20. Internationales Stuttgarter Symposium

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The Commercial Vehicle Business is at the beginning of more disruptive change driven by electrification, automation and digitization. New technologies will be introduced with growing frequency. In the same time the demands from legislation but even more important from society to step out of fossil energy sources towards CO2-neutral transport solutions forces the manufacturers of Commercial Vehicles to continue to improve on the classic technologies but as well manage the transition into the new areas.

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The Photonic Age within the Automotive Industry has started already long time ago for more than 100 years. Coming from candles to illuminate drive ways to today’s PIXEL Lighting and Laser beams in car headlamps or Organic-LED (OLED) in Backlighting.

In this paper we describe a two-dimension compass used in SEAT as a digitalization framework to define and prioritize IT projects to generate as much benefit possible for the company.

Electricity-based fuels allow a climate neutral operation of powertrains, including internal combustion engines (ICE). However, today such energy sources are characterized by higher costs compared to fossil fuels, which is not acceptable to the customer. A plug-in hybrid electric vehicle appears to be a beneficial solution. With a conveniently designed battery capacity, most of the average driving distance can be realized electrically, while the ICE has advantages for long-distance trips.

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In order to take measures for climate protection, CO2 emissions are to be reduced by 80-95% by 2050 in the European Union [1]. In order to keep the internal combustion engine competitive in terms of CO2 and pollutant emissions to Battery Electric Vehicles (BEV) or Fuel Cell Electric Vehicles (FCEV), alternatives to fossil fuels must be found. The choice of fuel plays a decisive role here. The main idea is to produce fuels synthetically with the help of renewable energies. This can lead to a CO2-neutral fuel.

The vehicle market in Germany is currently influenced by many different factors. Stricter emission regulations, exhaust gas scandals, high traffic densities and a large number of available models with different drive system topologies are just some of the characteristics. In order to successfully establish vehicles on the market, customer needs and conditions of use must be taken into account during their development. Aspects such as operational range and sustainability of the vehicles as well as legal questions must also be considered. In this work, these aspects will be summarized and put into context.

Changing drivetrain architectures and designs such as highly integrated e-Axles and hybrid transmissions require new filtration solutions. Especially when it comes to the joint integration of all drivetrain components such as the direct cooling of the engine and power electronics and even the direct cooling of the battery cells. For these tasks the requirements for differential pressure and oil cleanliness are less and the dielectric oil properties and the insulating function, for instance, come more and more into focus.

The vehicle electrification era is here, bringing with it a wave of innovative technologies and exponential advancements. However, there are important safety implications to be considered as these technologies and market trends are adopted. What are these trends and how do they impact circuit protection of electric vehicles?

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The limited range of battery electric vehicles continues to play an important role for their acceptance. This applies not only to private but also to public transport. Thus, operators require an accurate range prediction to be able to plan the operation of an electric bus fleet.

In a world of urbanization, the lockout risk for vehicles in cities is continually increasing. Emissions, air pollution and the increasing volume of traffic as well as the resulting traffic jams in cities are the main drivers for this development.

The accurate remaining range calculation for electric vehicles is crucial in reducing range anxiety. Thus, a reliable method to assess the quality of the used remaining driving range (RDR) calculation is required, which allows us to take into consideration multiple trips, ensuring RDR accuracy on all given environment conditions. Both the evaluation method and the evaluation criteria play a key role in finding an accurate RDR calculation.

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Standardized driving cycles (e.g. WLTC) define speed points for a straight driving road load calculation in simulation and on roller chassis dynamometers. Within real rides on public roads, straight driving road loads are increased by curve resistances during curve driving. Furthermore, for curve driving the wheels has to be steered, so that the steering system needs to be actuated to overcome the necessary steering torque at the steered wheels.

The automobile is quickly morphing from an isolated, largely mechanical piece of equipment to one of the most technically sophisticated and connected platforms on the planet. Few technologies have been more anticipated heading into the 2020s than autonomous vehicles. Still perhaps decades from market adoption in some use cases, the technology is as promising as it is misunderstood.

For autonomous cars it is crucial to perceive its current environment to ensure safe driving maneuvers. Light detection and ranging sensors (LiDAR) are often used for object detection due to their accurate distance measurements. However, point clouds sensed by LiDAR provide information of the environment which are not important for object detection algorithms (e.g: vegetation, buildings).

In the context of the release of automated driving functions on SAE level 3 and higher, research is currently focusing on the safety proof. However, even on level 1 and 2, the driver can potentially hand over the driving tasks to the vehicle in many situations, while still being responsible for the driving. Therefore, it is of interest to be able to quantify the value added by such driving functions, depending on the environment and region where the vehicle is driven.

Reducing the CO2 standard values through the EU is showing effect: Car manufacturers, cities and municipalities are looking for alternatives to vehicle propulsion systems in face of imminent fines.

Hydrogen-powered vehicles comprising low temperature proton exchange membrane fuel cells (LT-PEMFC) appear to be a viable, emission free, solution characterized by short refueling times and long driving ranges. However, additional optimizations are required on a component and system level to achieve further cost reduction while ensuring adequate service life and sufficiently high energy conversion efficiencies to boost the market shares of fuel cell electric vehicles (FCEVs).

Climate change is one of the major threats to mankind. To reach the target of maximum 1.5°C temperature rise compared to pre-industrial levels set by the COP21 Conference in Paris, CO2 emissions from transport must be reduced significantly. Fuel cell technology can play a major role in reducing these emissions.

The powertrain of a fuel cell hybrid electric vehicle (FCHEV) can be realized by a combination of a proton-exchange membrane (PEM) fuel cell system (FCS) and a battery system (BS). The use of two power sources in one vehicle increases the complexity but also combines the advantages of both systems, such as using an energy source in FCS for realizing high ranges in combination with the high dynamics of BS [1]. Furthermore, the hybrid powertrain provides an additional degree of freedom in the control of power demand.

