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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.

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 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.

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.

As a result of the ever-increasing application of cyber-physical components in the automotive industry, cybersecurity has become an urgent topic. Adapting technologies and communication protocols like Ethernet and WiFi in connected vehicles yields many attack scenarios. Consequently, ISO/SAE 21434 and UN R155 (2021) define a standard and regulatory framework for automotive cybersecurity, Both documents follow a risk management-based approach and require a threat modeling methodology for risk analysis and identification. Such a threat modeling methodology must conform to the Threat Analysis and Risk Assessment (TARA) framework of ISO/SAE 21434. Conversely, existing threat modeling methods enumerate isolated threats disregarding the vehicle’s design and connections. Consequently, they neglect the role of attack paths from a vehicle’s interfaces to its assets.

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 absence of combustion engine noise pushes increasingly attention to the sound generation from other, even much weaker, sources in the acoustic design of electric vehicles. The present work focusses on the numerical computation of flow induced noise, typically emerging in components of flow guiding devices in electro-mobile applications. The method of Large-Eddy Simulation (LES) represents a powerful technique for capturing most part of the turbulent fluctuating motion, which qualifies this approach as a highly reliable candidate for providing a sufficiently accurate level of description of the flow induced generation of sound. Considering the generic test configuration of turbulent pipe flow, the present study investigates in particular the scope and the limits of incompressible Large-Eddy Simulation in predicting the evolution of turbulent sound sources to be supplied as source terms into the acoustic analogy of Lighthill.

A new artificial intelligence (model order reduction) / finite element coupled approach will be presented for the risk assessment of battery fire during a car crash event. This approach combines standard crash finite element for the main car body with a reduced order model for the battery. Simulation is today used by automotive engineering teams to design lightweight vehicle bodies fulfilling vehicle safety regulations. Legislation is rapidly evolving to accommodate the growing electrical vehicle market share and is considering additional battery safety requirements. The focus is on avoiding internal short circuit due to internal damage within a cell which may result in a fire hazard. Assessing short circuit risk in CAE at the vehicle level is complex as there involves phenomena at different scales. The vehicle deforms on a macroscale level during the impact event.

Given approximately one million small and light aircraft in operation worldwide, icing detection and icing quantification of in-flight icing are still an open research topic. Despite technical means are available to de-ice on ground, there is a lack of a suitable control system based on sensor data to de-ice while the aircraft is airborne. Most often, it is still task of the pilot to visually inspect the icing status of the airfoil and/or other critical parts of the aircraft such as engine air intakes, which distracts the flight crew from flying the aircraft especially in IMC conditions. Based on preliminary simulation and tests in 2014 in a collaborative research project lasting from 2015 until 2018, the technology of energy self-sustaining, wireless, self-adhesive smart sensors for industrial sensing in an aerodynamically critical environment (i.e. wind turbines) was further investigated to fulfil general aviation requirements.

A reduction in CO2 emissions and consequently fuel consumption is essential in the context of future greenhouse gas limits. With respect to the thermodynamic loss analysis of an internal combustion engine, a gap between the net indicated thermal efficiency and the brake thermal efficiency is recognizable. This share is caused by friction losses, which are the focus of this research project. The parasitic loss reduction potential by replacing the mechanical water pump with an electric coolant pump is discussed in the course of this work. This is not a novel approach in light duty vehicles, whereas in commercial vehicles a rigid drive of all auxiliaries is standard. Taking into account an implementation of a 48-V power system in the short or medium term, an electrification of auxiliary components becomes feasible. The application of electric coolant pumps on an Euro VI certified 6-cylinder in-line heavy-duty diesel engine regarding fuel economy was thus performed.

From an acoustical point of view, the integration of seatbelt retractors in a vehicle is a real challenge that has to be met early in the vehicle development process. The buzz and rattle noise of seat belt retractors is a weak yet disturbing interior noise. Street irregularities excite the wheels and this excitation is transferred via the car body to the mounting location of the retractor. Ultimately, the inertia sensor of the locking mechanism is also excited. This excitation can be amplified by structural resonances and generate a characteristic impact noise. The objective of this paper is to describe a simulation method for an early development phase that predicts the noise-relevant low frequency local modes and consequently the contact of the retractor with the mounting panel of the car body via the finite element method.

Car driver’s behavior and its influence on driving characteristics play an increasing role in the development of modern vehicles, e.g. in view of efficient powertrain control and implementation of driving assistance functions. In addition, knowledge about actual driving style can provide feedback to the driver and support efficient driving or even safety-related measures. Driving patterns are caused not only by the driver, but also influenced by road characteristics, environmental boundary conditions and other traffic participants. Thus, it is necessary to take the driving circumstances into account, when driving patterns are studied. This work proposes a methodology to cluster and evaluate driving patterns under consideration of vehicle-related parameters (e.g. acceleration and jerk) in combination with additional influencing factors, e.g. road style and inclination. Firstly, segmentation of the trip in distance series is performed to generate micro cycles.

