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Squeak and rattle (SAR) noise audible inside a passenger car causes the product quality perceived by the customer to deteriorate. The consequences are high warranty costs and a loss in brand reputation for the vehicle manufacturer in the long run. Therefore, SAR noise must be prevented. This research shows the application and experimental validation of a novel method to predict SAR noise on an actual vehicle interior component. The novel method is based on non-linear theories in the frequency domain. It uses the harmonic balance method in combination with the alternating frequency/time domain method to solve the governing dynamic equations. The simulation approach is part of a process for SAR noise prediction in vehicle interior development presented herein. In the first step, a state-of-the-art linear frequency-domain simulation estimates an empirical risk index for SAR noise emission. Critical spots prone to SAR noise generation are located and ranked.

Finite element simulation (FE) makes it possible to analyze the structural dynamic behavior of vehicle seat structures in early design phases to meet Noise-Vibration-Harshness (NVH) requirements. For this purpose, linear simulations are usually used, which neglect many nonlinear mechanical properties of the real structure. These models are trimmed to fit global vibration behavior based on the complex description of contact or jointed definitions. Targeted design is therefore only possible to a limited extent. The aim of this work is to characterize the entire seat structure and its sub-components in order to identify the main contributors using experimental and simulative data. The Lagrange Multiplier Frequency Based Substructuring (LM-FBS) method is used for this purpose. Therefore, the individual subsystems of seat frame, seat backrest and headrest are characterized under different conditions.

Electrified vehicles are particularly quiet, especially at low speeds due to the absence of combustion noises. This is why there are laws worldwide for artificial driving sounds to warn pedestrians. These sounds are generated using a so-called Acoustic Vehicle Alerting System (AVAS) which must maintain certain minimum sound pressure levels in specific frequency ranges at low speeds. The creation of the sound currently involves an iterative and sometimes time-consuming process that combines composing the sound on a computer with measuring the levels with a car on an outside noise test track. This continues until both the legal requirements and the subjective demands of vehicle manufacturers are met. To optimize this process and reduce the measurement effort on the outside noise test track, the goal is to replace the measurement with a simulation for a significant portion of the development.

Trim materials are often used for vibroacoustic energy absorption purposes within vehicles. To estimate the sound impact at a driver’s ear, the substructuring approach can be applied. Thus, transfer functions are calculated starting from the acoustic source to the car body, from the car body to the trim and, finally, from the trim to the inner cavity where the driver is located. One of the most challenging parts is the calculation of the transfer functions from the car body inner surface to the bottom trim surface. Commonly, freely laying mass-spring systems (trims) are simulated with a fixed boundary and interface phenomena such as friction, stick-slip or discontinuities are not taken into consideration. Such an approach allows for faster simulations but results in simulations strongly overestimating the energy transfer, particularly in the frequency range where the mass-spring system’s resonances take place.

The introduction of autonomous vehicles has gained significant attention due to its potential to revolutionize mobility and safety. A critical aspect underpinning the functionality of these autonomous vehicles is their sensor perception system. Demonstrating the reliability of the environment perception sensors and sensor fusion algorithms is, therefore, a necessary step in the development of automated vehicles. Field tests offer testing conditions that come closest to the environment of an automated vehicle in the future. However, a significant challenge in field tests is to obtain a reference truth of the surrounding environment. Here, we propose a pipeline to assess the sensor reliabilities without the need for a reference truth. The pipeline uses a model to estimate the reliability of redundant sensors. To do this, it relies on a binary representation of the surrounding area, which indicates either the presence or absence of an object.

The objective of this study was to reduce pollutant emissions during cold start conditions in a spark-ignited direct injection engine, by exploring the potential of oxygenated fuels. With their high oxygen content and lack of direct C-C bonds, they effectively reduce particle number (PN) and NOx emissions under normal conditions. Methanol was chosen due to its wide availability. As methanol is toxic to humans and associated with cold-start issues, a second promising synthetic fuel was selected to be benchmarked against gasoline, comprising 65 vol% of dimethyl carbonate and 35 vol% of methyl formate (C65F5). Currently, there is a lack of detailed investigations on the cold start performance for both oxygenated fuels utilizing today’s injector capabilities. Spray measurements were caried out in a constant volume chamber to assess the spray of C65F35. Reduced fuel temperature increased spray-penetration length and compromised fast vaporization.

