Search Results

The courier express parcel service industry (CEP industry) has experienced significant changes in the recent years due to increasing parcel volume. At the same time, the electrification of the vehicle fleets poses additional challenges. A major advantage of battery electric CEP vehicles compared to internal combustion engine vehicles is the ability to regenerate the kinetic energy of the vehicle in the frequent deceleration phases during parcel delivery. If the battery is cold the maximum recuperation power of the powertrain is limited by a reduced chemical reaction rate inside the battery. In general, the maximum charging power of the battery depends on the state of charge and the battery temperature. Due to the low power demand for driving during CEP operation, the battery self-heating is comparably low under cold ambient conditions. Without active conditioning of the battery, potential regenerative energy is lost as a result of the cold battery.

Meta material has been known for many years and the physics are well known since decades. But the challenge has always been to put the know how into (mass) production. This was the reason why no meta material has found its way into the automotive industry so far. But now things have changed: meta material became Meta Design and is going into serial production in 2024. Meta Design is a tunable spring mass system with foam acting as the spring and heavy layer as the mass. Meta Design is characterized by cavities in the foam and concentrated masses of the heavy layer as functionalized mass pins. By tuning the size of the cavities and the weight of the mass pins the acoustic performance can be adjusted to the requirements of each individual car line. After preliminary simulations, flat samples were tested in the lab. The next step was launched: the production and testing of a handmade prototype part of a firewall insulation for a Mercedes-Benz A-Class.

In vehicle NVH development, vibroacoustic simulations with Finite Element (FE) models are a common technique. The computational costs for these calculations are steadily rising due to more detailed modelling and higher frequency ranges. At the same time, the need for multiple evaluations of the same model with different input parameters, e.g., for uncertainty quantification, optimization, or robustness investigations, is also increasing. Therefore, it is crucial to reduce the computational costs in these cases. A common technique is to use surrogate models that replace the computationally intensive FE model to perform repeated evaluations. Several different methods in this area are well established, but with the continuous advancements in the field of machine learning, interesting new methods like the Gaussian Process (GP) regression arises as a promising approach.

The automotive world is progressing fast towards autonomous vehicles making sensors one of the critical components. There is a requirement for constant exchange of information between the vehicle and its surrounding environment, which is assisted by sensors such as Camera, LiDAR, and RADAR. However, exposure to harsh environmental conditions such as rain, dirt, snow, and bird droppings can hamper the functioning of the sensors and in turn interrupt accurate vehicle maneuvers. Sensor-cleaning mechanisms are required to be tested under various weather conditions and vehicle operating situations. Besides wind tunnel tests, digitalizing this whole process becomes important to take decision on design changes in early vehicle development stage. This work presents a digital methodology to test the LiDAR cleaning system in the advent of mud clearing at different vehicle speeds. The cleaning mechanism consists of a telescopic nozzle placed above the LiDAR translating back and forth.

The rapidly growing amount of software in cars reshapes the automotive industry. The software has a significant influence on the production lines, due to the time required to flash it onto the vehicle and its capabilities to test vehicle functions during production. In this paper we identify the main pain points regarding software in the manufacturing process by performing a structured analysis on the experiences made at a major car manufacturer over last two years. Consequently, the paper analyses the possible approaches to address the challenges.

More and more applications (apps) are entering vehicles. Customers would like to have in-car apps in their infotainment system, which they already use regularly on their smartphones. Other apps with new functionalities also inspire vehicle customers, but only as long as the customer can utilize them. To ensure customer satisfaction, it is important that these apps work and that failures are found and corrected as quickly as possible. Therefore, in-car apps also implicate requirements for future vehicle diagnostics. This is because current vehicle diagnostic methods are not designed for handling dynamic software failures of apps. Consequently, new diagnostic methods are needed to support the diagnosis of in-car apps. Log data are a central building block in software systems for system health management or troubleshooting. However, there are different types of log data and log environment setups depending on the underlying system or software platform.

