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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.

During a thermal regeneration of a Diesel particulate filter (DPF) the temperature inside the DPF may raise above critical thresholds in an uncontrolled way (thermal shock). Especially driving conditions with a comparable low exhaust gas mass flow and high oxygen content like idle speed may create a thermal shock. This paper presents a concept for an ECU software structure to prevent the DPF from reaching improper temperatures and the methodology in order to calibrate this ECU structure. The concept deals in general with a closed-loop control of the exhaust gas air-fuel-ratio during the critical engine operation phases. Those critical operation phases are identified at the engine test bench during “Drop-to-Idle” and “Drop-to-Overrun” experiments. The experiments show that those phases are critical having on the one hand a low exhaust gas mass flow and on the other hand a high oxygen percentage in the exhaust gas.

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.

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.

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.

To achieve global market and brand specific drivability characteristics as unique selling proposition for the increasing number of passenger car derivatives, an objectified evaluation approach for the drivability capabilities of the various cars is required. Thereto, it is necessary to evaluate the influence of different engine concepts in various complex and interlinked powertrain topologies during engine load change maneuvers based on physical criteria. Such an objectification approach enables frontloading of drivability related engineering tasks by the execution of drivability development and calibration work within vehicle subcomponent-specific closed-loop real-time co-simulation environments in early phases of a vehicle development program. So far, drivability functionalities could be developed and calibrated only towards the end of a vehicle development program, when test vehicles with a sufficient level of product maturity became available.

Numerical analyses of the liquid fuel injection and subsequent fuel-air mixing for a high-tumble direct injection engine with an active pre-chamber ignition system at operation conditions of 2000 RPM are presented. The Navier-Stokes equations for compressible in-cylinder flow are solved numerically using a hierarchical Cartesian mesh based finite-volume method. To determine the fuel vapor before ignition large-eddy flow simulations are two-way coupled with the spray droplets in a Lagrangian Particle Tracking (LPT) formulation. The combined hierarchical Cartesian mesh ensures efficient usage of high performance computing systems through solution adaptive refinement and dynamic load balancing. Computational meshes with approximately 170 million cells and 1.0 million spray parcels are used for the simulations.

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.

In this work, a transport and mixing model that calculates mixing in thermodynamic phase space was derived and validated. The mixing in thermodynamic multizone space is consistent to the one in the spatially resolved physical space. The model is developed using a turbulent channel flow as simplified domain. This physical domain of a direct numerical simulation (DNS) is divided into zones based on the quantitative value of transported scalars. Fluxes between the zones are introduced to describe mixing from the transport equation of the probability density function based on the mixing process in physical space. The mixing process of further scalars can then be carried out with these fluxes instead of solving additional transport equations. The relationship between the exchange flux in phase space and the concept of scalar dissipation are shown and validated by comparison to DNS results.

With increasing interest in new biofuel candidates, 1-octanol and di-n-butylether (DNBE) were presented in recent studies. Although these molecular species are isomers, their properties are substantially different. In contrast to DNBE, 1-octanol is almost a gasoline-type fuel in terms of its auto-ignition quality. Thus, there are problems associated with engine start-up for neat 1-octanol. In order to find a suitable glow-plug position, mixture formation is studied in the cylinder under almost idle operating conditions in the present work. This is conducted by planar laser-induced fluorescence in a high-speed direct-injection optical diesel engine. The investigated C8-oxygenates are also significantly different in terms of their evaporation characteristics. Thus, in-cylinder mixture formation of these two species is compared in this work, allowing conclusions on combustion behavior and exhaust emissions.

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.

In this paper different air management system concepts including mechanical superchargers and turbochargers are analysed with regard to their suitability for fuel cell applications. Therefore a simulation model which takes the main mass, energy and heat flows in the fuel cell system including fuel evaporation, reformer, gas cleaning, humidification, burner and compressor/expander unit into account was setup. For a PEM system with methanol steam reformer the best system efficiencies at rated power can be achieved with a turbocharger in combination with a tailgas burner for operating pressures between 2.5 and 2.8 bar. For pure hydrogen systems the best system efficiency is obtained with an electric driven supercharger for a maximum pressure of 2 bar and an appropriate pressure strategy during part load operation in the complete operating range. The increase of system efficiency for pressurized stack operation is mainly attributed to advantages with regard to water management.