Photochromism is a reversible color change phenomenon based on chemical reactions caused by light illumination. In the present study, this technique is applied to visualize the lubricating oil and fuel around the piston rings in the gasoline engine. The oil film was colored with a UV laser and photographed by synchronizing the shutter of a high-speed camera with a flashlight. The color density was evaluated as a value of absorbance, calculated from images taken at two different wavelengths and two different times before and after the coloration. The authors performed photochromism visualization experiments in an engine under motored operation. However, using photochromic dyes that are robust to temperature changes makes it possible to visualize the engine under fired operation. The experiment was conducted mainly by switching to the motored operation for a fixed time between the fired operations.

Bicycle-drawn cargo trailers with an electric drive to enable the transportation of high cargo loads are used as part of the last-mile logistics. Depending on the load, the total mass of a trailer can vary between approx. 50 and 250 kg, potentially more than the mass of the towing bicycle. This can result in major changes in acceleration and braking behavior of the overall system. While existing systems are designed primarily to provide sufficient power, improvements are needed in the powertrain control system in terms of driver safety and comfort. Hence, we propose a novel prototype that allows measurement of the tensile force in the drawbar which can subsequently be used to design a superior control system. In this context, a sinusoidal force input from the cyclist to the trailer according to the cadence of the cyclist is observed. The novelty of this research is to analyze whether torque impulses of the cyclist can be reduced with the help of Model Predictive Control (MPC).

A novel algorithm-based approach is employed in this publication to calculate multiple direct injection patterns for spark ignition engines. The algorithm is verified by investigating the combustion and emission behavior of a single-cylinder research engine. State-of-the-art standard exhaust gas analyzers, a particle counter and an additional FTIR analyzer enable in-depth investigation of engine exhaust gas composition. With the upcoming worldwide pollutant emission targets, the emission limits will be reduced while the test procedures’ requirements to the engine increase. Special attention to the engine-out emissions must be paid during cold-start, during which the aftertreatment system lacks sufficient pollutant emission conversion efficiency. With advanced injection control, the engine-out emissions can be reduced and exhaust aftertreatment heat-up can be accelerated.

The thermal operational safety (TOS) of a vehicle ensures that no component exceeds its critical temperature during vehicle operation. To enhance the current TOS validation process, a data-driven approach is proposed to predict maximum component temperatures of a new vehicle project by leveraging the historical thermal wind tunnel data from previous vehicle projects. The approach intends to support engineers with temperature predictions in the early phase and reduce the number of wind tunnel tests in the late phase of the TOS validation process. In the early phase, all measurements of the new vehicle project are predicted. In the late phase, a percentage of measurements with the test vehicle used for the model training and the remaining tests are predicted with the trained ML model. In a first step, data from all wind tunnel tests is extracted into a joint dataset together with metadata about the vehicle and the executed load case.

Predicting the aerodynamic performance of an aircraft in icing conditions is critical as failures in an aircraft’s ice protection system can compromise flight safety. Aerodynamic effects of icing have typically relied on RANS modeling, which usually struggles to predict stall behavior, including those induced by surface roughness. Encouraged by recent studies using LES that demonstrate the ability to predict stall characteristics on full aircraft with smooth wings at an affordable cost [1], this study seeks to apply this methodology to icing conditions. Measurements of lift, drag, and pitching moments of a NACA23012 airfoil under clean and iced conditions are collected at Re = 1.8M. Using laser scanned, detailed representations of the icing geometries, LES calculations are conducted to compare integrated loads against experimental measurements in both clean and iced conditions at various angles of attack through the onset of stall [2].

Modeling of icing is important for the design of aircraft lifting surfaces and for the design of efficient propulsion systems. The computational modeling of ice accretion prediction is important to replace the expensive experimental techniques for calculating the ice shapes in Icing tunnels, and the first step toward modeling ice accretion is to accurately compute the droplet collection efficiency which acts as the input to the accretion model. In this work, we perform large-eddy simulations of supercooled droplet transport and impingement onto complex aircraft geometries using a Lagrangian particle approach. We assess the improvement in modeling droplet impingement by computing the droplet collection efficiency and by comparing with the existing experimental data.

This paper provides an overview of possible engine design optimizations by utilizing highly knock-resistant potential greenhouse gas (GHG) neutral synthetic fuels. Historically the internal combustion engine was tailored to and highly optimized for fossil fuels. For future engine generations one of the main objectives is to achieve GHG neutrality. This means that either carbon-free fuels such as hydrogen or potential greenhouse gas neutral fuels are utilized. The properties of hydrogen make its use challenging for mobile application as it is very diffusive, not liquid under standard temperature/pressure and has a low volumetric energy density. C1-based oxygenated fuels such as methanol (MeOH), dimethyl carbonate (DMC) and methyl formate (MeFo) have properties like conventional gasoline but offer various advantages. Firstly, these fuels can be produced with renewable energy and carbon capture technologies to be GHG neutral.

