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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.

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.

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 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.

Due to their high energy density, power density, and durability, lithium-ion (Li-ion) batteries are rapidly becoming the most popular energy storage method for electric vehicles. Difficulty arises in accurately estimating the amount of left capacity in the battery during operation time, commonly known as battery state of charge (SOC). This paper presents a comparative study between six different Equivalent Circuit Li-ion battery models and two different state of charge (SOC) estimation strategies. The Battery models cover the state-of-the-art of Equivalent Circuit models discussed in literature. The Li-ion battery SOC is estimated using non-linear estimation strategies i.e. Extended Kalman filter (EKF) and the Smooth Variable Structure Filter (SVSF). The models and the state of charge estimation strategies are compared against simulation data obtained from AVL CRUISE software.

The worldwide automotive industry is currently preparing for a market introduction of hydrogen-fueled powertrains. These powertrains in fuel cell electric vehicles (FCEVs) offer many advantages: high efficiency, zero tailpipe emissions, reduced greenhouse gas footprint, and use of domestic and renewable energy sources. To realize these benefits, hydrogen vehicles must be competitive with conventional vehicles with regards to fueling time and vehicle range. A key to maximizing the vehicle's driving range is to ensure that the fueling process achieves a complete fill to the rated Compressed Hydrogen Storage System (CHSS) capacity. An optimal process will safely transfer the maximum amount of hydrogen to the vehicle in the shortest amount of time, while staying within the prescribed pressure, temperature, and density limits. The SAE J2601 light duty vehicle fueling standard has been developed to meet these performance objectives under all practical conditions.

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.

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).

The BMW Group has introduced electric cars to the market with the MINI E already in 2009. The next step will be the launch of the BMW ActiveE in 2011, followed by the revolutionary Mega City Vehicle in 2013. The presentation will explain the BMW Group strategy for implementing sustainable mobility. A focus will be emobility, the use of carbon fiber and the holistic sustainability approach of BMW Group?s project i. Reference will be made to the research results of the MINI E projects in the US and in Europe. Presenter Andreas Klugescheid, BMW AG

Heavy trucks are produced with a great variety of vehicle configurations, operate over a wide range of gross vehicle weight and sometimes function in extreme duty environments. Frontal crashes of heavy trucks can pose a threat to truck occupants when the vehicle strikes another large object such as bridge works, large natural features or another heavy-duty vehicle. Investigations of heavy truck frontal crashes indicate that the factors listed above all affect the outcome for the driver and the resulting damage to the truck Recently, a new chassis was introduced for on-highway heavy truck models that feature frontal airbag occupant protection. This introduction presented an opportunity to incorporate the knowledge gained from crash investigation into the process for developing the crash sensor's parameter settings.

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.

In the automotive industry, the model-based approach is increasingly establishing itself as a standard paradigm for developing control unit software. Just as code reviews are widespread in classical software development as a quality assurance measure, models also have to undergo a stringent review procedure – particularly if they serve as a starting point for automatic implementation by code generators. In addition to these model reviews, the generated production code is reviewed later in the development process by performing auto code reviews. This article will present procedures for and give an account of experiences with model and code reviews which have been adapted to the model-based development process.

The EU SPARC Project (Secure Propelled Vehicle with Advanced Redundant Control) has developed a new system architecture that enables effective application of driver assisted systems in an X-by-wire powertrain. A major challenge in the conception of such a system is development of a reliable electrical energy supply. A simulation is the most important tool for enabling the fundamental aspects to work, as for example, a dimensioning of the powernet. This article explains our approach in this SPARC simulation. We provide suggestions through examples of how to find simulation solutions for powernet dimensioning, as well as for the conception and validation of energy management strategies.

There is a common understanding that hydrogen has a great potential to be the fuel of the future. In addition to the challenge of developing appropriate hydrogen propulsion systems the development of hydrogen storage systems is the second big issue. Due to its high potential in cost and weight and specific storage capacity, the BMW Group is focusing on the development of liquid hydrogen storage systems. In the next hydrogen 7-Series the BMW Group is about to make for the first time the step from demonstration fleets to cars used by external users with a liquid hydrogen storage system. To realize this significant goal, special focus has to be put on high safety standards so that hydrogen can be considered as safe as common types of fuel, and on the every day reliability of the storage system. Moreover, the development of strong partnerships with suppliers is a key factor to realize the design and identify appropriate manufacturing processes.

Whereas the verification of non-safety-related, embedded software typically focuses on demonstrating that the implementation fulfills its functional requirements, this is not sufficient for safety-relevant systems. In this case, the control software must also meet application-specific safety requirements. Safety requirements typically arise from the application of hazard and/or safety analysis techniques, e.g. FMEA, FTA or SHARD. During the downstream development process it must be shown that these requirements cannot be violated. This can be achieved utilizing different techniques. One way of providing evidence that violations of the safety properties identified cannot occur is to thoroughly test each of the safety requirements. This paper introduces Evolutionary Safety Testing (EST), a fully automated procedure for the safety testing of embedded control software.

Because of the rapidly increasing amount of electronic components and busses in a vehicle, the use of gateways in Electronic Control Units (ECUs) becomes more important. The upcoming question is how to design an optimal gateway. This paper describes a method for designing an optimal automotive gateway in an FPGA by using Evolutionary Algorithms (EAs). The complete gateway functionality is diagrammed in a specification graph which consists of a function graph and an architecture graph. The function graph describes the complete functionality of the gateway. The architecture graph shows the variety of the different implementation options of the mapped function graph. Each gateway task in the function graph can be realized either in a parallel way (different kinds of hardware implementations) or in a sequential way (software on a microprocessor core).

Development times in the automotive industry are becoming increasingly shorter. For this reason, design decisions based on simulation results must be made at an early development stage. The dynamic simulation of an automotive refrigeration cycle with Dymola/Modelica as part of the design process will be described in the following paper. The component supplier's expertise as well as the automotive manufacturer's knowledge of vehicle parameters in one simulation platform will also be discussed.

Increasing vehicle complexity, broader vehicle variety, and global vehicle projects are a major challenge to all global vehicle manufacturers. Common vehicle projects not only require a common E/E-architecture in order to share components, but also a common diagnostic strategy. On-board and off-board strategies have been developed to achieve this goal. This paper will explain the importance of ISO-standardization, common networking & diagnostic architectures and standardized access to vehicle electronics. Also the need for a common diagnostic process chain throughout engineering, manufacturing and service, and the need for harmonized vehicle standards between passenger cars and heavy duty trucks will be addressed.

In open jet wind tunnels with high blockage ratios a sharp rise in drag is observed for models approaching the nozzle exit plane. The physical background for this rise in drag will be analyzed in the paper. Starting with a basic analysis of the dependencies of the effect on model and wind tunnel properties, the key parameters of the problem will be identified. It will be shown using a momentum balance and potential flow theory that interaction between model and nozzle exit can result in significant tunnel-induced gradients at the model position. In a second step, a CFD-based investigation is used to show the interaction between nozzle exit and a bluff body. The results cover the whole range between open jet and closed wall test section interaction. The model starts at a large distance from the nozzle, then moves towards the nozzle, enters the nozzle and is finally completely inside the nozzle.