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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.

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.

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.

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).

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.

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.

Increasing demand for utilities like navigation systems or user-defined electronic phonebooks on one hand and sophisticated engine and gear controls on the other hand leads to growing bus load between distributed local control units. This paper shows the benefits and the characteristics of various state of the art data-compression algorithms and their impact on typical automotive multiplex dataclasses. The evaluation and optimization of promising algorithms can be done via a proposed “communications prototyping”-approach. The hardware/software components of such a rapid prototyping package are outlined. Finally, first performance results of suitable data-compression measures are presented.

The use of micro controllers in automotive systems renders the coordination of about 150 actors (70 electric motors, 15 magnetic valves and 50 relays). The resulting complexity of those systems as well as the requested zero defects demands time consuming testing. This work describes a method of performing test-scenarios, starting from a zero defect running specification, modeled by finite state machines. The test-scenarios are intended to determine whether a given system meets all specification requirements. First, a kind of structured modelling reactive automotive systems is deduced. Next, some important test selection methods, developed for the case the specification is given in the form of a finite state machine, are considered. Finally, a procedure and method for performing minimized complete test-scenarios for automotive systems are presented.

A lot of very valuable information has already been gained in the process of dismantling, assorting and reconditioning plastic parts on old cars, in reconditioning defective plastic parts from workshops, and in the use of reject parts from production. This know-how is now applied primarily to increase the use of recycled plastics and to optimise the composition and design of future plastic components in the interest of recycling, since further development in these areas is essential in order to establish economically stable material cycles functioning properly in the long term. The present paper describes the most important criteria through which the materials and designs chosen affect the processes and principles of recycling in the case of plastic parts and components.

For the BMW V8 engine, a new LEV/EU3 emission concept has been developed by improvements to the previous engine management and secondary air supply and a complete new exhaust system. Beside the emission limits, also high engine output targets and high operating reliability were targeted. In addition the new exhaust system had to meet low cost targets. Based on these requirements an exhaust concept with separate pre catalyst and main catalyst was chosen. To reduce the heat mass and to optimize the pressure drop, 4.3mil/400cpsi thin wall ceramic substrates were used for the pre and main catalyst.

In order to optimise the vibro-acoustic behaviour of panel-like structures in a more systematic way, accurate structural models are needed. However, at the frequencies of relevance to the vibro-acoustic problem, the mode shapes are very complex, requiring a high spatial resolution in the measurement procedure. The large number of required transducers and their mass loading effects limit the applicability of accelerometer testing. In recent years, optical measuring methods have been proposed. Direct electronic (ESPI) imaging, using strobed continuous laser illumination, or more recently, pulsed laser illumination, have lately created the possibility to bring the holographic testing approach to the level of industrial applicability for modal analysis procedures. The present paper discusses the various critical elements of a holographic ESPI modal testing system.

Seatbelt retractors as important part of modern safety systems are mounted in any automotive vehicle. Their internal locking mechanism is based on mechanically sensing elements. When the vehicle is run over rough road tracks, the retractor oscillates by spatial mode shapes and its interior components are subjected to vibrations in all 6 degrees of freedoms (DOF). Functional backlash of sensing elements cause impacts with neighbouring parts and leads to weak, but persistent rattle sound, being often rated acoustically annoying in the vehicle. Current acoustic retractor bench tests use exclusively uni-directional excitations. Therefore, a silent 2 DOF test bench is developed to investigate the effect of multi-dimensional excitation on retractor acoustics, combining two slip-tables, each driven independently by a shaker. Tests on this prototype test bench show, that cross coupling between the two perpendicular directions is less than 1%, allowing to control both directions independently.

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.

The specification of automotive ventilation / defrosting systems has often utilized “trial-and-error” and “prior experience” techniques. But design development and production efficiency has generated a strong interest in using more sophisticated design tools such as computational fluid dynamics. For this purpose a joint experimental and numerical study was undertaken. This comprehensive investigation was divided into two parts. First, the three dimensional defroster flow field was measured using LDA in an actual automobile. Second, LDA and infrared thermography was used to map the flow and temperature fields for a two dimensional jet impinging upon a slanted plate -- a simplified representation of a car defroster geometry.