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The integration of safety-critical and major power-consuming electrical systems presents a challenge for the development of future automotive electrical networks. Both reliability and performance must be enhanced in order to guarantee the power supply to essential electrical consumers at a sufficient degree of power quality. Often, in order to cope with these requirements, merely an upgrade of the existing wiring harness design is used, resulting in additional complexity, weight, and cost [3]. A characterization of the wiring harness and its electrical consumers facilitates a systematic optimization approach aimed at designing new automotive power networks [1, 5]. Measurement and analysis methods to characterise the thermal behaviour of the wiring harness have been presented and discussed in a previous paper [4] This paper presents and compares two methods aimed at modeling the electrical behavior of consumers at various voltages and temperatures.

Both the electrical portion of the powertrain and the rising number of auxiliary systems will considerably increase the electrical power requirements in future vehicles. In addition, multiple voltage supply levels will enhance the complexity of the electrical distribution system (EDS), while strict cost, weight, packaging, and safety constraints must be upheld, posing serious design challenges in terms of robustness, reliability and energy efficiency. Currently, a self-contained integral test or evaluation of the EDS is normally not applied. For such a purpose, quantitative quality criteria are introduced here which allow a comparative assessment of an EDS by addressing the dynamic and static stability of the supply voltage, the reliability of the fusing system, and the ability to provide the required electrical power. The presented approach uses both precisely-defined test scenarios and a comprehensive EDS test bench.

Prospective combustion engine applications require the highest possible energy conversion efficiencies for environmental and economic sustainability. For conventional Spark-Ignition (SI) engines, the quasi-hemispherical flame propagation combustion method can only be significantly optimized in combination with high excess air dilution or increased combustion speed. However, with increasing excess air dilution, this is difficult due to decreasing flame speeds and flammability limits. Pre-Chamber (PC) initiated jet ignition combustion systems significantly shift the flammability and flame stability limits towards higher dilution areas due to high levels of introduced turbulence and a significantly increased flame area in early combustion stages, leading to considerably increased combustion speeds and high efficiencies. By now, vehicle implementations of PC-initiated combustion systems remain niche applications, especially in combination with lean mixtures.

State-of-the-art spark-ignition engines mainly rely on the quasi-hemispherical flame propagation combustion method. Despite significant development efforts to obtain high energy conversion efficiencies while avoiding knock phenomena, achieved indicated efficiencies remain around 35 - 40 %. Further optimizations are enabled by significant excess air dilution or increased combustion speed. However, flammability limits and decreasing flame speeds with increasing air dilution prevent substantial improvements. Pre-Chamber (PC) initiated jet ignition combustion systems improve flame stability and shift flammability limits towards higher dilution levels due to increased turbulence and a larger flame area in the early Main-Chamber (MC) combustion stages. Simultaneously, the much-increased combustion speed reduces knock tendency, allowing the implementation of an innovative combustion method: PC-initiated jet ignition coupled with Spark-Assisted Compression Ignition (SACI).

The integration of safety-critical and major power-consuming electrical systems presents a challenge for the development of future vehicle power nets. Reliability and performance of the electrical network must be enhanced in order to guarantee the power supply to essential electrical consumers at a sufficient degree of power quality. This paper presents a test bench for automotive electrical networks based on a hardware-in-the-loop (HiL) platform. The test bench is used to assess the power and temperature behavior of the wiring harness and the connected power consumers. This characterisation facilitates the development of new tailored automotive electrical networks to meet the increased requirements while efficiently using the available resources.

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.

Modeling thermal systems in Battery Electric Vehicles (BEVs) is crucial for enhancing energy efficiency through predictive control strategies, thereby extending vehicle range. A major obstacle in this modeling is the often limited availability of detailed system information. This research introduces a methodology using neural networks for system identification, a powerful technique capable of approximating the physical behavior of thermal systems with minimal data requirements. By employing black-box models, this approach supports the creation of optimization-based operational strategies, such as Model Predictive Control (MPC) and Reinforcement Learning-based Control (RL). The system identification process is executed using MATLAB Simulink, with virtual training data produced by validated Simulink models to establish the method's feasibility. The neural networks utilized for system identification are implemented in MATLAB code.

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.

Tightening of emission norms necessitate intensified research in the field of emissions reduction. Fuel research opens up a vast area of potential improvement, since combustion behavior and the nature of the combustion products can be heavily influenced by fuel composition. In this paper, the effects of fuel properties on combustion and emissions shall be discussed, based on the study of standard diesel fuel, two types of diesel-like fuels and a kerosene fuel. Investigations were conducted on a single cylinder heavy duty direct-injected diesel engine operating under part-homogeneous combustion in the part-load operating range. For this purpose, a statistical design of experiments method (DOE) was utilized in order to evaluate the influence of each fuel property and, thus, develop a model for all selected fuels. Variation in EGR rates, injection and air patterns have significant effects on the combustion in the fuels under investigation.

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, these engines feature an increasing 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.

Lignocellulosic biomass consists of (hemi-) cellulose and lignin. Accordingly, an integrated biorefinery will seek to valorize both streams into higher value fuels and chemicals. To this end, this study evaluated the overall combustion performance of both cellulose- and lignin derivatives, namely the high cetane number (CN) di-n-butyl ether (DnBE) and low CN anisole, respectively. Said compounds were blended both separately and together with EN590 diesel. Experiments were conducted in a single cylinder compression ignition engine, which has been optimized for improved combustion characteristics with respect to low emission levels and at the same time high fuel efficiency. The selected operating conditions have been adopted from previous “Tailor-Made Fuels from Biomass (TMFB)” work.

Multi-motor powertrain topologies are playing an increasingly important role in the development of heavy duty battery electric trucks due to the changing driving requirements of these vehicles. The use of multiple motors and/or transmissions in a powertrain provides additional degrees of freedom for the energy management. The energy management system (EMS) consist of the gear selection strategy and torque split between the drive motors. The aim of the EMS is thereby to achieve high energy efficiency in motor and regenerative operation, while reducing the number of gear changes to ensure driving comfort. Ongoing research focuses on the energy management system of hybrid electric trucks, where the aim is to optimize the torque split between the combustion engine and the electric motor. In this paper, the EMS for an electric truck is described as a mixed-integer nonlinear control problem. This type of optimal control problem is notoriously difficult to solve.

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 paper presents a simulation environment for the test of driver assistance systems. It covers software-in-the-loop and hardware-in-the-loop test capabilities. In the hardware-in-the-loop (HiL) configuration, real components such as electronic control units (ECUs) and actuators are embedded in the system. First, requirements for a virtual environment are defined. They build the basis for the entire simulation. Special emphasis is given to the interaction between the simulated vehicle under test and its traffic environment. A virtual environment was developed in which the simulated vehicle can drive on a road together with the surrounding traffic. The simulation environment is composed mainly of a traffic scenario generator and a simulation of sensor behavior allowing the recognition of the vehicle's surroundings. Appropriate critical traffic scenarios are generated depending on the tested driver assistance system.

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.