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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.

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.

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).

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.

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.

Vehicle thermal management (VTM) simulations are becoming increasingly important in the development phase of a vehicle. These simulations help in predicting the thermal profiles of critical components over a drive cycle. They are usually done using two methodologies: (1) Solving every aspect of the heat transfer, i.e., convection, radiation, and conduction, in a single solver (Conjugate Heat Transfer) or (2) Simulating convection using a fluid solver and computing the other two mechanisms using a separate thermal solver (Co-simulation). The first method is usually computationally intensive, while the second one isn’t. This is because Co-simulation reduces the load of simulating all heat transfer mechanisms in a single code. This is one of the reasons why the Co-simulation method is widely used in the automotive industry. Traditionally, the methods developed for Co-simulation processes are load case specific.

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.

Air conditioning acoustics have become of paramount importance in electric vehicles, where noise from electromechanical components is no longer masked by the presence of the internal combustion engine. In a car HVAC systems, the coolant compressor is one of the most important sources in terms of vibration and noise generation. The paper, the generated structural noise is studied in detail on a prototype installation, and the noise transmission and propagation mechanisms are analyzed and discussed. Through ”in situ” measurements and virtual point transformation, the rotor unbalance forces and torque acting within the component are identified. The dynamic properties of the rubber mounts, installed between the compressor and its support, are identified thanks to matrix inversion methods. To assess the quality of the proposed procedure, the synthesized sound pressure level is compared with experimental SPL measurements in different operational conditions.

Synthetic fuels for internal combustion engines offer CO2-neutral mobility if produced in a closed carbon cycle using renewable energies. C1-based synthetic fuels can offer high knock resistance as well as soot free combustion due to their molecular structure containing oxygen and no direct C-C bonds. Such fuels as, for example, dimethyl carbonate (DMC) and methyl formate (MeFo) have great potential to replace gasoline in spark-ignition (SI) engines. In this study, a mixture of 65% DMC and 35% MeFo (C65F35) was used in a single-cylinder research engine to determine friction losses in the piston group using the floating-liner method. The results were benchmarked against gasoline (G100). Compared to gasoline, the density of C65F35 is almost 40% higher, but its mass-based lower heating value (LHV) is 2.8 times lower. Hence, more fuel must be injected to reach the same engine load as in a conventional gasoline engine, leading to an increased cooling effect.

To minimize the impact of global warming worldwide, net greenhouse-gas (GHG) emissions have to be reduced. The transportation sector is one main contributor to overall greenhouse gas emissions due to the fact that most of the current propulsion systems rely on fossil fuels. The gasoline engine powertrain is the most used system for passenger vehicles in the EU and worldwide. Besides emitting GHG, gasoline driven cars emit harmful pollutants, which can cause health issues for humans. Hybrid powertrains provide an available short-term solution to reduce fuel consumption and thus overall emissions. Therefore, an overview of the currently available technology and methodology of hybrid cars is provided in this paper as well as an overview of the performance of current HEV cars in real world testing. From the testing, it can be concluded that despite reducing harmful emissions, hybrid vehicles still emit pollutants and GHG when fueled with conventional gasoline.

It is particularly easy to get tunnel vision as a domain expert, and focus only on the improvements one could provide in their area of expertise. To make matters worse, many Original Equipment Manufacturers (OEMs) are silo-ed by domain of expertise, unconsciously promoting this single mindedness in design. Unfortunately, the successful and profitable development of a vehicle is dependent on the delicate balance of performance across many domains, involving multiple physics and departments. Taking for instance the design of a Heating, Ventilation & Air Conditioning (HVAC) system, the device’s primary function is to control the climate system in vehicle cabins, and more importantly to make sure that critical areas on the windshield can be defrosted in cold weather conditions within regulation time. With the advent of electric and autonomous vehicles, further importance is now also placed on the energy efficiency of the HVAC, and its noise.

In the near future, pollutant and GHG emission regulations in the transport sector will become increasingly stringent. For this reason, there are many studies in the field of internal combustion research that investigate alternative fuels, one example being oxygenated fuels. Additionally, the design of engine components needs to be optimized to improve the thresholds of clean combustion and thus reduce particulates. Simulations based on PRiME 3D® for dynamic behaviors inside the piston ring group provide a guideline for experimental investigation. Gas flows into the combustion chamber are controlled by adjusting the piston ring design. A direct comparison of regular and synthetic fuels enables to separate the emissions caused by oil and fuel. This study employed a mixture of dimethyl carbonate (DMC) and methyl formate (MeFo).

