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Vehicle thermal management (VTM) simulations are becoming increasingly important in the development phase of a vehicle. These simulations help in predicting the thermal profiles of critical components over a drive cycle. They are usually done using two methodologies: (1) Solving every aspect of the heat transfer, i.e., convection, radiation, and conduction, in a single solver (Conjugate Heat Transfer) or (2) Simulating convection using a fluid solver and computing the other two mechanisms using a separate thermal solver (Co-simulation). The first method is usually computationally intensive, while the second one isn’t. This is because Co-simulation reduces the load of simulating all heat transfer mechanisms in a single code. This is one of the reasons why the Co-simulation method is widely used in the automotive industry. Traditionally, the methods developed for Co-simulation processes are load case specific.

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.

The continuous encouragement of lightweight design in modern vehicles demands a reliable and efficient method to predict and ameliorate the interior acoustic comfort for passengers. Due to considerable psychological effects on stress and concentration, the low frequency contribution plays a vital rule regarding interior noise perception. Apart other contributors, low frequency noise can be induced by transient aerodynamic excitation and the related structural vibrations. Assessing this disturbance requires the reliable simulation of the complex multi-physical mechanisms involved, such as transient aerodynamics, structural dynamics and acoustics. The domain of structural dynamics is particularly sensitive regarding the modelling of attachments restraining the vibrational behaviour of incorporated membrane-like structures. In a later development stage, when prototypes are available, it is therefore desirable to replace or update purely numerical models with experimental data.

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.

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.

Vehicle thermal management (VTM) simulations constitute an important step in the early development phase of a vehicle. They help in predicting the temperature profiles of critical components over a drive cycle and identify components which are exceeding temperature design limits. Parts with the highest temperatures in a vehicle with an internal combustion engine are concentrated in the engine bay area. As packaging constraints grow tighter, the components in the engine bay are packed closer together. This makes the thermal protection in the engine bay even more crucial. The fan influences the airflow into the engine bay and plays an important role in deciding flow distribution in this region. This makes modelling of the fan an important aspect of VTM simulations. The challenge associated with modelling the fan is the accurate simulation of the rotation imparted by the fan to the incoming flow. Currently, two modelling approaches are prevalent in the industry.

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.

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.

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.

Real-time simulation is a valuable tool in the design and test of vehicles and vehicle parts, mainly when interfacing with hardware modules working at a given rate, as in hardware-in-the-loop testing. Real-time operating-systems (RTOS) are designed for minimizing the latency of critical operations such as interrupt dispatch, task switch or inter-process communication (IPC). General-purpose operating-systems (GPOS), instead, are designed for maximizing throughput in heavy-load systems. In complex simulations where the amount of work to do in one step is high, achieving real-time depends not only in the latency of the event starting the step, but also on the capacity of the system for computing one step in the available time. While it is demonstrated that RTOS present lower latencies than GPOS, the choice is not clear when maximizing throughput is also critical.

The paper presents first a description of the methods used for the analysis of global dynamics of rotating systems like jet engines but also auxiliary power units. Different methodologies are described so to model rotating parts using beam, but also Fourier multi-harmonic, three dimensional models or to take into account cyclic symmetry and multistage cyclic symmetry concepts. Advantages and disadvantages of the different model types are discussed and compared. The coupling of the rotating parts with casings and stators is then discussed both in the inertial frame and in the rotating frame. The effect on global dynamics of bearing and other linking devices is taken into account for different type of analysis from critical speed analysis, to harmonic and transient analysis. The effect of gears and gear boxes coupling different rotors (like it is the case for auxiliary power units in a jet engine) is then discussed and appropriate methods described so to model this coupling effect.

An ESC hydraulic modulator contains on/off valves and proportional valves. A complex model of one proportional valve is detailed and used as a basis for model reduction the activity index technique. One interesting aspect is that the technology of the proportional valves remains (i.e. ball valves under conical seat). As such, the parameters are physical parameters forming the ones to master (manufacturing tolerances) by the supplier to also master the dynamic behavior of the system. Once this has been done, a complete model of half an ESC braking circuit is built including the pump, the reservoir, the pipes and hoses as well as the calipers. The activity index technique is thus reused on the circuit to further reduce it to finally obtain a modeling level acceptable for real time purpose.

Multicore-based ECUs are increasingly used in embedded automotive software systems to allow more demanding automotive applications at moderate cost and energy consumption. Using a high number of parallel processors together with a high number of executed software components results in a practically unmanageable number of deployment alternatives to choose from. However correct deployment is one important step for reaching timing goals and acceptable latency, both also a must to reach safety goals of safety-relevant automotive applications. In this paper we focus at reducing the complexity of deployment decisions during the phases of allocation and scheduling. We tackle this complexity of deployment decisions by a mixed constructive and analytic approach.

Vehicles with electrified powertrains are being introduced at an increasing pace. On the level of interior sound, one is often inclined to assume that NVH problems in EV have disappeared together with the combustion engine. Three observations demonstrate that this is not the case. First of all, only the dominant engine sound disappears, not the noise from tire, wind or auxiliaries, which consequently become increasingly audible due to the removal of the broadband engine masking sound. Secondly, new noise sources like tonal sounds from the electro-mechanical drive systems emerge and often have, despite their low overall noise levels, a high annoyance rating. Thirdly, the fact that engine/exhaust sounds are often used to contribute to the “character” of the vehicle leads to an open question how to realize an appealing brand sound with EV. Hybrid vehicles are furthermore characterized by mode-switching effects, with impact on both continuity feeling and sound consistency problems.

Tip-in/Tip-out of the accelerator pedal generates transient torque oscillations in the driveline. These oscillations may be amplified by P/T, suspension and body modes and will eventually be sensible at the receiver side in the vehicle, for example at the seat or at the steering-wheel. The forces that are active during this transient excitation are influenced by non-linear effects in both the suspension and the power train mounts. In order to understand the contribution of each of these forces to the total interior target response (e.g. seat rail vibration) a detailed investigation is performed. Traditional force identification methods are not suitable for low-frequent, transient phenomena like tip-in/tip-out. Mount stiffness method can not be used because of non-linear effects in the P/T and suspension mounts. Application of matrix inversion method based on trimmed body vibration transfer functions is not possible due to numerical condition problems.