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The ongoing energy transition will have a profound impact on future mobility, with electrification playing a key role. Battery electric vehicles (EVs) are the dominant technology, relying on the conversion of alternating current (AC) from the grid to direct current (DC) to charge the traction battery. This process involves power electronic components such as rectifiers and DC/DC converters operating at high switching frequencies in the kHz range. Fast switching is essential to minimize losses and improve efficiency, but it might also generate electromagnetic interferences (EMI). Hence, electromagnetic compatibility (EMC) testing is essential to ensure reliable system operations and to meet international standards. During DC charging, the AC/DC conversion takes place off-board in the charging station, allowing for better cooling and larger components, resulting in increased power transfer, currently up to 350 kW.

A potential route to reduce CO2 emissions from heavy-duty trucks is to combine low-carbon fuels and a hybrid-electric powertrain to maximize overall efficiency. A hybrid electric powertrain can reduce the peak power required from the internal combustion engine, leading to opportunities to reduce the engine size but still meet vehicle performance requirements. Although engine downsizing in the light-duty sector can offer significant fuel economy savings mainly due to increased part-load efficiency, its benefits and downsides in heavy-duty engines are less clear. As there has been limited published research in this area to date, there is a lack of a standardized engine downsizing procedure.

Test of autonomous systems is mostly brute force and ad-hoc thus being neither efficient nor transparent. Though requirements invite for a situational transparency, a framework is missing to judge quality of requirements and derived test-cases. Practical challenges are state explosion, difficulty to derive corner cases, no systematic safety of the intended functionality as specified, lack of accepted KPI, etc. Maintaining a valid safety case is hardly possible with such adaptive systems and continuous software updates. To achieve trusted autonomous vehicles, test cases must be generated automatically while at same time providing coverage (e.g., indicating progress with KPI), efficiency (e.g., limiting the amount of regression testing) and transparency (e.g., showing how specific corner cases are tested in case of accidents). This paper provides a method for automatically generating test cases for AI-based autonomous systems and compares it with existing testing methods.

Urban air mobility (UAM) is a fast-growing industry that utilizes electric vertical take-off and landing (eVTOL) technologies to operate in densely populated urban areas with limited space. However, atmospheric icing serves as a limitation to its operational envelope as in-flight icing can happen all year round anywhere around the globe. Since icing in smaller aviation systems is still an emerging topic, there is a necessity to study icing of eVTOL rotors specifically. Two rotor geometries were chosen for this study. A small 15-inch rotor was selected to illustrate a multirotor UAV drone, while a large 80-inch rotor was chosen to represent a UAM passenger aircraft. The ice accretion experiments were conducted in an icing wind tunnel on the small 15-inch rotor. The icing simulations were performed using FENSAP-ICE. The ice accretion simulations of the 15-inch rotor sections at –5 °C show a large, rather streamlined ice shape instead of the expected glaze ice characteristics.

Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains.

In this paper, the differences between a production car of the 2018 A-class and an early stage vehicle model with a mostly similar outer skin are examined experimentally and numerically. The aerodynamic development of vehicles at Mercedes-Benz is divided into several phases. When comparing force coefficients differences can be observed between these distinct hardware stages as well as when comparing steady state simulations to wind tunnel measurements. In early phases when prototype vehicles are not yet available, so-called aero foam models are used. These are well-defined full-sized vehicle models, as the outer skin is milled from Polyurethane. Important aerodynamic characteristics such as a motor compartment with a cooling module, deflecting axles with rotatable wheels and underbody covers are represented.

The development and calibration of modern combustion engines is challenging in the area of continuously tightening emission limits and the necessity for meeting real driving emissions regulations. A focus is on the knowledge of the internal engine processes and the determination of pollutants formations in order to predict the engine emissions. A physical model-based development provides an insight into hardly measurable phenomena properties and is robust against changing input data. With increasing modeling depth the required computing capacities increase. As an alternative to physical modeling, data-driven machine learning methods can be used to enable high-performance modeling accuracy. However, these are dependent on the learned data. To combine the performance and robustness of both types of modeling a hybrid application of data-driven and physical models is developed in this paper as a grey box model for the exhaust emission prediction of a commercial vehicle diesel engine.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

The development and qualification of distributed and highly safety-critical avionics systems implicate high efforts and risks. The resulting costs usually limit implementations like fly-by-wire systems to the military or commercial airliner domains. The aim of previous and ongoing research at the Institute of Aircraft Systems at University of Stuttgart is the reduction of these costs and therefore open up their benefits, inter alia, to general aviation, remotely piloted or unmanned aircraft. An approach for an efficient development is the application of a platform based development which supports the reuse of software and hardware components. The Flexible Platform adopts this approach. It is accompanied by a tool suite which automates the design and parameter instantiation, documentation generation and the generation of verification artifacts for a platform instance. This paper presents the approach for the requirement document generation compliant to ARP4754A and DO-178C.

