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In electrified vehicles, auxiliary units can be a dominant source of noise, one of which is the refrigerant scroll compressor. Compared to vehicles with combustion engines, e-vehicles require larger refrigerant compressors, as in addition to the interior, also the battery and the electric motors have to be cooled. Currently, scroll compressors are widely used in the automotive industry, which generate one pressure pulse per revolution due to their discontinuous compression principle. This results in speed-dependent pressure fluctuations as well as higher-harmonic pulsations that arise from reflections. These fluctuations spread through the refrigeration cycle and cause the vibration excitation of refrigerant lines and heat exchangers. The sound transmission path in the air conditioning heat exchanger integrated in the dashboard is particularly critical. Various silencer configurations can be used to dampen these pulsations.

Society is moving towards climate neutrality where hydrogen fuelled combustion engines (H2 ICE) could be considered a main technology. These engines run on hydrogen (H2) so carbon-based emission are only present at a very low level from the lube oil. The most important pollutants NO and NO2 are caused by the exhaust aftertreatment system as well as CO2 coming from the ambient air. For standard measurement technologies these low levels of CO2 are hard to detect due to the high water content. Normal levels of CO2 are between 400-500 ppm which is very close or even below the detection limit of commonly used non-dispersive-infrared-detectors (NDIR). As well the high water content is very challenging for NOx measuring devices, like chemiluminescence detectors (CLD), where it results in higher noise and therefore a worse detection limit. Even for Fourier-transformed-infrared-spectroscopy-analysers (FT-IR) it is challenging to deal with water content over 15% without increased noise.

The production of electric vehicles (EVs) has a significant environmental impact, with up to 50 % of their lifetime greenhouse gas potential attributed to manufacturing processes. The use of sustainable materials in EV design is therefore crucial for reducing their overall carbon footprint. Wood laminates have emerged as a promising alternative due to their renewable nature. Additionally, wood-based materials offer unique damping properties that can contribute to improved Noise, Vibration, and Harshness (NVH) characteristics. In comparison to conventional materials such as aluminum, ply wood structures exhibit beneficial damping properties. The loss factor of plywood structures with a thickness below 20 mm ranges from 0.013 to 0.032. Comparable aluminum structures however exhibit only a fraction of this loss factor with a range between 0.002 and 0.005.

In an era characterized by the rapid proliferation and advancement of AI-based technologies across various domains, the spotlight is placed on the integration of these technologies into trustworthy autonomous systems. The integration into embedded systems necessitates a heightened focus on dependability. This paper combines the findings from the TEACHING project, which delves into the foundations of humanistic AI concepts, with insights derived from an expert workshop in the field of dependability engineering. We establish the body of knowledge and key findings deliberated upon during an expert workshop held at an international conference focused on computer safety, reliability and security. The dialogue makes it evident that despite advancements, the assurance of dependability in AI-driven systems remains an unresolved challenge, lacking a one-size-fits-all solution.

In battery electric vehicles (BEV), thermal management is a key technique to improve efficiency and lifetime. Currently, manufacturers use different cooling concepts with numerous architectures. This work describes the development of a co-simulation framework to optimize BEV thermal management on system level, using advanced simulation methodologies also on component level, merging simulation and testing. Due to interactions between multiple conditioning circuits, thermal management optimization requires an overall vehicle approach. Thus, a full vehicle co-simulation of a BEV is developed, combining 1D thermal management software KULI and MATLAB/Simulink. Within co-simulation, the precise modeling of vehicle’s subsystems is important to predict thermal behavior and to calculate dynamic heating and cooling demands as well as exchanged energy flows with the thermal management system.

As alternative to electrification or carbon free fuels such as hydrogen, CO2-neutral fuels have been researched aiming to decrease the impact of fossil energy sources on the environment. Despite the potential benefit of capturing CO2 emission after combustion for own fuel production, the so-called eFuels also benefit by using a green source of energy during their fabrication. Among all the possibilities for eFuels, alcohols, ethers (such as MTBE and ETBE) and alternative hydrocarbons have shown positive impacts regarding emission reduction and performance when compared to standard gasoline. Previously in [1] and [2], synthetic fuels and methanol blends were tested at steady state conditions in order to verify advantages and drawbacks relative to gasoline, for power-sport motorcycles.

The objective of this experimental investigation was to analyze the effect of various exhaust gas aftertreatment technologies on particulate number emissions (PN) of an MPFI EU5 motorcycle. Specifically, three different aftertreatment strategies were compared, including a three-way-catalyst (TWC) with LS structure as the baseline, a hybrid catalyst with a wire mesh filter, and an optimized gasoline particulate filter (GPF) with three-way catalytic coating. Experimental investigations using the standard test cycle WMTC performed on a two-wheeler chassis dynamometer, while the inhouse particulate sampling system was utilized to gather information about size-dependent filtering efficiency, storage, and combustion of nanoparticles. The particulate sampling and measuring system consist of three condensation particle counters (CPCs) calibrated to three different size classes (SPN4, SPN10, SPN23).

