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Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains.

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.

The reduction of vehicle exhaust particle emissions is a success story of European legislation. Various particle number (PN) counters and calibration procedures serve as tools to enforce PN emission limits during vehicle type approval (VTA) or periodical technical inspection (PTI) of in-use vehicles. Although all devices and procedures apply to the same PN-metric, they were developed for different purposes, by different stakeholder groups and for different target costs and technical scopes. Furthermore, their calibration procedures were independently defined by different stakeholder communities. This frequently leads to comparability and interpretation issues. Systematic differences of stationary and mobile PN counters (PN-PEMS) are well-documented. New, low-cost PTI PN counters will aggravate this problem. Today, tools to directly compare different instruments are scarce.

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.

As a consequence of reduced fuel consumption, direct injection gasoline engines have already prevailed against port fuel injection. However, in-cylinder fuel homogenization strongly depends on charge motion and injection strategies and can be challenging due to the reduced available time for mixture formation. An insufficient homogenization has generally a negative impact on the combustion and therefore also on efficiency and emissions. In order to reach the targets of the intensified CO2 emission reduction, further increase in efficiency of SI engines is essential. In this connection, 0D/1D simulation is a fundamental tool due to its application area in an early stage of development and its relatively low computational costs. Certainly, inhomogeneities are still not considered in quasi dimensional combustion models because the prediction of mixture formation is not included in the state of the art 0D/1D simulation.

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 the automotive industry, there is a continued need to improve the development process and handle the increasing complexity of the overall vehicle system. One major step in this process is a comprehensive and complementary approach to both simulation and testing. Knowledge of the overall dynamic vehicle behavior is becoming increasingly important for the development of new control concepts such as integrated vehicle dynamics control aiming to improve handling quality and ride comfort. However, with current well-established test systems, only separated and isolated aspects of vehicle dynamics can be evaluated. To address these challenges and further merge the link between simulation and testing, the Institute of Internal Combustion Engines and Automotive Engineering (IVK), University of Stuttgart is introducing a new Handling Roadway (HRW) Test System in cooperation with The Research Institute of Automotive Engineering and Vehicle Engines Stuttgart (FKFS) and MTS Systems Corporation.

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.

The development and qualification of distributed and highly safety-critical avionics systems implicate high efforts and risks. The resulting costs usually limit implementations like fly-by-wire systems to the military or commercial airliner domains. The aim of previous and ongoing research at the Institute of Aircraft Systems at University of Stuttgart is the reduction of these costs and therefore open up their benefits, inter alia, to general aviation, remotely piloted or unmanned aircraft. An approach for an efficient development is the application of a platform based development which supports the reuse of software and hardware components. The Flexible Platform adopts this approach. It is accompanied by a tool suite which automates the design and parameter instantiation, documentation generation and the generation of verification artifacts for a platform instance. This paper presents the approach for the requirement document generation compliant to ARP4754A and DO-178C.

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.

Over the past decades, interior noise from wind noise or engine noise have been significantly reduced by leveraging improvements of both the overall vehicle design and of sound package. Consequently, noise sources originating from HVAC systems (Heat Ventilation and Air Conditioning), fans or exhaust systems are becoming more relevant for perceived quality and passenger comfort. This study focuses on HVAC systems and discusses a Flow-Induced Noise Detection Contributions (FIND Contributions) numerical method enabling the identification of the flow-induced noise sources inside and around HVAC systems. This methodology is based on the post-processing of unsteady flow results obtained using Lattice Boltzmann based Method (LBM) Computational Fluid Dynamics (CFD) simulations combined with LBM-simulated Acoustic Transfer Functions (ATF) between the position of the sources inside the system and the passenger’s ears.

A simulation approach to predict the amount of snow which is penetrating into the air filter of the vehicle’s engine is important for the automotive industry. The objective of our work was to predict the snow ingress based on an Eulerian/Lagrangian approach within a commercial CFD-software and to compare the simulation results to measurements in order to confirm our simulation approach. An additional objective was to use the simulation approach to improve the air intake system of an automobile. The measurements were performed on two test sites. On the one hand we made measurements on a natural test area in Sweden to reproduce real driving scenarios and thereby confirm our simulation approach. On the other hand the simulation results of the improved air intake system were compared to measurements, which were carried out in a climatic wind tunnel in Stuttgart.

In recent years, driving simulators have become a valuable tool in the automotive design and testing process. Yet, in the field of vehicle dynamics, most decisions are still based on test drives in real cars. One reason for this situation can be found in the fact that many driving simulators do not allow the driver to evaluate the handling qualities of a simulated vehicle. In a driving simulator, the motion cueing algorithm tries to represent the vehicle motion within the constrained motion envelope of the motion platform. By nature, this process leads to so called false cues where the motion of the platform is not in phase or moving in a different direction with respect to the vehicle motion. In a driving simulator with classical filter-based motion cueing, false cues make it considerably more difficult for the driver to rate vehicle dynamics.

