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Due to manifold benefits compared to proprietary software solutions, free and open source software (FOSS) in general, and Linux especially becomes more and more relevant for embedded solutions in the automotive domain, especially in High Performance Computing Platforms (HPC). However, taking over liability and warranty for a FOSS software-based problem raises the problem of software quality assurance, and thus respectively risk control. In order to control and minimize the residual risk of a product or service, the traditional and well-accepted measure in the automotive domain is to assess the engineering processes and resulting work products via a process assessment model given by the ASPICE maturity model, as well as requirements from functional safety standards for safety related functions. The underlying process reference model of ASPICE assumes software development performed and controlled by an organization.

Homologation is an important process in vehicle development and aerodynamics a main data contributor. The process is heavily interconnected: Production planning defines the available assemblies. Construction defines their parts and features. Sales defines the assemblies offered in different markets, where Legislation defines the rules applicable to homologation. Control engineers define the behavior of active, aerodynamically relevant components. Wind tunnels are the main test tool for the homologation, accompanied by surface-area measurement systems. Mechanics support these test operations. The prototype management provides test vehicles, while parts come from various production and prototyping sources and are stored and commissioned by logistics. Several phases of this complex process share the same context: Production timelines for assemblies and parts for each chassis-engine package define which drag coefficients or drag coefficient contributions shall be determined.

Investigating human driver behavior enhances the acceptance of the autonomous driving and increases road safety in heterogeneous environments with human-operated and autonomous vehicles. The previously established driver fingerprint model, focuses on the classification of driving style based on CAN bus signals. However, driving styles are inherently complex and influenced by multiple factors, including changing driving environments and driver states. To comprehensively create a driver profile, an in-car measurement system based on the Driver-Driven vehicle-Driving environment (3D) framework is developed. The measurement system records emotional and physiological signals from the driver, including ECG signal and heart rate. A Raspberry Pi camera is utilized on the dashboard to capture the driver's facial expressions and a trained convolutional neural network (CNN) recognizes emotion. To conduct unobtrusive ECG measurements, an ECG sensor is integrated into the steering wheel.

As part of the safety validation of advanced driver assistance systems (ADAS) and automated driving (AD) functions, it is necessary to demonstrate that the frequency at which the system exhibits hazardous behavior (HB) in the field is below an acceptable threshold. This is typically tested by observation of the system behavior in a field operational test (FOT). For situations in which the system under test (SUT) actively intervenes in the dynamic driving behavior of the vehicle, it is assessed whether the SUT exhibits HB. Since the accepted threshold values are generally small, the amount of data required for this strategy is usually very large. This publication proposes an approach to reduce the amount of data required for the evaluation of emergency intervention systems with a state machine based intervention logic by including the time periods between intervention events in the validation process.

EU legislation provides for only local CO2 emission-free vehicles to be allowed in individual passenger transport by 2035. In addition, the directive provides for fuels from renewable sources, i.e. defossilised fuels. This development leads to three possible energy sources or forms of energy for use in individual transport. The first possibility is charging with electricity generated from renewable sources, the second possibility is hydrogen generated from renewable sources or blue production path. The third possibility is the use of renewable fuels, also called e-fuels. These fuels are produced from atmospheric CO2 and renewable hydrogen. Possible processes for this are, for example, methanol or Fischer-Tropsch synthesis. The production of these fuels is very energy-intensive and large amounts of renewable electricity are needed.

The optimization and further development of automated driving functions offers great potential to relieve the driver in various driving situations and increase road safety. Simulative testing in particular is an indispensable tool in this process, allowing conclusions to be drawn about the design of automated driving functions at a very early stage of development. In this context, the use of driving simulators provides support so that the driving functions of tomorrow can be experienced in a very safe and reproducible environment. The focus of the acceptance and optimization of automated driving functions is particularly on vehicle lateral control functions. As part of this paper, a test person study was carried out regarding manual vehicle lateral control on the dynamic vehicle road simulator at the Institute of Automotive Engineering.

The modern automotive industry is facing challenges of ever-increasing complexity in the electrified powertrain era. On-board diagnostic (OBD) systems must be thoroughly validated and calibrated through many iterations to function effectively and meet the regulation standards. Their development and design process are more complex when prototype hardware is not available and therefore virtual testing is a prominent solution, including Software-in-the-loop (SiL) and Hardware-in-the-loop (HIL) simulations. Virtual prototype testing relying on real-time simulation models is necessary to design and test new era’s OBD systems quickly and in scale. The new fuel cell powertrain involves new and preciously unexplored fail modes. To make the system robust, simulations are required to be carried out to identify different fails.