Currently, emission regulations for the LVs using standard spark ignited ICEs considering only gaseous pollutants, just as CO, HC and NOx. Following the upcoming legislation for personal vehicles sector, the LVs might also include limits of PN and PM. Regarding fuel injection strategies, the MPFI which was previously excluded from particulate control will be incorporated into the new regulation [1]. In terms of social harm, there will be a necessity to reduce engine particulate emissions, as they are known for being carcinogenic substances [2, 3, 4]. Generally, the smaller the particulate diameter, the more critical are the damages for human health therefore, the correct determination of PN and particulate diameter is essential. Beside future challenges for reducing and controlling particulates, the reduction of fossil fuel usage is also an imminent target, being the replacement by eFuels one of the most promising alternatives.

In order to combat the predicted consequences of climate change, major efforts are required in all industries. The use of hydrogen in combustion engines can make a valuable contribution as a CO2-free bridging technology. A challenge in the development of a suitable combustion process is the high ignitibility of hydrogen with an impact on combustion stability due to combustion anomalies. The avoidance of such combustion phenomena is of the highest relevance for ensuring long-term stable engine operation as far as possible. The present paper deals with the multifractional topic of abnormal combustion phenomena in the hydrogen engine.

To achieve global climate goals, greenhouse gas emissions must be drastically reduced. The energy and transportation sectors are responsible for about one third of the greenhouse gases emitted worldwide, and they often use internal combustion engines (ICE). One effective way to decarbonize ICEs may be to replace carbon-containing fossil fuels such as natural gas entirely, or at least partially, with hydrogen. Cost-effective development of sustainable combustion concepts for hydrogen and natural gas/hydrogen mixtures in ICEs requires the intensive use of fast and robust simulation tools for prediction. The key challenge is appropriate modeling of flame front propagation. This paper evaluates and applies different approaches to modeling laminar flame speeds from the literature. Both appropriate models and reaction kinetic calculations are considered.

Precise prediction of combustion parameters such as peak firing pressure (PFP) or crank angle of 50% burned mass fraction (MFB50) is essential for optimal engine control. These quantities are commonly determined from in-cylinder pressure sensor signals and are crucial to reach high efficiencies and low emissions. Highly accurate in-cylinder pressure sensors are only applied to test rig engines due to their high cost, limited durability and special installation conditions. Therefore, alternative approaches which employ virtual sensing based on signals from non-intrusive sensors retrieved from common knock sensors are of great interest. This paper presents a comprehensive comparison of selected approaches from literature, as well as adjusted or further developed methods to determine engine combustion parameters based on knock sensor signals. All methods are evaluated on three different engines and two different sensor positions.

The current European fleet of vehicles is ageing and lifetime mileages are rising proportionally. Consequently, a substantial fraction of the vehicle fleet is currently operating at mileages well beyond current durability legislation (≤ 160,000 km). Emissions inventories and models show substantial increases in emissions with increasing mileage, but knowledge of the effect of emissions control system deterioration at very high mileages is sparse. Emissions testing has been conducted on matched pairs (or more) of diesel and gasoline (and CNG) vehicles, of low and high mileage, supplementing the results with in-house data, in order to explore high mileage emission deterioration factors (DF). The study isolated, as far as possible, the effect of emissions deterioration with mileage, by using nominally identical vehicle models and controlling other variables.

In the acoustic design of flow guiding components, novel simulation concepts for predicting relevant sound sources in the early design state become increasingly important. This requires accurate numerical methods to describe the involved phenomena. The present study computationally investigates the flow-induced aeroacoustic sound sources, generated in turbulent pipe flow. The analysis follows a hybrid approach, where the acoustic sound field is predicted separately from the underlying turbulent flow field, supplied with acoustic source terms from an incompressible flow simulation of the considered configuration in the limit of low Mach number. Source terms for use as input into different acoustic wave equations, the Lighthill wave equation, the vortex sound theory, and the Perturbed Convective Wave Equation (PCWE) are computed performing incompressible Direct Numerical Simulations (DNS) and Large-Eddy Simulations (LES) of fully developed pipe flow.

Aiming at the test safety problems in the early stage of self-driving cars development, firstly the virtual vehicle on-board CAN data acquisition module of the present project was designed based on virtual LabVIEW. Then a wireless remote control system for the self-driving car was constructed, which integrated the built virtual vehicle on-board CAN data acquisition system, the remote real-time image monitoring module and the remote upper computer control module based on ZigBee wireless transmission. It can execute the environmental awareness training and continuous and complex motion manipulation testing of the vehicle without relying on the driver, which can solve the safety problems in the tests of initial development of self-driving cars. Finally, the four-wheel independent steering electric vehicle was used as the self-driving test vehicle, and the wireless remote control system was tested on the double lane change type path and S-type path.