In this study, a fully optically accessible single-cylinder research engine is the basis for the visualization and generation of extensive knowledge about the in-cylinder processes of mixture formation, ignition and combustion of oxygenated synthetic fuels. Previous measurements in an all-metal engine showed promising results by using a mixture of dimethyl carbonate and methyl formate as a fuel substitute in a DISI-engine. Lower THC and NOx emissions were observed along with a low PN-value, implying low-soot combustion. The flame luminosity transmitted via an optical piston was split in the optical path to simultaneously record the natural flame luminosity with an RGB high-speed camera. The second channel consisted of OH*-chemiluminescence recording, isolated by a bandpass filter via an intensified monochrome high-speed camera.

A reliable environment perception is a requirement for safe automated driving. For evaluating and demonstrating the reliability of the vehicle’s environment perception, field tests offer testing conditions that come closest to the vehicle’s driving environment. However, establishing a reference ground truth in field tests is time-consuming. This motivates the development of a procedure for learning the vehicle’s perception reliability from fleet data without the need for a ground truth, which would allow learning the perception reliability from fleet data. In Berk et al. (2019), a method based on Bayesian inference to determine the perception reliability of individual sensors without the need for a ground truth was proposed. The model utilizes the redundancy of sensors to learn the sensor’s perception reliability. The method was tested with simulated data.

One way of increasing the efficiency of a gasoline engine is to operate it in lean-burn mode. However, a lean mixture in the combustion chamber reduces its ignitability, which leads to poor combustion stability and even misfires. This investigation presents a solution to this problem using an active pre-chamber for each cylinder, into which fuel can be injected separately and in which ignition takes place. This increases the ignition energy in the main combustion chamber, thus enabling stable combustion. Cylinder-specific feedback control of the fuel quantity injected into the pre-chambers was implemented on the basis of measured cylinder pressures so as to compensate for injector component deviations, achieve maximum efficiency, and prevent increased emissions. Since combustion delay and burn duration are dependent on the fuel mass injected into the pre-chamber, an additional feedback control for the center of combustion (MFB50) was integrated along with the fuel quantity controller.

Beside the main trend technologies such as downsizing, down speeding, external exhaust gas recirculation, and turbocharging in combination with Miller cycles, the optimization of the mechanical efficiency of gasoline engines is an important task in meeting future CO2 emission targets. Friction in the piston assembly is responsible for up to 45% of the total mechanical loss in a gasoline engine. Therefore, optimizing piston assembly friction is a valuable approach in improving the total efficiency of an internal combustion engine. The form honing process enables new specific shapes of the cylinder liner surface. These shapes, such as a conus or bottle neck, help enlarge the operating clearance between the piston assembly and the cylinder liner, which is one of the main factors influencing piston assembly friction.

Current legal requirements based on new driving cycles like WLTP or RDE focus on elevated power and torque from the engine. The gear ratios are chosen so as to permit low engine speeds to reduce fuel consumption and consequently CO2 emissions by shifting the operating point to higher loads with reduced throttling and friction losses at low engine speeds. To achieve the required acceleration values the engine tends to be operated more frequently close to its power and torque limits. Thus, the knock occurring at the load limits will increase in significance. Today, in series production, knock is detected via structure-borne sound sensors and eliminated via retarded ignition. New low-cost in-cylinder pressure sensors (ICPS) suitable for series-production now permit evaluation of every single combustion cycle, thus detecting knock in the engine control unit (ECU) at all speed and load ratios independent of parasitic noise.

Laser induced fluorescence (LIF) is used to investigate oil transport mechanisms under real engine conditions. The engine oil is mixed with a dye that can be induced by a laser. The emitted light intensity from the dye correlates with the residual oil at the sensor position and the resulting oil film thicknesses can be precisely determined for each crank angle. However, the general expectation is not always achieved, e.g. an exact representation of piston ring barrel shapes. In order to investigate the responsible lubrication effects of this behavior, a new cavitation algorithm for the Reynolds equation has been developed. The solution retains the mass conservation and does not use any switch function in its mathematical approach. In contrast to common approaches, no vapor-liquid ratio is used, but one or several bigger bubbles are approximated, as have been observed in other experiments already.