The effects of Variable Valve Timing (VVT) on Homogeneous Charge Compression Ignition (HCCI) engine energy distribution and waste heat recovery are investigated using a fully flexible Electromagnetic Variable Valve Timing (EVVT) system. The experiment is carried out in a single cylinder, 657 cc, port fuel injection engine fueled with n-heptane. Exergy analysis is performed to understand the relative contribution of different loss mechanisms in HCCI engines and how VVT changes these contributions. It is found that HCCI engine brake thermal efficiency, the Combined Heat and Power (CHP) power to heat ratio, the first and the second law efficiencies are improved with proper valve timing. Further analysis is performed by applying the first and second law of thermodynamics to compare HCCI energy and exergy distribution to Spark Ignition (SI) combustion using Primary Reference Fuel (PRF). HCCI demonstrates higher fuel efficiency and power to heat and energy loss ratios compared to SI.

In order to achieve high customer satisfaction and to avoid high warranty costs caused by component failures of the power-train of hybrid electric vehicles (HEV), car manufacturers have to optimize the dimensioning of these elements. Hence, it is obligatory for them to gain knowledge about the different types of vehicle usage being predominant all over the world. Therefore, in this paper we present a Data Mining system that employs a Random Forest (RF) based dissimilarity measure in the dimensionality reduction technique t-Distributed Stochastic Neighbor Embedding (t-SNE) to automatically identify and visualize different types of vehicle usage by applying these methods to aggregated logged on-board data, i.e., load spectrum data. This kind of data is calculated and recorded directly on the control units of the vehicles and consists of aggregated numerical data, like the histogram of the velocity signal or the traveled distance of a vehicle.

Gasoline direct injection engines can greatly improve the fuel economy, but the idea mixture distribution cannot be easily controlled. In this paper, the linearized instability sheet atomization (LISA) and large eddy simulation (LES) implemented into KIVA-3V code were used to study the gasoline hollow cone spray process for gasoline direct injection (GDI) in a constant volume vessel. The three-dimensional results show that the LISA model can effectively simulate the gasoline hollow cone spray and obtain the string structure compared to the experiment data. And the velocity interpolation method can reduce the grid dependency of spray simulation. Using dense grid (about 8 million cells) in LES and RANS all can obtain the good spray tip penetration and width. Unlike diesel spray, for gasoline spray there are not big difference between the results using LES and RANS. In additional the ambient pressure significantly influence the gasoline spray shape.

In this work, large eddy simulation (LES) with a K-equation subgrid turbulent kinetic energy model is implemented into the CFD code KIVA3V to study the features of liquid fuel spray and combustion using gradually varying grid in a constant volume chamber. The characteristic time-scale combustion model (CTC) incorporating a turbulent timescale is adopted to predict the combustion process and the SHELL auto-ignition model is used to predict auto-ignition. Combustion is also simulated using Parallel Detailed Chemistry with Lu's n-heptane reduced mechanism (58 species), which has been added into the KIVA3V-LES code. The computational results are compared with Sandia experimental data for non-reacting and reacting cases. As a result, LES can capture the complex structure of the spray and temperature distribution as well as the trend of ignition delay and flame lift-off length variations. Better results are obtained using the Parallel Detailed Chemistry than the CTC model.

More than 20 years after the first presentation of the heat transfer equation according to Bargende [1,2], it is time to introduce some useful additions and enhancements, with respect to new and advanced combustion principles like diesel- and gasoline- homogeneous charge compression ignition (HCCI). In the existing heat transfer equation according to Bargende the calculation of the actual combustion chamber surface area is formulated in accordance with the work of Hohenberg. Hohenberg found experimentally that in the piston top land only about 20-30% of the wall heat flux values from the combustion chamber are transferred to the liner and piston wall. Hohenberg explained this phenomenon that is caused by lower gas temperature and convection level in charge within the piston top land volume. The formulation just adds the existing piston top land surface area multiplied by a specified factor to the surface of the combustion chamber.