The automotive industry changes rapidly. New players, concepts, and technologies from the Information Technology (IT) domain enter the market and software receives a high priority. Inside the vehicle, the number of components, which consist mostly of software, are increasing and more and more software-based functions are offered. In addition, High Performance Computers (HPCs) are continuing to be integrated into vehicles. These aspects lead to several challenges with current vehicle diagnostics, but also enable new opportunities in that field. However, in the specific area of vehicle diagnostics, there exists only very limited literature that considers current challenges and new possibilities for future vehicle diagnostics. Some literature deals with the general automotive system design or shows results from about five years ago. The viewpoints of an Original Equipment Manufacturer (OEM) are not included there.

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.

The continuous encouragement of lightweight design in modern vehicles demands a reliable and efficient method to predict and ameliorate the interior acoustic comfort for passengers. Due to considerable psychological effects on stress and concentration, the low frequency contribution plays a vital rule regarding interior noise perception. Apart other contributors, low frequency noise can be induced by transient aerodynamic excitation and the related structural vibrations. Assessing this disturbance requires the reliable simulation of the complex multi-physical mechanisms involved, such as transient aerodynamics, structural dynamics and acoustics. The domain of structural dynamics is particularly sensitive regarding the modelling of attachments restraining the vibrational behaviour of incorporated membrane-like structures. In a later development stage, when prototypes are available, it is therefore desirable to replace or update purely numerical models with experimental data.

The electric range of plug-in hybrids as well as the installed electric power has steadily increased. With an electric power share of more than half of the overall system power, concepts of hybrid electric vehicles with at least two electric machines come into focus. Especially the concept of adding an individual electric axle to a state-of-the-art parallel hybrid, such as a P2-hybrid, is promising. However, the system complexity of a so-called P24-hybird increases significantly because the number of possible system states rises. This leads to an increased development and calibration effort for an online energy management. Especially a transfer from an optimized operating strategy to a rule-based energy management is challenging. Thus, a development framework for the calibration of an online energy management system (EMS) which is as fuel efficient as possible is needed.

Increasing combustion pressure, low viscosity oils, less oil supply and the increasing stress due to downsizing of internal combustion engines (ICE) lead to higher loads within the bearing. As the mechanical and tribological loads on the piston pin bearings have a direct impact on the service life and function of the overall engine system, it is necessary to develop a robust tribological design approach. Regarding the piston pin bearing of a diesel engine, this study aims to describe the effects of different parameters on a DLC-coated piston pin within the bearing. Therefore, an external engine part test rig, which applies various forces to the connecting rod and measures the torque on a driven pin, is used to carry out validation measurements. The special feature of the test bench is the way the piston is beared. For the first experiments, the piston crown is placed against a plate (plate-bearing); later, this plate-bearing is replaced by a hydrostatic bearing.

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.

Sunroof buffeting is examined experimentally and numerically in this paper. Despite the fact that some consider the simulation process for sunroof buffeting to be mature, there remain substantial uncertainties even in recently published methodologies. Capturing the frequencies and especially the sound pressure levels correctly is essential if CFD simulations are intended to be used during early stages of a car development process. Numerous experimental results of sunroof buffeting and the interior low-frequency characteristics of a 2013 Mercedes-Benz S-Class have been used to develop a simplified car model: a full-size S-Class model with slightly simplified geometries in the interior as well as at the exterior. To avoid the effects of numerous different materials in the interior, it is solely made from polyurethane and aluminum and built to maximize its structural rigidity and air-tightness.

In this paper, the differences between a production car of the 2018 A-class and an early stage vehicle model with a mostly similar outer skin are examined experimentally and numerically. The aerodynamic development of vehicles at Mercedes-Benz is divided into several phases. When comparing force coefficients differences can be observed between these distinct hardware stages as well as when comparing steady state simulations to wind tunnel measurements. In early phases when prototype vehicles are not yet available, so-called aero foam models are used. These are well-defined full-sized vehicle models, as the outer skin is milled from Polyurethane. Important aerodynamic characteristics such as a motor compartment with a cooling module, deflecting axles with rotatable wheels and underbody covers are represented.

