additional dependencies within one single combustion cycle.

In this paper, boosting strategies are investigated for part load operation of typical fuel-cell-systems. The optimal strategy can mainly be obtained by simulation. The boosting strategy is one of the most essential parameters for design and operation of a fuel-cell-system. High pressure ratios enable high power densities, low size and weight. Simultaneously, the demands in humidification and water recovery for today's systems are reduced. But power consumption and design effort of the system increases strongly with the pressure level. Therefore, the main focus must be on the system efficiencies at part load. In addition, certain boundary conditions like the inlet temperature of the fuel-cell stack must be maintained. With high pressure levels the humidification of the intake air before, within or after the compressor is not sufficient to dissipate enough heat. Vaporization during the compression process shows efficiency advantages while the needs in heat dissipation decreases.

In the field of gasoline powertrain calibration, the challenges are growing due to ever shorter time-to-market requirements and a simultaneous increase in powertrain complexity. In addition, the great variety of vehicle variants requires an increasing number of prototypes for calibration and validation tasks within the framework of the current Real Driving Emissions (RDE) regulations and the expected Post Euro 6 emission standards. Hardware-in-the-Loop (HiL) simulations have been introduced successfully to support the calibration tasks in parallel to the conventional vehicle development activities. The HiL approach enables a more reliable compliance with emission limits and improves the quality of calibrations, while reducing the number of prototype vehicles, test resources and thus overall development costs.

The modern engine design process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. The introduction of predictive real-time simulation tools that represent the entire powertrain can likely contribute to improving the efficiency of the calibration process. Engine models, which are purely based on physical first principles, are usually not capable of real-time applications, especially if the simulation is focused on cold start and warm-up behavior. However, the initial data definition for the ECU using a Hardware-in-the-Loop (HiL)-Simulator requires a model with both real-time capability and sufficient accuracy. The use of artificial intelligence systems becomes necessary, e.g. neural networks. Methods, structures and the realization of a hybrid real-time model are presented in this paper, which combines physical and neural network models.

A main focus of development in modern SI engine technology is variable valve timing, which implies a high potential of improvement regarding fuel consumption and emissions. Variable opening, period and lift of inlet and outlet valves enable numerous possibilities to alter gas exchange and combustion. However, this additional variability generates special demands on the calibration process of specific engine control devices, particularly under cold start and warm-up conditions. This paper presents procedures, based on Hardware-in-the-Loop (HiL) simulation, to support the classical calibration task efficiently. An existing approach is extended, such that a virtual combustion engine is available including additional valve timing variability. Engine models based purely on physical first principles are often not capable of real time execution. However, the definition of initial parameters for the ECU requires a model with both real time capability and sufficient accuracy.

The modern engine development process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. It is expected that predictive simulation tools that encompass the entire powertrain can potentially improve the efficiency of the calibration process. The testing of an ECU using a HiL system requires a real-time model. Additionally, if the initial parameters of the ECU are to be defined and tested, the model has to be more accurate than is typical for ECU functional testing. It is possible to enhance the generalization capability of the simulation, with neuronal network sub-models embedded into the architecture of a physical model, while still maintaining real-time execution. This paper emphasizes the experimental investigation and physical modeling of the port fuel injected SI engine.

The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation.

The growing number of passenger car variants and derivatives in all global markets, their high degree of software differentiability caused by regionally different legislative regulations, as well as pronounced market-specific customer expectations require a continuous optimization of the entire vehicle development process. In addition, ever stricter emission standards lead to a considerable increase in powertrain hardware and control complexity. Also, efforts to achieve market and brand specific multistep adjustable drivability characteristics as unique selling proposition, rapidly extend the scope for calibration and testing tasks during the development of powertrain control units. The resulting extent of interdependencies between the drivability calibration and other development and calibration tasks requires frontloading of development tasks.

Controlled Auto Ignition combustion systems have a high potential for fuel consumption and emissions reduction for gasoline engines in part load operation. Controlled auto ignition is initiated by reaching thermal ignition conditions at the end of compression. Combustion of the CAI process is controlled essentially by chemical kinetics, and thus differs significantly from conventional premixed combustion. Consequently, the CAI combustion process is determined by the thermodynamic state, and can be controlled by a high amount of residual gas and stratification of air, residual gas and fuel. In this paper both fundamental and application relevant aspects are investigated in a combined approach. Fundamental knowledge about the auto-ignition process and its dependency on engine operating conditions are required to efficiently develop an application strategy for CAI combustion.

Knock control is one of the most vital functions for safe and fuel-efficient operation of gasoline engines. However, all knock control strategies rely on accurate knock detection to operate the engine close to the optimal set point. Knock detection is usually calibrated on the engine test bench, requiring the engine to run with knocking combustion in a time-consuming multi-stage campaign. Model-based calibration significantly reduces calibration loops on the test bench. However, this method requires a large effort in building and validating the model, which is often limited by the lack of function documentation, available measurements or hardware representation. As the software models are often not available, function structures vary between manufacturers and sub model functions are often documented as black boxes. Hence, using the model-based approach is not always possible.

This article discusses highly flexible and accurate physics-based mean value modeling (MVM) for internal combustion engines and its wide applicability towards virtual vehicle calibration. The requirement to fulfill the challenging Real Driving Emissions (RDE) standards has significantly increased the demand for precise engine models, especially models regarding pollutant emissions and fuel economy. This has led to a large increase in effort required for precise engine modeling and robust model calibration. Two best-practice engine modeling approaches will be introduced here to satisfy these requirements. These are the exclusive MVM approach, and a combination of MVM and a Design of Experiments (DOE) model for heterogeneous multi-domain engine systems.

Controlled Auto Ignition (CAI) as a promising future combustion process is a concept to strongly reduce fuel consumption as well as NOx emissions. The acceptance and the potential of this combustion process depends on the possible CAI operation range in the engine map and the fuel consumption benefit, as well as the complexity of the variable valve train which is necessary to realize the CAI combustion process. The thermodynamic investigations presented in this paper were done on an engine equipped with an electromechanical valve train (EMVT), featuring Port Fuel Injection (PFI) and direct Injection. They show that the electromechanical valve train is an excellent platform for developing the CAI process. Controlled Auto Ignition has been realized with port fuel injection in a speed range between 1000 and 4500 rpm and in a load range between approximately 1 and 6 bar BMEP (about 5 bar BMEP for pressure gradients lower than 3 bar/°CA) depending on engine speed.