Real world operating scenarios have a major influence on emissions and fuel consumption. To reduce climate-relevant and environmentally harmful gaseous emissions and the exploitation of fossil resources, deep understanding concerning the real drive behavior of mobile sources is needed because emissions and fuel consumption of e.g. passenger cars, operated in real world conditions, considerably differ from the officially published values which are valid for specific test cycles only [1]. Due to legislative regulations by the European Commission a methodology to measure real drive emissions RDE is well approved for heavy duty vehicles and automotive applications but may not be adapted similar to two-wheeler-applications. This is due to several issues when using the state of the art portable emission measurement system PEMS that will be discussed.

The development of future internal combustion engines and fuels is influenced by decreasing energy resources, restriction of emission legislation and increasing environmental awareness of humanity itself. Alternative renewable fuels have, in dependency on their physical and chemical properties, on the production process and on the raw material, the potential to contribute a better well-to-wheel-CO2-emission-balance in automotive and nonautomotive applications. The focus of this research is the usage of alcohol fuels, like ethanol and 2-butanol, in motorcycle high power engines. The different propulsion systems and operation scenarios of motorcycle applications in comparison to automobile applications raise the need for specific research in this area.

The presented paper focuses on the computation of heat transfer related to continuously variable transmissions (CVTs). High temperatures are critical for the highly loaded rubber belts and reduce their lifetime significantly. Hence, a sufficient cooling system is inevitable. A numerical tool which is capable of predicting surface heat transfer and maximum temperatures is of high importance for concept design studies. Computational Fluid Dynamics (CFD) is a suitable method to carry out this task. In this work, a time efficient and accurate simulation strategy is developed to model the complexity of a CVT. The validity of the technique used is underlined by field measurements. Tests have been carried out on a snowmobile CVT, where component temperatures, air temperatures in the CVT vicinity and engine data have been monitored. A corresponding CAD model has been created and the boundary conditions were set according to the testing conditions.

Reynolds-averaged Navier-Stokes (RANS) computations of heat transfer involving wall bounded flows at elevated Prandtl numbers typically suffer from a lack of accuracy and/or increased mesh dependency. This can be often attributed to an improper near-wall turbulence modeling and the deficiency of the wall heat transfer models (based on the so called P-functions) that do not properly account for the variation of the turbulent Prandtl number in the wall proximity (y+< 5). As the conductive sub-layer gets significantly thinner than the viscous velocity sub-layer (for Pr >1), treatment of the thermal buffer layer gains importance as well. Various hybrid strategies utilize blending functions dependent on the molecular Prandtl number, which do not necessarily provide a smooth transition from the viscous/conductive sub-layer to the logarithmic region.

Small displacement two-stroke engines are widely used as affordable and low-maintenance propulsion systems for motorcycles, scooters, hand-held power tools and others. In recent years, considerable progress regarding emission reduction has been reached. Nevertheless, a further improvement of two-stroke engines is necessary to cover protection of health and environment. In addition, the shortage of fossil fuel resources and the anthropogenic climate change call for a sensual use of natural resources and therefore, the fuel consumption and engine efficiency needs to be improved. With the application of suitable analyses methods it is possible to find improving potential of the working processes of these engines. The thermodynamic loss analysis is a frequently applied method to examine the working process and is universally adaptable.

A steady increasing share and complexity of automotive software is a huge challenge for quality management during software development and in-use phases. In cases of faults occurring in customer’s use, warranty leads to product recalls which are typically associated with high costs. To avoid software faults efficiently, quality management and enhanced development processes have to be realized by the introduction of specific analysis methods and Key Process/Performance Indicators (KPIs) to enable objective quality evaluations as soon as possible during product development process. The paper introduces an application of specific analysis methods by using KPIs and discusses their potential for automotive software quality improvement. Target is to support quality evaluation and risk-analysis for the release process of automotive software.

Precise three-dimensional dummy head trajectories during crash tests are very important for vehicle safety development. To determine precise trajectories with a standard deviation of approximately 5 millimeters, three-dimensional video analysis is an approved method. Therefore the tracked body is to be seen on at least two cameras during the whole crash term, which is often not given (e.g. head dips into the airbag). This non-continuity problem of video analysis is surmounted by numerical integration of differential un-interrupted electrical rotation and acceleration sensor signals mounted into the tracked body. Problems of this approach are unknown sensor calibration errors and unknown initial conditions, which result in trajectory deviations above 10 centimeters.

Although the ISO 26262 provides requirements and recommendations for an automotive functional safety lifecycle, practical guidance on how to handle these safety activities and safety artifacts is still lacking. This paper provides an overview of a semi-formal safety engineering approach based on SysML for specifying the relevant safety artifacts in the concept phase. Using specific diagram types, different views of the available data can be provided that reflects the specific needs of the stakeholders involved. One objective of this work is to improve the common understanding of the relevant safety aspects during the system design. The approach, which is demonstrated here from the perspective of a Tier1 supplier for an automotive battery system, covers different breakdown levels of a vehicle. The safety workflow presented here supports engineers' efforts to meet the safety standard ISO 26262 in a systematic way.