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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.

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.

Heavy Duty Vehicle (HDV) Diesel emission regulations are set to be tightened in the future. The introduction of PN PEMS testing for Euro VI-e, and the expected tightening of PM/NOx targets set to be introduced by CARB in the US beyond 2024 are expected to create challenging tailpipe PN conditions for OEMs. Additionally, warranty and the useful life period will be extended from current levels. Improved fuel efficiency (reduction of CO2) also remains an important performance criteria. Furthermore, future non-road diesel emission regulations may follow tighten HDV diesel emission regulations contents, and non-road cycles evaluation needs to be considered as well for future. In response to the above tightened regulation, for Diesel Particulate Filter (DPF) technologies will require higher PN filtration performance, lower pressure drop, higher ash capacity and better pressure drop hysteresis for improved soot detectability.

Selective catalyst reduction (SCR) using cordierite honeycomb substrate is generally used as a DeNOx catalyst for diesel engines exhaust in both on-road and commercial off-highway vehicles to meet today’s worldwide emission regulations. Worldwide NOx emission regulations will become stricter, as represented by CARB2027 and EuroVII. Technologies which can achieve further lower NOx emissions are required. Recently, several technologies, like increased SCR catalyst loading amount on honeycomb substrates, and additional SCR catalyst volume in positions closer to the engine are being considered to achieve ultra-low NOx emissions. However, undesirable pressure drop increase and enlarging after treatment systems will be caused by adopting these technologies. Therefore, optimization of the material and honeycomb cell structure for SCR is inevitable to achieve ultra-low NOx emissions, while minimizing any system drawbacks.

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.

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.

In order to meet the challenging CO2 targets beyond 2020 without sacrificing performance, Gasoline Direct Injection (GDI) technology, in combination with turbo charging technology, is expanding in the automotive industry. However, while this technology does provide a significant CO2 reduction, one side effect is increased Particle Number (PN) emission. As a result, from September 2017, GDI vehicles in Europe are required to meet the stringent PN emission limits of 6x1011 #/km under the Worldwide harmonized Light vehicles Test Procedure (WLTP). In addition, it is required to meet PN emission of 9x1011 #/km under Real Driving Emission (RDE) testing, which includes a Conformity Factor (CF) of 1.5 to account for current measurement inaccuracies on the road. This introduction of RDE testing in Europe and China will especially provide a unique challenge for the design of exhaust after-treatment systems due to its wide boundary conditions.

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.

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.

With the push towards more stringent on-road US heavy duty diesel regulations (i.e. HD GHG Phase 2 and the proposed ARB 20 mg/bhp-hr NOx), emission system packaging has grown critical while improving fuel economy and NOx emissions. The ARB regulations are expected to be implemented post 2023 while regulation for EU off-road segment will begin from 2019. The regulation, called Stage V, will introduce particle number (PN) regulation requiring EU OEMs to introduce a diesel particulate filter (DPF) while customer demands will require the OEMs to maintain current emission system packaging. A viable market solution to meet these requirements, especially for EU Stage V being implemented first, is a DPF coated with a selective catalyst reduction (SCR) washcoat (i.e. SCRonDPF).

In order to meet the challenging CO2 targets beyond 2020 despite keeping high performance engines, Gasoline Direct Injection (GDI) technology usually combined with charged aspiration is expanding in the automotive industry. While providing more efficient powertrains to reduce fuel consumption one side effect of GDI is the increased particle formation during the combustion process. For the first time for GDI from September 2014 there is a Particle Number (PN) limit in EU of 6x10 sup 12 #/km, which will be further reduced by one order of magnitude to 6x10 sup 11 #/km effective from September 2017 to be the same level as applied to Diesel engines. In addition to the PN limit of the certification cycle NEDC further certification of Real Driving Emissions (RDE) including portable PN measurements are under discussion by the European Commission. RDE test procedure requires stable and low emissions in a wide range of engine operations and durable over a distance of 160 000 km.

With the increased use of engines utilizing direct fuel injection and the upcoming introduction of more stringent emissions legislation that regulates not only particulate mass (PM) but also particulate number (PN), the emissions from Direct Injection Spark Ignition Engines (DISI) are an increasing concern. Gasoline Particle Filters (GPF) represent a potential way to reduce particle number emissions from DISI engines and are particularly effective considering the tough performance requirements during cold start and over RDE operation. Even though some learning from the development and application of particulate filters to diesel engines can be transferred to gasoline engines, the particle composition, mass to number ratio as well as the exhaust gas temperature and composition from gasoline engines are significantly different to diesel engines. Therefore, there is the need to study the application of particulate filters to gasoline engines in more depth.

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.

Ammonia Selective Catalytic Reduction (SCR) is adapted for a variety of applications to control nitrogen oxides (NOx) in diesel engine exhaust. The most commonly used catalyst for SCR in established markets is Cu-Zeolite (CuZ) due to excellent NOx conversion and thermal durability. However, most applications in emerging markets and certain applications in established markets utilize vanadia SCR. The operating temperature is typically maintained below 550°C to avoid vanadium sublimation due to active regeneration of the diesel particulate filter (DPF), or some OEMs may eliminate the DPF because they can achieve particulate matter (PM) standard with engine tuning. Further improvement of vanadia SCR durability and NOx conversion at low exhaust gas temperatures will be required in consideration of future emission standards.