Traditional CACC systems utilize inter-vehicle wireless communication to maintain minimal yet safe inter-vehicle distances, thereby improving traffic efficiency. However, introducing communication delays generates system uncertainties that jeopardize string stability, a crucial requirement for robust CACC performance. To address these issues, we introduce a decentralized Model Predictive Control (MPC) approach that incorporates Kalman Filters and state predictors to counteract the uncertainties posed by noise and communication delays. We validate our approach through MATLAB Simulink simulations, using stochastic and mathematical models to capture vehicular dynamics, Wi-Fi communication errors, and sensor noises. In addition, we explore the application of a Reinforcement Learning (RL)-based algorithm to compare its merits and limitations against our decentralized MPC controller, considering factors like feasibility and reliability.

In contrast to refrigeration circuits in internal combustion engine vehicles (ICEVs) mainly used for cabin cooling, in electric vehicles (EVs) additional functions need to be taken into consideration, e.g., cabin heating, which in ICEVs is realized by the combustion engine’s waste heat, conditioning of the electric battery and drive train components. Additionally, each of these functions demands a different temperature level. Therefore, requirements towards the thermal management in EVs are more challenging. In modern EVs most of these functions are realized by direct refrigerant circuits, which are optimal in terms of efficiency and response time, however, result in greater complexity and different architectures for almost every vehicle model. In addition, the vast majority of EVs worldwide use chemical refrigerants that contain PFAS, e.g. R1234yf, which are known to be persistent and harmful for human health and environment.

Design verification and quality control of automotive components require the analysis of the source location of ultra-short sound events, for instance the engaging event of an electromechanical clutch or the clicking noise of the aluminium frame of a passenger car seat under vibration. State-of-the-art acoustic cameras allow for a frame rate of about 100 acoustic images per second. Considering that most of the sound events introduced above can be far less than 10ms, an acoustic image generated at this rate resembles an hard-to-interpret overlay of multiple sources on the structure under test along with reflections from the surrounding test environment. This contribution introduces a novel method for visualizing impulse-like sound emissions from automotive components at 10x the frame rate of traditional acoustic cameras.

Though modal analysis is a common tool to evaluate the dynamic properties of a structure, there are still many individual decisions to be made during the process which are often based on experience and make it difficult for occasional users to gain reliable and correct results. One of those experience-based choices is the correct number and placement of reference points. This decision is especially important, because it must be made right in the beginning of the process and a wrong choice is only noticeable in the very end of the process. Picking the wrong reference points could result in incomplete modal analysis outcomes, as it might make certain modes undetectable, compounded by the user's lack of awareness about these missing modes. In the paper an innovative approach will be presented to choose the minimal number of mandatory reference points and their placement.

Liebherr Machines Bulle SA designs and produces High-quality diesel engines, injection systems as well as hydraulic components. Liebherr has an Acoustic End of Line (A-EoL) system on serial test benches. All engines are measured, and noises are evaluated by operators. This subjective evaluation leads to dispersion on the evaluations, particularly for whining noise. To achieve Swiss quality requirements and ensure customer satisfaction, Liebherr wishes to define a new methodology to find a quantitative and objective criterion to set a robust engine noise compliance standard. This new methodology is based on near field microphone measurement of an engine run-down. First, whining noise signatures are extracted from the raw signal. Secondly, psychoacoustic indicators are calculated on the isolated signatures. Thresholds are then established to validate engine deliveries.

The engine acoustic character has always represented the product DNA, owing to its strong correlation with in-cylinder pressure gradient, components design and perceived quality. Best practice for engine acoustic characterization requires the employment of a hemi-anechoic chamber, a significant number of sensors and special acoustic insulation for engine ancillaries and transmission. This process is highly demanding in terms of cost and time due to multiple engine working points to be tested and consequent data post-processing. Since Neural Networks potentially predicting capabilities are apparently un-exploited in this research field, the following paper provides a tool able to acoustically estimate engine performance, processing system inputs (e.g. Injected Fuel, Rail Pressure) thanks to the employment of Multi Layer Perceptron (MLP, a feed forward Network working in stationary points).

