Search Results

It is essential to include uncertainties in the simulation process in order to perform reliable vibroacoustic predictions in the early design phase. In this contribution, uncertainties are quantified using the generalized Polynomial Chaos (gPC) expansion in combination with a Finite Element (FE) model of a vehicle body in white. It is the objective to particularly investigate the applicability of the gPC method in the industrial context with a high number of uncertain parameters and computationally expensive models. A non-intrusive gPC expansion of first and second order is implemented and the approximation of a stochastic response process is compared to a Latin Hypercube sampling based reference solution with special regard to accuracy and computational efficiency. Furthermore, the method is examined for other input distributions and transferred to another FE model in order to verify the applicability of the gPC method in practical applications.

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.

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.

Highly automated driving (HAD) is under rapid development and will be available for customers within the next years. However the evidence that HAD is at least as safe as human driving has still not been produced. The challenge is to drive hundreds of millions of test kilometers without incidents to show that statistically HAD is significantly safer. One approach is to let a HAD function run in parallel with human drivers in customer cars to utilize a fraction of the billions of kilometers driven every year. To guarantee safety, the function under test (FUT) has access to sensors but its output is not executed, which results in an open loop problem. To overcome this shortcoming, the proposed method consists of four steps to close the loop for the FUT. First, sensor data from real driving scenarios is fused in a world model and enhanced by incorporating future time steps into original measurements.

A joint study was conducted between BMW and Goodyear with the objective of analysing the cause and identifying methods to reduce the structure-borne interior noise in a vehicle driving on rough road surfaces. A vibro-acoustic characterization of the car was performed by measuring the car vibro-acoustic transfer functions and by using a transfer path analysis technique to identify the main suspension parts affecting the interior noise at target frequencies. The vibration transmissibility characteristics of the tire were measured and also simulated by Finite Element in [1-200Hz] frequency range. The vibro-acoustic interaction between the tire and car sub-systems was examined. A Finite Element sensitivity analysis was used to define and build new prototype tires. A 3dB(A) interior noise improvement was obtained with these new tires at target frequencies.

In this contribution functional safety is discussed from a car manufacturer's point of view. Typical elements of a safety standard concerning safety activities during the product development process are described as well as management and other supporting processes. Emphasis is laid on the aspect of risk assessment and the determination of safety classes. Experiences with methods for safety analysis like FTA or FMEA are discussed and pros and cons of quantitative safety assessment are argued.

Integration scenarios for ECU software become more complicated, as more constraints with regards to timing, safety and security need to be considered. Multi-core microcontrollers offer even more hardware potential for integration scenarios. To tackle the complexity, more and more model based approaches are used. Understanding the interaction between the different software components, not only from a functional but also from a timing view, is a key success factor for high integration scenarios. In particular for multi-core systems, an amazing amount of timing data can be generated. Usually a multi-core system handles more software functionality than a single-core system. Furthermore, there may be timing interference on the multicore systems, due to the shared usage of buses, memory banks or other hardware resources.

The amount of electronics in vehicles is increasing, so is the amount of power electronics circuits, like inverters for electric motor drives or dc/dc converters. The muscles of these circuits are power transistors like MOSFETs and IGBTs - in each circuit are several of them. While MOSFETs and IGBTs have advanced over the years in terms of their performance, their wide product spectrum and feature spectrum as well as cost, they are still not unbreakable, but semiconductors which are more sensitive to electrical or thermal overstress than, a relay for instance. Especially electrical overstress, like overvoltage or over current, may damage a power transistor within a short time frame. Hence, electrical overstress must be avoided when designing the power electronics circuit. However, even a power transistor in a carefully designed power electronics circuit, may still be exposed to over current, short circuit, over voltage, over temperature and so forth.

Two models for the forecast of road traffic emissions, independently developed in parallel, are comparatively presented and assessed: EPROG developed by BMW and enlarged by VDA for a national application (Germany) and FOREMOVE, developed for application on European Community scale. The analysis of the methodological character of the two algorithms proves that the models are fundamentally similar with regard to the basic calculation schemes used for the emissions. The same holds true as far as the significant dependencies of the emission factors, and the recognition and incorporation of the fundamental framework referring to traffic important parameters (speeds, mileage and mileage distribution etc) are concerned.

