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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.

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.

Noise, vibration and harshness (NVH) is one of the most important performance evaluation aspect of electric motors. Among the different causes of the NVH issues of electrical drives, the high-frequency spatial and temporal harmonics of the electrical drive system is of great importance. To reduce the tonal noise of the electric motors, harmonic injection methods can be applied. However, a lot of the existing related work focuses more on improving the optimization process of the parameter settings of the injected current/flux/voltage, which are usually limited to some specific working conditions. The applicability and effectivity of the algorithm to the whole frequency/speed range are not investigated. In this paper, a multi-domain pipeline of harmonic injection controller design for a permanent magnet synchronous motor (PMSM) is proposed.

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.

The thermal behavior of wires within the electrical distribution system (EDS) has a strong impact on the conductor cross section, the type of insulation, the derating, and the fusing system, and therefore on weight, cost, and reliability. Consequently, significant efforts have been made to develop sound static and dynamic thermal models for single wires and wire bundles. However, these models are based on the simplifying assumption that the object is completely surrounded by air, where, with the exception of free convection, airflow can be neglected, and where no interaction with other objects is considered. The approach presented in this paper takes into account the actual environment and routing within a vehicle, where some objects such as metal sheets can be considered as heat sinks and other objects, e.g. a motor block, as heat sources.

The increasingly stringent limits on pollutant emissions from internal combustion engine-powered vehicles require the optimization of advanced combustion systems by means of virtual development and simulation tools. Among the gaseous emissions from spark-ignition engines, the unburned hydrocarbon (HC) emissions are the most challenging species to simulate because of the complexity of the multiple physical and chemical mechanisms that contribute to their emission. These mechanisms are mainly three-dimensional (3D) resulting from multi-phase physics - e.g., fuel injection, oil-film layer, etc. - and are difficult to predict even in complex 3D computational fluid-dynamic (CFD) simulations. Phenomenological models describing the relationships between the physical-chemical phenomena are of great interest for the modeling and simplification of such complex mechanisms.

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.

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.

Within the scope of the development of autonomous vehicles, the mandatory reliability requirements of the electrical power supply, and consequently of the electrical distribution system (EDS), are increased considerably. In addition, the overall rising number of electrical functions leads to significantly higher electrical power demands, while strict cost, weight and packaging constraints must be upheld. Current developments focus on adding redundancies, enhancing physical robustness, or dimensioning critical components. New approaches address predictive power management, better diagnostic capabilities, and, the subject of this paper, alternative topologies and architectures [1]. These are derivations of the conventional tree structure, as well as ring- or linear-bus-based zonal architectures, which feature in part distributed storage devices or semiconductor switches that rearrange the power paths in case of a fault [2,3].

Besides optical and haptic criteria, the interior noise especially influences the quality impression of a vehicle. Separately audible disturbing noises are usually perceived as inadequate product quality. As a result, the reduction of disturbing noise components is a key factor for the overall product quality. Since the acoustic optimization is a complex and time consuming process, the need for an analysis tool which identifies automatically disturbing engine noise components within the vehicle interior noise is high. For this reason, a novel analysis tool has been developed which extracts tonal and impulsive engine noise components from the overall engine noise, and evaluates the annoyance of each noticeable engine component automatically. In addition, each disturbing noise is allocated to the emitting engine component. It is then possible to listen to each engine component noise individually and synthesize a target noise by superimposing manually weighted component noises.

Both the rising number of electrical systems and the electrical part of the powertrain are considerably increasing the electrical power requirements of vehicles. As a consequence, multiple voltage supply levels have been introduced. However, even if only the 12V/48V configuration is considered, as in this paper, the number of possible electrical distribution system (EDS) architectures is greatly enlarged. Additional degrees of freedom are the allocation of the loads to the voltage levels, the dimensioning of new components, and the control strategy. Hence, the optimization of such architectures must be based on simulation, which allows the evaluation of a multitude of variants and test scenarios within an acceptable time frame. While strict cost, weight, and quality constraints must be upheld, the stability of the voltage supply is a major focus because a significant part of future electrical systems is highly safety-critical.

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.