During the last decades, big steps have been taken towards a realistic simulation of NVH (Noise Vibration Harshness) behavior of vehicles using the Finite Element (FE) method. The quality of these computation models has been substantially increased and the accessible frequency range has been widened. Nevertheless, to perform a reliable prediction of the vehicle vibroacoustic behavior, the consideration of uncertainties is crucial. With this approach there are many challenges on the way to valid and useful simulation models and they can be divided into three areas: the input uncertainties, the propagation of uncertainties through the FE model and finally the statistical output quantities. Each of them must be investigated to choose sufficient methods for a valid and fast prediction of vehicle body vibroacoustics. It can be shown by rough estimation that dimensionality of the corresponding random space for different types of uncertainty is tremendously high.

Computational models directly derived from data gained increased interest in recent years. Data-driven approaches have brought breakthroughs in different research areas such as image-, video- and audio-processing. Often denoted as Machine Learning (ML), today these approaches are not widely applied in the field of vehicle Noise, Vibration and Harshness (NVH). Works combining ML and NVH mainly discuss the topic with respect to psychoacoustics, traffic noise, structural health monitoring and as improvement to existing numerical simulation methods. Vehicle interior noise is a major quality criterion for today’s automotive customers. To estimate noise levels early in the development process, deterministic system descriptions are created by utilizing time-consuming measurement techniques. This paper examines whether pattern-recognizing algorithms are suitable to conduct the prediction process for a steering system.

This paper describes a novel approach for modeling an automotive HVAC unit. The model consists of black-box models trained with experimental data from a self-developed measurement setup. It is capable of predicting the temperature and mass flow of the air entering the vehicle cabin at the various air vents. A combination of temperature and velocity sensors is the basis of the measurement setup. A measurement fault analysis is conducted to validate the accuracy of the measurement system. As the data collection is done under fluctuating ambient conditions, a review of the impact of various ambient conditions on the HVAC unit is performed. Correction models that account for the different ambient conditions incorporate these results. Numerous types of black-box models are compared to identify the best-suited type for this approach. Moreover, the accuracy of the model is validated using test drive data.

Autonomous driving systems and connected mobility are the next big developments for the car manufacturers and their suppliers during the next decade. To achieve the high computing power needs and fulfill new upcoming requirements due to functional safety and security, heterogeneous processor architectures with a mixture of different core architectures and hardware accelerators are necessary. To tackle this new type of hardware complexity and nevertheless stay within monetary constraints, high performance computers, inspired by state of the art data center hardware, could be adapted in order to fulfill automotive quality requirements. The European Processor Initiative (EPI) research project tries to come along with that challenge for next generation semiconductors. To be as close as possible to series development needs for the next upcoming car generations, we present a hybrid semiconductor system-on-chip architecture for automotive.

In this paper a method for the identification of most significant vehicle parameters influencing the behavior of a lateral control system of autonomous car is presented. Requirements for the design stage of the controller need to consider many uncertainties in the plant. While most vehicle properties can be compensated by an appropriate tuning of the control parameters, other vehicle properties can change significantly during usage. The control system is evaluated based on performance measures. Analyzed parameters comprise functional tire characteristics, mass of the vehicle and position of its center of gravity. Since the parameters are correlated, but Sobol’ sensitivity analysis assumes decorrelated inputs, random variation yields no reasonable results. Furthermore, the variation of each parameter or set of parameters is not applicable since the numbers of required simulations is increased significantly according to input dimension.

Sensing and acting elements to guarantee the locking functions of seat belt retractors can emit noise when the retractor is subjected to externally applied vibrations. For these elements to function correctly, stiffness, inertia and friction needs to be in tune, leading to a complex motion resistance behavior, which makes it delicate to test for vibration induced noise. Requirements for a noise test are simplicity, robustness, repeatability, and independence of laboratory and test equipment. This paper reports on joint development activities for an alternative test procedure, involving three test laboratories with different equipment. In vehicle observation on parcel shelf mounted retractors, commercially available test equipment, and recent results from multi-axial component tests [1], set the frame for this work. Robustness and reliability of test results is being analyzed by means of sensitivity studies on several test parameters.