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The reduction of vehicle exhaust particle emissions is a success story of European legislation. Various particle number (PN) counters and calibration procedures serve as tools to enforce PN emission limits during vehicle type approval (VTA) or periodical technical inspection (PTI) of in-use vehicles. Although all devices and procedures apply to the same PN-metric, they were developed for different purposes, by different stakeholder groups and for different target costs and technical scopes. Furthermore, their calibration procedures were independently defined by different stakeholder communities. This frequently leads to comparability and interpretation issues. Systematic differences of stationary and mobile PN counters (PN-PEMS) are well-documented. New, low-cost PTI PN counters will aggravate this problem. Today, tools to directly compare different instruments are scarce.

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.

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.

Vehicle-to-Grid (V2G) technology is expected to play a role in addressing the imbalance between periods of peak demand and peak supply on the electricity grid. V2G technology enables two-way power flow between the grid and the high-power, high-capacity propulsion battery in an electrified vehicle. That is, V2G enables a balance between power demand and supply, which is becoming difficult due to the introduction of intermittent sources of renewable energy. The authors have shown through system simulations that bi-directional wireless power transfer (WPT) is possible with a system that meets the emerging SAE J2954 standard. Based on the result, the authors have also demonstrated a bi-directional wireless charging system for V2G applications. In this work an existing SAE J2954 compatible uni-directional system was adapted to enable bi-directional wireless power transfer with minimum impact to system cost, while maintaining full compatibility with the requirements of SAE J2954.

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.

Wireless Power Transfer (WPT) promises automated and highly efficient charging of electric and plug-in-hybrid vehicles. As commercial development proceeds forward, the technical challenges of efficiency, interoperability, interference and safety are a primary focus for this industry. The SAE Vehicle Wireless Power and Alignment Taskforce published the Recommended Practice J2954 to help harmonize the first phase of high-power WPT technology development. SAE J2954 uses a performance-based approach to standardizing WPT by specifying ground and vehicle assembly coils to be used in a test stand (per Z-class) to validate performance, interoperability and safety. The main goal of this SAE J2954 bench testing campaign was to prove interoperability between WPT systems utilizing different coil magnetic topologies. This type of testing had not been done before on such a scale with real automaker and supplier systems.

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.

A new hydrogen fueling protocol named MC Formula Moto was developed for fuel cell motorcycles (FCM) with a smaller hydrogen storage capacity than those of light duty FC vehicles (FCV) currently covered in the SAE J2601 standard (over than 2kg storage). Building on the MC Formula based protocol from the 2016 SAE J2601 standard, numerous new techniques were developed and tested to accommodate the smaller storage capacity: an initial pressure estimation using the connection pulse, a fueling time counter which begins the main fueling time prior to the connection pulse, a pressure ramp rate fallback control, and other techniques. The MC Formula Moto fueling protocol has the potential to be implemented at current hydrogen stations intended for fueling of FCVs using protocols such as SAE J2601. This will allow FCMs to use the existing and rapidly growing hydrogen infrastructure, precluding the need for exclusive dispensers or stations.

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 study was undertaken to develop a method of numerical performance prediction for application to the development of controlled-auto-ignition (CAI) natural gas engines. By using a combustion model based on analyzed combustion data and introducing this to a commercial one-dimensional gas dynamic simulator, we attempted to establish a means of attaining a highly accurate performance prediction while reducing the calculation load. The combustion model was separately calibrated for two models, namely, the auto-ignition timing of the combustion and the mass fraction burned. As a result, the combustion modeling was able to successfully predict the accuracy of the auto-ignition timing difference at 0.03 degree of crank angle on average, and 0.95 degree in the 2σ region. Furthermore, the functions of the mass fraction burned were expressed using closely correlated in-cylinder parameters.

Simplified Side Impact Finite Element Model (SSM) merged the complex side crash model parameters used in LS-DYNA4; the same sophisticated software employed by finite element (FE)2 analysts, and the user-friendly custom graphical user interface (GUI)1 to allow users having little to no simulation software knowledge the ability to conduct a full vehicle representative crash simulation. Prior to SSM development a literature search was carried to try and identify similar CAE tools for side impact. We did not find any tool that would cater specifically to side impact. During the testing phase, SSM demonstrated that one model analysis run can be completed in fewer than thirty (30) minutes, a radical efficiency increase because previous procedures require several days of effort from a highly skilled FE2 analyst to set up, execute, and analyze.

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.

Precise vehicle positioning is dependent on the availability of a clear line of sight path between a vehicle and four or more satellites. For improved estimation of vehicle positioning in the absence of a complete set of visible satellites, Dedicated Short-Range Communication (DSRC) may offer a temporary position estimate. This paper investigates the feasibility of acquiring position based solely on DSRC signals. First the precision of DSRC signal strength (SI)-based position estimates is analyzed; this is followed by an analysis of position estimates based on time of flight (TOF). SI and TOF methods are compared using an extensive set of outdoor data collected from 30 vehicles on a test track. This research suggests that TOF is a better measure than SI for estimating position information.

Timing evaluation methods help to design a robust and extendible E/E architecture (electric/electronic). BMW has introduced the systematic application of such methods in the E/E design process within the last three years. Meanwhile, most of the architectural changes are now verified by a tool-based, automatic real-time analysis. This has increased the accuracy of the network planning and productivity of the BMW network department. In this paper, we give an overview of the actual status of timing evaluations in BMW's E/E architecture design. We discuss acceptance criteria, analysis metrics, and design rules, as far as these are related to timing. We look specifically at automation options, as these improve the productivity further. We will see that timing analysis has matured and should be mandatory for application in mass production E/E architecture development. At the same time, there is room for future improvements.

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.