In motorsports, aerodynamic development processes target to achieve gains in performance. This requires a comprehensive understanding of the prevailing aerodynamics and the capability of analysing large quantities of numerical data. However, manual analysis of a significant amount of Computational Fluid Dynamics (CFD) data is time consuming and complex. The motivation is to optimize the aerodynamic analysis workflow with the use of deep learning architectures. In this research, variants of 3D deep learning models (3D-DL) such as Convolutional Autoencoder (CAE) and U-Net frameworks are applied to flow fields obtained from Reynolds Averaged Navier Stokes (RANS) simulations to transform the high-dimensional CFD domain into a low-dimensional embedding. Consequently, model order reduction enables the identification of inherent flow structures represented by the latent space of the models.

Lithium-ion batteries (LIBs) repurposed from retired electric vehicles (EVs) for grid-scale energy storage systems (ESSs) have the potential to contribute to a sustainable, low-carbon-emissions energy future. The economic and technological value of these “second-life” LIB ESSs must be evaluated based on their operation on the electric grid, which determines their aging trajectories. The battery research community needs experimental data to understand the operation of these batteries using laboratory experiments, yet there is a lack of work on experimental evaluation of second-life batteries. Previous studies in the literature use overly-simplistic duty cycling in order to age second-life batteries, which may not produce aging trajectories that are representative of grid-scale ESS operation. This mismatch may lead to inaccurate valuation of retired EV LIBs as a grid resource.

Software complexity of vehicles is constantly growing especially with additional autonomous driving features being introduced. This increases the risk for bugs in the system, when the car is delivered. According to a car manufacturer, more than 90% of availability problems corresponding to Electronic Control Unit (ECU) functionality are either caused by software bugs or they can be resolved by applying software updates to overcome hardware issues. The main concern are sporadic errors which are not caught during the development phase since their trigger condition is too unlikely to occur or is not covered by the tests. For such systems, there is a need of safe and secure infield diagnosis. In this paper we present a tool software architecture with remote access, which facilitates standard read/write access, an efficient channel interface for communication and file I/O, and continuous trace.

Future emission regulations require further development for internal combustion engines operating on gasoline. To comply with such regulations and simultaneously improve fuel efficiency, major development trends are found in reduced displacements, increased compression ratios and turbocharging. To counteract such engines’ increased tendencies to knocking combustion, direct fuel injection systems are necessarily applied. Compared to standard port fuel injection, direct injection systems cause increased particle emissions. State-of-the-art magnet-driven gasoline direct injectors are capable of realizing various injection events of small injected mass per event and short dwell time between one another. Thereby, they facilitate multiple injection strategies, able to overcome the drawbacks of direct injection systems in relation to exhaust emissions. However, the full potential of multiple injection strategies is not yet taken advantage of.

Deep neural network models have been widely used for environment perception of intelligent vehicles. However, due to models’ innate probabilistic property, the lack of transparency, and sensitivity to data, perception results have inevitable uncertainties. To compensate for the weakness of probabilistic models, many pieces of research have been proposed to analyze and quantify such uncertainties. For safety-critical intelligent vehicles, the uncertainty analysis of data and models for environment perception is especially important. Uncertainty estimation can be a way to quantify the risk of environment perception. In this regard, it is essential to deliver a comprehensive survey. This work presents a comprehensive overview of uncertainty estimation in deep neural networks for environment perception of intelligent vehicles.

The NVH-development cycle of vehicle components often requires a source characterization separated from the vehicle itself, which leads to the implementation of test bench setups. In the context of frequency based substructuring and transfer path analysis, a component can be characterized using Blocked Forces. The following paper provides a comparison of methods between an acceleration-based in-situ and a new hybrid in-situ Blocked Force determination, using measurements of an artificially excited electric power steering (EPS). Under real-life conditions on a test rig, the acceleration-based in-situ approach often shows limitations in the lower frequency range, due to relatively bad signal-to-noise ratio at the indicator sensors, while delivering accurate results in the higher spectrum. Due to considerable loads on components in operation, the stiffness of the test-rig cannot be decreased arbitrarily.

In order to predict reality as accurately as possible leads to the fact that numerical models in automotive vibroacoustic problems become increasingly high dimensional. This makes applications with a large number of model evaluations, e.g. optimization tasks or uncertainty quantification hard to solve, as they become computationally very expensive. Engineers are thus faced with the challenge of making decisions based on a limited number of model evaluations, which increases the need for data-efficient methods and reduced order models. In this contribution, variational autoencoders (VAEs) are used to reduce the dimensionality of the vibroacoustic model of a vehicle body and to find a low-dimensional latent representation of the system.