Computer modelling, virtual prototyping and simulation is widely used in the automotive industry to optimize the development process. While the use of CAE is widespread, on its own it lacks the ability to provide observable acoustics or tactile vibrations for decision makers to assess, and hence optimize the customer experience. Subjective assessment using Driver-in-Loop simulators to experience data has been shown to improve the quality of vehicles and reduce development time and uncertainty. Efficient development processes require a seamless interface from detailed CAE simulation to subjective evaluations suitable for high level decision makers. In the context of perceived vehicle vibration, the need for a bridge between complex CAE data and realistic subjective evaluation of tactile response is most compelling. A suite of VI-grade noise and vibration simulators have been developed to meet this challenge.

Broadband active noise control algorithms require high-performance so multi-channel control to ensure high performance, which results in very high computational power and expensive DSP. When the control filter update part need a huge computational power of the algorithm is separated and calculated by the server, it is possible to reduce cost by using a low-cost DSP in a local vehicle, and a performance improvement algorithm requiring a high computational power can be applied to the server. In order to achieve the above goal, this study analyzed the maximum delay time when communication speed is low and studied response measures to ensure data integrity at the receiving location considering situations where communication speed delay and data errors occur.

Severe problem of aerodynamic heating and drag force are inherent with any hypersonic space vehicle like space shuttle, missiles etc. For proper design of vehicle, the drag force measurement become very crucial. Ground based test facilities are employed for these estimates along with any suitable force balance as well as sensors. There are many sensors (Accelerometer, Strain gauge and Piezofilm) reported in the literature that is used for evaluating the actual aerodynamic forces over test model in high speed flow. As per previous study, the piezofilm also become an alternative sensor over the strain gauges due to its simple instrumentation. For current investigation, the piezofilm and strain gauge sensors have mounted on same stress force balance to evaluate the response time as well as accuracy of predicted force at the same instant. However, these force balance need to be calibrated for inverse prediction of the force from recorded responses.

Gaganyaan programme is India's prestigious human space exploration endeavour. During the re-entry of the spacecraft, achieving the minimum terminal velocity is paramount to ensure the crew's safety upon landing. Therefore, acquiring accurate in-flight velocity data is essential for comprehensively understanding the landing dynamics and facilitating post-flight data analysis and validation. Moreover, terminal velocity plays a pivotal role in the qualification of parachute systems during platform-drop tests where the objective is to minimize the terminal velocity for safe impact. Terminal velocity also serves as a critical design parameter for the crew seat attenuation system. In addition to terminal velocity, it is equally necessary to characterize the horizontal velocities acting on the decelerating body due to various factors such as parachute sway and wind drift. This data also plays a central role in refining our systems for future enhancements.

The purpose of the Air Generation System is to provide a constant supply of conditioned fresh air to meet the necessary oxygen availability and to prevent carbon dioxide (CO2) concentrations for the occupants in an aircraft. The engine bleed energy or electrical load energy consumed towards this circumstance accounts to be approx. 5% of total fuel burn and in turn, contributes to the global emissions of greenhouse gases. This paper studies the improvement areas of the present conventional system such as fuel burn consumption associated with an aircraft environmental control system (ECS) depending on, the amount of bleed and ram air usage, electric power consumption. Improved systems for propulsion, power generation, sustainability, hybridization, and environmental control can be desirable for an aircraft.

Selective Laser Melting (SLM) has gained widespread usage in aviation, aerospace, and die manufacturing due to its exceptional capacity for producing intricate metal components of highly complex geometries. Nevertheless, the instability inherent in the SLM process frequently results in irregularities in the quality of the fabricated components. As a result, this hinders the continuous progress and wider acceptance of SLM technology. Addressing these challenges, in-process quality control strategies during SLM operations have emerged as effective remedies for mitigating the quality inconsistencies found in the final components. This study focuses on utilizing optical emission spectroscopy and IR thermography to continuously monitor and analyze the SLM process within the powder bed, with the aim of strengthening process control and minimizing defects.

Uncrewed Aerial vehicles are useful for a multitude of applications in today’s age, covering a wide variety of fields such as defense, environmental science, meteorology, emergency responders, search and rescue operations, entertainment robotics, etc. Different types of aircrafts such as fixed wing UAVs, rotor wing UAVs are used for the mentioned applications depending upon the application requirements. One such category of UAVs is the lighter-than-air aircrafts, that provide their own set of advantages over the other types of UAVs. Blimps are among the participants of the lighter-than-air category that are expected to offer advantages such as higher endurance and range, and safer and more comfortable Human-machine-Interaction, etc. as compared to fixed wing and rotor wing UAVs due to their design. A ROS (Robot Operating System) based control system was developed for controlling the blimp.