A local Gaussian process regression approach is presented, which allows to model nonlinearities of internal combustion engines more accurate than global Gaussian process regression. By building smaller models, the prediction of local system behavior improves significantly. In order to predict a value, the algorithm chooses the nearest training points. The number of chosen training points depends on the intensity of estimated nonlinearity. After determining the training points, a model is built, the prediction performed and the model discarded. The approach is demonstrated with a benchmark system and air charge test bed measurements. The measurements are taken from a turbocharged SI gasoline engine with both variable inlet valve lift and variable inlet and exhaust valve opening angle. The results show how local Gaussian process regression outmatches global Gaussian process regression concerning model quality and nonlinearities in particular.

Air charge calibration of turbocharged SI gasoline engines with both variable inlet valve lift and variable inlet and exhaust valve opening angle has to be very accurate and needs a high number of measurements. In particular, the modeling of the transition area from unthrottled, inlet valve controlled resp. throttled mode to turbocharged mode, suffers from small number of measurements (e.g. when applying Design of Experiments (DoE)). This is due to the strong impact of residual gas respectively scavenging dominating locally in this area. In this article, a virtual residual gas sensor in order to enable black-box-modeling of the air charge is presented. The sensor is a multilayer perceptron artificial neural network. Amongst others, the physically calculated air mass is used as training data for the artificial neural network.

A physically based model to predict the amount of snow which is entering the air intake of an automobile is extremely important for the automotive industry. It allows to improve the air intake system in the development state so that new vehicles can be developed in a shorter time. Using an Eulerian/Lagrangian approach within a commercial CFD-software we set up a model and calculated the snow ingress into an air intake of an automobile. In our numerical investigations we considered different particle shapes when calculating the drag coefficient, different coefficients of restitution and different particle sizes. Furthermore two-way coupling was considered. To obtain key parameters for the simulation, we measured the size of snow particles in the Daimler climatic wind tunnel in Sindelfingen by using a microscope and a measuring device from Malvern. Besides we used mechanical snow traps to determine the snow mass flux in the climatic wind tunnel and on a test area in Sweden.

This paper introduces an innovative approach, named synergetic 1D-3D-Coupling, by using synergy effects of 1D and 3D simulation in order to bring down modeling and simulation efforts. At the same time the methodology sustains the spatial resolution of a 3D model. This goal is reached by reducing the 3D fluid side with its time consuming continuity, momentum, energy and turbulence equations to a simple but precise 1D model. Because of the solid structure staying three dimensional, heat flux direction and spatial resolution have 3D accuracy but short calculation times due to the simple heat diffusion equation to be solved. The 1D model is represented by an automatically generated equation system which is capable of considering transient effects. The energy transfer between 1D fluid model and 3D structure model is realized through a neutral 1D-3D-coupling program and the application of the fluid element specific Nusselt correlations.

Timing evaluation methods help to design a robust and extendible E/E architecture (electric/electronic). BMW has introduced the systematic application of such methods in the E/E design process within the last three years. Meanwhile, most of the architectural changes are now verified by a tool-based, automatic real-time analysis. This has increased the accuracy of the network planning and productivity of the BMW network department. In this paper, we give an overview of the actual status of timing evaluations in BMW's E/E architecture design. We discuss acceptance criteria, analysis metrics, and design rules, as far as these are related to timing. We look specifically at automation options, as these improve the productivity further. We will see that timing analysis has matured and should be mandatory for application in mass production E/E architecture development. At the same time, there is room for future improvements.

Today's automotive software integration is a static process. Hardware and software form a fixed package and thus hinder the integration of new electric and electronic features once the specification has been completed. Usually software components assigned to an ECU cannot be easily transferred to other devices after they have been deployed. The main reasons are high system configuration and integration complexity, although shifting functions from one to another ECU is a feature which is generally supported by AUTOSAR. The concept of a Virtual Functional Bus allows a strict separation between applications and infrastructure and avoids source code modifications. But still further tooling is needed to reconfigure the AUTOSAR Basic Software (BSW). Other challenges for AUTOSAR are mixed integrity, versioning and multi-core support. The upcoming BMW E/E-domain oriented architecture will require all these features to be scalable across all vehicle model ranges.

The potential to reduce the cost of embedded software by standardizing the application behavior for Automotive Body and Comfort domain functions is explored in this paper. AUTOSAR, with its layered architecture and a standard definition of the interfaces for Body and Comfort application functions, has simplified the exchangeability of software components. A further step is to standardize the application behavior, by developing standard specifications for common Body and Comfort functions. The corresponding software components can be freely exchanged between different OEM/Tier-1 users, even if developed independently by multiple suppliers. In practice, individual OEM users may need to maintain some distinction in the functionality. A method of categorizing the specifications as ‘common’ and ‘unique’, and to configure them for individual applications is proposed. This allows feature variability by means of relatively simple adapter functions.