The demand for high efficiency powertrains in automotive engineering is further increasing, with hybrid powertrains being a feasible option to cope with new legislations. So far hybridization has only played a minor role for L-category vehicles. Focusing on an exemplary high-power L-category on-road vehicle, this research aims to show a new development approach, which combines longitudinal dynamic simulation (LDS) with “Design of Experiments” (DoE) in course of hybrid electric powertrain development. Furthermore, addressing the technological aspect, this paper points out how such a vehicle can benefit from 48V-hybridization of its already existing internal combustion powertrain. A fully parametric LDS model is built in Matlab/Simulink, with exchangeable powertrain components and an adaptable hybrid operation strategy. Beforehand, characterizing decisions as to focus on 48V and on parallel hybrid architecture are made.

On the basis of small hybrid powertrain investigations in hand-held power tools for fuel consumption and emissions reduction, the prototype hybrid configuration of a small single-cylinder four-stroke internal combustion engine together with a brushless DC electric motor is built and measured on the testbench in terms of efficiency and emissions but also torque and power capabilities. The onboard energy storage system allows the combustion engine electrification for controlling the fuel amount and the combustion behavior while the electric motor placement instead of the pull-start and flywheel allows for start-stop of the system and load point shifting strategy for lower fuel consumption. The transient start-up results as well as the steady-state characterization maps of the system can set the limits on the fuel consumption reduction for such a hybrid tool compared with the baseline combustion-driven tool for given load cycle characteristics.

Virtual sensing refers to the processing of desired physical data based on measured values. Virtual sensors can be applied not only to obtain physical quantities which cannot be measured or can only be measured at an unreasonable expense but also to reduce the number of physical sensors and thus lower costs. In the field of spark ignited internal combustion engines, the virtual sensing approach may be used to predict the cylinder pressure signal (or characteristic pressure values) based on the acceleration signal of a knock sensor. This paper presents a method for obtaining the cylinder pressure signal in the high-pressure phase of an internal combustion engine based on the measured acceleration signal of a knock sensor. The approach employs a partial differential equation to represent the physical transfer function between the measured signal and the desired pressure. A procedure to fit the modeling constants is described using the example of a large gas engine.

Drivetrain electrification is increasing in the kart racing sector since noise emissions are an important factor in urban areas. To improve range, it has become necessary to optimize the rolling resistance of kart racing tires. This paper introduces a parameter study for small bias-ply tires which are used in kart racing and investigates the effect of these parameters on rolling resistance. In recent literature, rolling resistance is mostly examined in radial passenger car tires. Most testing devices are limited to rim sizes from ten inches upwards. In this study, a test rig was developed with focus on low cost and small rim sizes. This self-developed test rig was validated through a comparison with an approved test rig according to ISO 18164 standard. A parameter study was conducted to investigate the effect of changes in the construction of the tire. These changes affect the warp count of the carcass fabric and the crown angle of the different plies.

In-cylinder flows in internal combustion engines are highly turbulent in nature. An important property of turbulence that plays a key role in mixture formation is anisotropy; it also influences ignition, combustion and emission formation. Thus, understanding the turbulence anisotropy of in-cylinder flows is critical. Since the most widely used two-equation Reynolds-averaged Navier-Stokes (RANS) turbulence models assume isotropic turbulence, they are not suitable for correctly capturing the anisotropic behavior of turbulence. However, large eddy simulation (LES) can account for the anisotropic behavior of turbulence. In this paper, the Reynolds stress tensor (RST) is analyzed to assess the predictive capability of RANS and LES with regard to turbulence anisotropy. The influence of mesh size on turbulence anisotropy is also looked into for multi-cycle LES.

In the ongoing competition of powertrain concepts the Internal Combustion Engine (ICE) will also have to demonstrate its potential for increased efficiency [1]. Variable Compression Ratio (VCR) Systems for Internal Combustion Engines (ICE) can make an important contribution to meeting stringent global fuel economy and CO2 standards. Using such technology a CO2 reduction of between 5% and 9% in the World Harmonized Light-Duty Vehicle Test Cycle (WLTC) are achievable, depending on vehicle class, load profile and power rating [2]. This paper provides a detailed description of the measurement approaches that are used during development of the AVL Dual Mode VCSTM and other VCR systems in fired operation. Results obtained from these measurements are typically used to calibrate or verify simulation models, which themselves are an integral part of the development of these systems [3].