This paper presents a large eddy simulation study of the liquid spray mixing with hot ambient gas in a constant volume vessel under engine-like conditions with the injection pressure of 1500 bar, ambient density 22.8 kg/m₃, ambient temperature of 900 K and an injector nozzle of 0.09 mm. The simulation results are compared with the experiments carried out by Pickett et al., under similar conditions. Under modern direct injection diesel engine conditions, it has been argued that the liquid core region is small and the droplets after atomization are fine so that the process of spray evaporation and mixing with the air is controlled by the heat and mass transfer between the ambient hot gas and central fuel flow. To examine this hypothesis a simple spray breakup model is tested in the present LES simulation. The simulations are performed using an open source compressible flow solver, in OpenFOAM.

Natural gas is a challenging fuel for HCCI engines because its single-stage ignition and rapid combustion make it difficult to optimize combustion timing over a significant load range. This study investigates direct injection of a pilot quantity of high-cetane fuel near TDC as a range extension and combustion control mechanism for natural gas HCCI engines. The EGR and load range is studied in a supercharged natural gas HCCI engine equipped with external EGR, intake heating and a direct injection system for n-heptane pilot fuel. The operating range and emissions are of primary interest and are compared between both the baseline HCCI engine with variable intake temperature and the direct injected HCCI (DI-HCCI) engine with constant intake temperature. Test results show the EGR and load range at fixed intake temperature can be extended using pilot direct injection.

A physics-based control-oriented model is developed to dynamically predict cycle-to-cycle combustion timing in transient fueling conditions for Homogeneous Charge Compression Ignition (HCCI) engines. The model simulates the engine cycle from the intake stroke to the exhaust stroke and includes the thermal coupling dynamics caused by the residual gases from one cycle to the next cycle. A residual gas model, a modified knock integral model, a fuel burn rate model, and thermodynamic models for the gas state in combustion and exhaust strokes are incorporated to simulate the engine cycle. The gas exchange process, generated work and completeness of combustion are predicted using semi-empirical correlations. The resulting model is parameterized for the combustion of Primary Reference Fuel (PRF) blends using 5703 simulations from a detailed thermo-kinetic model. Semi-empirical correlations in the model are parameterized using the experimental data obtained from a single-cylinder engine.

The paper describes a novel method for calculating the residual gas fraction and the trapping efficiency in a 2 stroke engine. Assuming one dimensional compressible flow through the inlet and exhaust ports, the method estimates the instantaneous mass flowing in and out from the combustion chamber; later the residual gas fraction and trapping efficiency are estimated combining together the perfect displacement and perfect mixing scavenging models. It is assumed that when the intake port opens, the fresh mixture is pushing out the burned charge without any mixing and after a multiple of the time needed for the largest eddy to perform one rotation, the two gasses are instantly mixed up together and expelled. The result is a very simple algorithm that does not require much computational time and is able to estimate with high level of precision the trapping efficiency and the residual gas fraction in 2 stroke engines.

It has been known from multi-zone simulations that HCCI combustion can be significantly affected by temperature stratification of the in-cylinder gas. With the same combustion timing (i.e. crank angles at 50% heat release, denoted as CA50), large temperature stratification tends to prolong the combustion duration and lower down the in-cylinder pressure-rise-rate. With low pressure-rise-rate HCCI engines can be operated at high load, therefore it is of practical importance to look into more details about how temperature stratification affects the auto-ignition process. It has been realized that multi-zone simulations can not account for the effects of spatial structures of the stratified temperature field, i.e. how the size of the hot and cold spots in the temperature field could affect the auto-ignition process. This question is investigated in the present work by large eddy simulation (LES) method which is capable of resolving the in-cylinder turbulence field in space and time.

Nowadays for small-scale power generation there are electrochemical batteries and mini engines. Many efforts have been done for improving the power density of the batteries but unfortunately the value of 1 MJ/kg seems to be asymptotic. If the energy source is an organic fuel which has an energy density of around 29 MJ/kg with a minimum overall efficiency of only 3.5%, this device would surpass the batteries. This paper is the fifth of a series of publications aimed to study the HCCI combustion process in the milli domain at high engine speed in order to design and develop VIMPA, Vibrating Microengine for Low Power Generation and Microsystems Actuation. Previous studies ranged from general characterization of the HCCI combustion process by using metal and optical engines, to more specific topics for instance the influence of the boundary layer and quenching distance on the quality of the combustion.