Partly competing objectives, as low fuel consumption, low friction, long oil maintenance rate, and at the same time lowest exhaust emissions have to be fulfilled. Diminishing resources, continuously reduced development periods, and shortened product cycles yield detailed knowledge about oil consumption mechanisms in combustion engines to be essential. There are different ways for the lubricating oil to enter the combustion chamber: for example as blow-by gas, leakage past valve stem seals, piston rings (reverse blow-by) and evaporation from the cylinder liner wall and the combustion chamber. For a further reduction of oil consumption the investigation of these mechanisms has become more and more important. In this paper the influence of the mixture formation and the resulting fuel content in the cylinder liner wall film on the lubricant oil emission was examined.

With the implementation of the “Worldwide harmonized Light duty Test Procedure” (WLTP) and the highly dynamic “Real Driving Emissions” (RDE) tests in Europe, different engineering methodologies from virtual calibration approaches to Engine-in-the-loop (EiL) methods have to be considered to define and calibrate efficient exhaust gas aftertreatment technologies without the availability of prototype vehicles in early project phases. Since different types of testing facilities can be used, the effects of test benches as well as real and virtual vehicle operators have to be determined. Moreover, in order to effectively reduce harmful emissions, the reproducibility of test cycles is essential for an accurate and efficient application of exhaust gas aftertreatment systems and the calibration of internal combustion engines.

The optimization study presented herein is aimed to minimize the fuel consumption and engine-out emissions using commercially available EN15940 compatible HVO (Hydrogenated Vegetable Oil) fuel. The investigations were carried out on FEV’s 3rd generation HECS (High Efficiency Combustion System) multi-cylinder engine (1.6L, 4 Cylinder, Euro 6). Using a global DOE approach, the effects of calibration parameters on efficiency and emissions were obtained and analyzed. This was followed by a global optimization procedure to obtain a dedicated calibration for HVO. The study was aiming for efficiency improvement and it was found that at lower loads, higher fractions of low pressure EGR in combination with lower fuel injection pressures were favorable. At higher loads, a combustion center advancement, increase of injection pressure and reduced pilot injection quantities were possible without exceeding the noise and NOx levels of the baseline Diesel.

A conventional spark plug and a spark plug with smaller electrodes were studied in M.I.T.'s transparent square piston engine. The purpose was to learn more about how the electrode geometry affects the heat losses to the electrodes and the electrical performance of the ignition system, and how this affects the flame development process in an engine. A schlieren system which provides two orthogonal views of the developing flame was used to define the initial flame growth process, for as many as 100 consecutive cycles. Voltage and current waveforms were recorded to characterize the spark discharge, and cylinder pressure data were used to characterize the engine performance. The spark plug with the smaller electrodes was shown to reduce the heat losses to the electrodes, and thereby extend the stable operating regime of the engine. At conditions close to the stable operating limit, cycle-by-cycle variations in heat losses cause significant cyclic variations in flame development.

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 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.

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 requirement of reducing worldwide CO₂ emissions and engine pollutants are demanding an increased use of bio-fuels. Ethanol with its established production technology can contribute to this goal. However, due to its resistive auto-ignition behavior the use of ethanol-based fuels is limited to the spark-ignited gasoline combustion process. For application to the compression-ignited diesel combustion process advanced ignition systems are required. In general, ethanol offers a significant potential to improve the soot emission behavior of the diesel engine due to its oxygen content and its enhanced evaporation behavior. In this contribution the ignition behavior of ethanol and mixtures with high ethanol content is investigated in combination with advanced ignition systems with ceramic glow-plugs under diesel engine relevant thermodynamic conditions in a high pressure and temperature vessel.