In order to reduce engine out CO2 emissions, it is a main subject to find new alternative fuels from renewable sources. For identifying the specification of an optimized fuel for engine combustion, it is essential to understand the details of combustion and pollutant formation. For obtaining a better understanding of the flame behavior, dynamic structure large eddy simulations are a method of choice. In the investigation presented in this paper, an n-heptane spray flame is simulated under engine relevant conditions starting at a pressure of 50 bar and a temperature of 800 K. Measurements are conducted at a high-pressure vessel with the same conditions. Liquid penetration length is measured with Mie-Scatterlight, gaseous penetration length with Shadowgraphy and lift-off length as well as ignition delay with OH*-Radiation. In addition to these global high-speed measurement techniques, detailed spectroscopic laser measurements are conducted at the n-heptane flame.

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.

Both the integration of safety-critical electrical systems and the increasing power requirements in vehicles present a challenge for electrical distribution systems in terms of reliability, packaging, weight, and cost. In this regard, the wire protection device is a key element, as it determines the reliability of the short circuit detection, the immunity against false tripping, and the wire diameters. Currently, in most cases, thermal fuses are used, due to their low cost and robust design. However, the description of their tripping behavior based only on steady-state currents is insufficient for the increasingly complex current profiles in vehicles. Thus, to achieve an optimum dimensioning of a fuse-wire combination, a profound understanding of the thermal behavior of both components under dynamic load conditions is mandatory. However, the FEM tools used for the thermal design of fuses are relatively slow, require huge calculation resources, and must be well-parameterized.

Currently, circuit breakers and, in most cases, thermal fuses are used for wire protection due to their low cost and robust design. As an alternative, solid state switches are being considered within future electrical distribution systems (EDS) for several reasons, e.g. resetability, diagnosis, smaller tolerances, and reduced dependencies on ambient temperature or arcing. Particularely if combined with benefits on the system level, such an application can be advantageous. The new approach presented in this paper uses a thermal model of the wire instead of only an emulation of the thermal fuse behavior. This allows, based on the electrical current profile, the calculation of the wire temperature and thus a robust and precise protection of the wire. In addition, it minimizes the probability of faulty switching, which is of particular importance with regard to safety-critical electrical functions.

The topology of an EDS, defined by the routing paths and by the location of the distribution boxes and the inline connectors, has a strong impact on weight and required amount of material, especially of copper, as well as on the manufacturing- and assembly time. Although a good part of the routing and packaging is fixed due to technical reasons and carry-over situations, in general there are enough optional paths and locations to allow up to several thousand alternative topologies. For these reasons, an optimization is possible as well as important. For such an optimization, in this paper a method is presented to concurrently minimize predefined criteria, e.g. the required copper, length of the wires, and the overall length of the wire bundles. It is based on designated algorithms for the variation of the topology, the routing, and the calculation of the optimization criteria as mentioned above.

A new approach is presented to modelling wall heat transfer in the exhaust port and manifold within 1D gas exchange simulation to ensure a precise calculation of thermal exhaust enthalpy. One of the principal characteristics of this approach is the partition of the exhaust process in a blow-down and a push-out phase. In addition to the split in two phases, the exhaust system is divided into several sections to consider changes in heat transfer characteristics downstream the exhaust valves. Principally, the convective heat transfer is described by the characteristic numbers of Nusselt, Reynolds and Prandtl. However, the phase individual correlation coefficients are derived from 3D CFD investigations of the flow in the exhaust system combined with Low-Re turbulence modelling. Furthermore, heat losses on the valve and the seat ring surfaces are considered by an empirical model approach.

The demand for lower CO2 emissions requires not just the optimization of every single component but the complete system. For a transmission system, it is important to optimize the transmission hardware as we well as the interaction of powertrain components. For automatic transmission with wide ratio spreads, the main losses are caused by the actuation system, which can be reduced with use of ondemand actuation systems. In this paper, a new on-demand electromechanical actuation system with validation results on a clutch test bench is presented. The electro-mechanical actuator shows an increase in the efficiency of 4.1 % compared to the conventional hydraulic actuation in a simulated NEDC (New European Driving Cycle) cycle. This increase is based on the powerless end positions of the actuator (engaged and disengaged clutch). The thermal tension and wear are compensated with a disk spring. This allows a stable control over service life.