The increasing number of local control units in automotive systems led to growing emphasis on developing and using multiplex systems. For reasons of price and robustness the use of asynchronous and slow multiplex systems is preferred. Since the communication volume now reaches critical dimensions in peak load situations during the use of those systems, new concepts on different communication levels have to be developed. Due to the use of many different message types (wide range of message length) and the statistical dependence of the communication behaviour of control units (e.g. question-answer-combinations), the application of standard methodologies is only partly suitable for a performance analysis of automotive multiplex systems.

The increasing complexity of communication protocols for asynchronous multiplex systems requires the use of simulation during the optimisation of these protocols or the integration of other control units. Consideration of realistic communication behaviour of the connected control units is essential for performance analysis of multiplex systems. For a first pass, the use of simple statistical distributions (e.g. Poisson distribution) is suitable to get some simulation results. A better way to get realistic results is the approximation of empirical communication data through the use of more complex statistical distribution (e.g. mixed Erlang distributions). In this paper several approaches for the approximation of empirical data are presented. Beside simple statistical distributions (with one parameter), the use of more complex statistical distributions is discussed and methods for the identification of their parameters are presented.

This paper focuses on the requirements of electronic systems in automotive applications in terms of reliability and quality. As one of the most common devices in such applications for switching electronic loads, the power MOSFET, is investigated in detail. Today's qualification procedure for discrete devices according to AEC Q101 [1] will be explained and how this correlates to the stress of the device in the application. It will be pointed out what additional tests for “extended qualification” should be made to deal with critical failure modes reducing overly conservative safety margins and preventing excessive costs on the component side. The tests will be explained and the results presented.

In recent years, we see more and more ECUs integrating a huge number of application software components. This process mostly results from the increasing amount of so called in-house software in various fields like electric-drive, chassis and driver assistance systems. The software development for these systems is partially moved from the supplier to the car manufacturers. Another important trend is the introduction of new network architectures intending to meet the growing communication requirements. For such ECUs the software integration scenarios become more complicated, as more quality of service requirements with regards to timing, safety and security need to be considered [2]. Multi-core microcontrollers offer even more potential variants for integration scenarios. Understanding the interaction between the different software components, not only from a functional, but also from a timing view, is a key success factor for modern electronic systems [6,7,8,9].

The German funded project ARAMiS included work on several demonstrators one of which was a multicore approach on large scale software integration (LSSI) for the automotive domain. Here BMW and Audi intentionally implemented two different integration platforms to gain both experience and real life data on a Hypervisor based concept on one side as well as using only native AUTOSAR-based methods on the other side for later comparison. The idea was to obtain figures on the added overhead both for multicore as well as safety, based on practical work and close-to-production implementations. During implementation and evaluation on one hand there were a lot of valuable lessons learned about multicore in conjunction with safety. On the other hand valuable information was gathered to make it finally possible to set up a cost model for estimation of potential overhead generated by different integration approaches for safety related software functions.

This paper presents a reliability study of a directly cooled IGBT module after a test drive of 85,000 Km in a fuel cell electric vehicle, as well as of an indirectly cooled IGBT module after a test drive of 200,000km in a hybrid car on public roads. At the end of the test drive, the inverter units were disassembled and analyzed with regard to the lifetime consumption. First, electrical measurements were carried out and the results were compared with the ones obtained directly after module production (End of Line test). After that, ultrasonic microscopy was performed in order to investigate any delamination in the solder layers. As a third step, an optical inspection was performed to monitor damages in the housing, formation of cracks or degradation of wire bonds. The results show none of the depicted failure modes could be found on the tested power modules after the field test. Obviously, no significant life time consumption could be observed.

Functional safe products conforming to the ISO 26262 standard are getting more important for automotive applications wherein electronic takes more and more response for safety relevant operations. Consequently safety mechanisms are needed and implemented in order to reach defined functional safety targets. To prove their effectiveness diagnostic coverage provides a measurable quantity. A straight forward safety mechanism for sensor systems can be established by redundant signal paths measuring the same physical quantity and subsequently performing an independent output difference-check that decides if the data can be transmitted or an error message shall be sent. This paper focuses on the diagnostic coverage figure calculation of such data correlation-checks for linear sensors which are also shown in ISO 26262 part5:2011 ANNEX D2.10.2.

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.