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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 aim of the project is to reliably identify the set of constructive features responsible for the highest noise levels in the interior of motor vehicles. A simulation environment based on artificial intelligence techniques such as neural networks and genetic algorithms has been implemented. We used a system identification approach in order to approximate the functional relationship between the target noise series and the sets of constructive parameters corresponding to the cars. The noise levels were measured with a microphone positioned on the driver''s chair, and corresponded to a variation of the engine rotation of 600-900 rot/min. The database includes 45 different cars, each described by vectors of 67 constructive features.

A new simulation tool was established and approved by TRW as part of the continuous improvement of the development process. This tool allows the OEM and the system supplier to keep high quality even with further reduced development times. The introduction of the tool in a side air-bag development program makes it possible to ensure high development confidence with a reduced number of vehicle crash tests and late availability of interior component parts.

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.

Nowadays, a continually growing system complexity due to the development of an increasing number of vehicle concepts in a steadily decreasing development time forces the engineering departments in the automotive industry to a deepened system understanding. The virtual design and validation of individual components from subsystems up to full vehicles becomes an even more significant role. As an answer to the challenge of reducing complete hardware prototypes, the virtual competence in NVH, among other methods, has been improved significantly in the last years. At first, the virtual design and validation of objectified phenomena in analogy to hardware tests via standardized test rigs, e.g. four poster test rig, have been conceived and validated with the so called MBS (Multi Body Systems).

Handling characteristics, ride comfort and active safety are customer relevant attributes of modern premium vehicles. Electronic control units offer new possibilities to optimize vehicle performance with respect to these goals. The integration of multiple control systems, each with its own focus, leads to a high complexity. BMW and ITK Engineering have created a tool to tackle this challenge. A simulation environment to cover all development stages has been developed. Various levels of complexity are addressed by a scalable simulation model and functionality, which grows step-by-step with increasing requirements. The simulation environment ensures the coherence of the vehicle data and simulation method for development of the electronic systems. The article describes both the process of the electronic control unit (ECU) development and positive impact of an integrated tool on the entire vehicle development process.

This paper presents an object-oriented method customized for a tool-assisted development of car software components. Tough market conditions motivate smart software development. ASCET SD is a tool to generate target code from graphic specifications, avoiding costly programming in C. But ASCET lacks guidelines on what to do, how to do it, in what order, like a fully equipped kitchen without a cooking book. Plans to employ the tool for BMW vehicle software sparked off demand for an adequate, object-oriented real-time methodology. We show how to scan the methodology market in order to adopt an already existing method for this purpose. The result of the adaptation of a chosen method to ASCET SD is a pragmatic version of ROOM, which we call BROOM. We present a modeling guidebook that includes process recommendations not only for the automotive sector, but for real-time software development in general.

The environmental impact of automobiles and components is of growing importance both in the public discussion and in the complex decision finding process for future car concepts. Especially more and more conflicts of objectives occur between technical, economical, ecological and political requirements. For a lasting improvement of environmental compatibility and recyclability as an element of the so-called „sustainable development” of automobiles and components, BMW is the first carmaker which has developed quantitative management tools /1, 2/. These component related instruments enable designers in the product development phase to evaluate different component variations practice-oriented and with an ecological perspective in mind. In this endeavor, BMW is placing its bets on „intelligent lightweight construction”, i. e., the ecologically-economically best component solution.

This paper describes the first results from the AutoMoDe project (Automotive Model-based Development), where an integrated methodology for model-based development of automotive control software is being developed. The results presented include a number of problem-oriented graphical notations, based on a formally defined operational model, which are associated with system views for various degrees of abstraction. It is shown how the approach can be used for partitioning comprehensive system designs for subsequent implementation-related tasks. Recent experiences from a case study of an engine management system, specific issues related to reengineering, and the current status of CASE-tool support are also presented.

BMW has introduced new test stands for noise measurements on passenger cars and motorcycles. Information is given on room conditions, machinery equipment, sound levels, frequency ranges and types of measurement. The semi-anechoic room is designed for measuring the sound distribution emitted by a single vehicle. Road influence is simulated by a reflecting floor and a roller-dynamometer. The free field sound distribution in terms of distance and direction is measured in the anechoic room. This room has high-precision installations for sound source identification and noise mapping. The reverberation room serves to measure sound power emitted by the test object. Its second purpose is to subject the bodywork to a high-power external sound source and to measure the sound-deadening effect of the passenger compartment. In conclusion, the presentation provides reports on the initial experience with these test facilities.

Exhaust acoustics simulation is an important part of the exhaust system process. Especially important is the trend towards a coupled approach to performance and acoustics design. The present paper describes a new simulation tool developed for such coupled simulations. This tool is based on a one-dimensional fluid dynamics solution of the flow in the engine manifolds and exhaust and intake elements. To represent the often complex geometries of mufflers, an easy-to-use graphical pre-processor is provided, with which the user builds a model representation of mufflers using a library of basic elements. A comparison made to two engines equipped with exhaust silencers, shows that the predictions give good results.

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.