The use of combustion engines in the powertrain of vehicles will continue to play an important role for some years to come. In order to be able to comply with the legal framework conditions in the future, a further reduction in emissions must be achieved. An optimally designed exhaust aftertreatment system plays a decisive role in achieving this goal.

LED-Matrix headlamps use single controllable LEDs to realize AFS lighting functions (advanced frontlighting system) such as glare-free highbeam. In those modules, each LED is responsible for a specific illumination angle on the street. By European law, in case of a failure of one LED, the driver must be notified [1].

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Highly Automated Driving (HAD) technology is an enabler for innovation in the automotive industry through the integration of complex vehicle functions for comfort, safety and economy. However, the safety implications of these functions greatly increase the validation requirements as a pre-requisite for their release into the intended environment. Software-in-the-Loop (SiL) environments are emerging as an alternative to traditional testing approaches such as Hardware-in-the-Loop (HiL) systems. This is due to the strong evolution of vehicle simulation technologies as well as the virtualization of electronic control units, vehicle networks, vehicle simulation technologies coupled with the availability of increased computing power. In this paper we provide insights into a novel approach for arguing the credibility of SiL environments for validating the complex functional and non-functional requirements of HAD. These approaches include methodologies to extract test scenarios from real field-data and automatically evaluate their relevance, selection of test cases that validate the credibility of the given SiL environment and a unique metric to indicate the accuracy of the SiL environment in comparison to the reference system. With the help of a HAD use case we then demonstrate the advantages of this novel approach compared to methods currently employed in the automotive industry.

The success of automotive development in the field of autonomous driving and electrification increasingly relies on the ability to gather, manage and analyze large sets of data. Thereby, the development processes and utilized tools must adapt to provide the right environment to face emerging challenges. What are those challenges and how can an integrated Big Data platform help engineers to succeed? In this article, we discuss the four main challenges and requirements we identified which are linked to vehicle development in the era of Big Data. They emerge from the integration-, collaboration-, automation- and innovation-aspects of such a platform. With a use case, we furthermore demonstrate the capabilities of a Big Data platform in practice.

The development of driver assistance systems and automated driving functions is associated with new challenges for the development process. More and more comprehensive functions have to be realized in series production at low cost. This problem pushes established development environments to their limits.

48 V-hybrid drives represent a cost-efficient technology that can significantly reduce the fuel consumption of passenger cars, while requiring limited integration effort. To achieve the maximum benefit with a 48 V-system while maintaining low overall system costs, a detailed understanding of the relations between conflicting optimization goals such as efficiency, comfort and cost, especially under real-driving conditions, is essential. This paper analyzes this conflict in a 48 V-system with P2 topology, where the optimization goals are represented by CO2 emissions (efficiency), engine start frequency (comfort) and effective battery load (component cost). Characteristic real-driving scenarios are investigated using a longitudinal-dynamics simulation model.

In hybrid powertrains, the energy conversion in the Internal Combustion Engine (ICE) is the primary source of mechanic energy. With hybridization, an overall efficiency benefit can be achieved by an engine load point shift towards best efficiency and recuperation of brake energy. An optimal control of the system is crucial to exploit the theoretical potential.

The increasing share of hybrid powertrains entails new challenges for the development of drive systems. Setting up and equipping a test bench with all the necessary components of a future drive system is very costly and time-consuming. Besides, in an early development phase prototype components may not even be available yet or may not have the maturity level required to run tests on the overall system. For these reasons more efficient development approaches are needed.

Engine and vehicle manufacturers are facing increasing pressure by legislation and economics to reduce vehicle emissions and deliver improved fuel economy. Significant reductions in carbon dioxide (CO2) emissions will need to be achieved to meet these requirements whilst at the same time satisfying the more stringent forthcoming emissions regulations. This focus on techniques to reduce the tailpipe CO2 is increasing the interest in both techniques for reducing internal combustion engine fuel consumption, such as dilute combustion, and also hybrid and electric vehicle technologies.

With the entry into force of Regulation (EU) 2017/1151, compliance with the limit values for pollutants (CO, HC, NOx) as defined in (EC) 715/2007 (EURO6) will become effective on 01.01.2021 for all newly registered passenger cars and light commercial vehicles within the EU binding in the RDE cycle.

The downsizing of direct injection gasoline engines shifts the engine operating points to areas of higher efficiency increasing the power density by means of turbocharging. The disadvantage is a delay in torque build-up at low rpm because of the low mass flow rate through the turbine. The low mass flow causes a low boost power, which again is coupled with the mass flow through the turbine.

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The ongoing development of automated vehicles and continuing electrification of mobility allows new design possibilities for vehicle concept development. However, the issue of electric power supply system reliability of automated vehicles gains more importance. Single faults within the electric power supply system shall not result in loss of vehicle control and unsafe operation.

The rapid development of advanced driving assistance systems (ADAS) and the expected introduction of automated driving functions in the coming years shifts the safety requirements to E/E-architectures on a new level. While until now fail-safe behavior was the predominant approach, SAE level 3-5 applications will demand a fail-operational infrastructure.

The increasing importance of simulation methods in vehicle development leads to high demands on simulation models with regard to complexity and reusability. The fragmented tool landscapes, which are state of the art, offer only solutions that involve high manual development effort. Therefore, a software toolchain was created in the HiFi- ELEMENTS EU-funded research project, with the aim of accelerating the development process with model-based simulation and with using the example of battery-electric vehicles. For this purpose, standardized processes were designed and the tools used were coupled. The toolchain adapts the approach of the Systems Engineering Methodology MTSF, which enables the standardization of a component-based model architecture with internal interfaces for the simulation of several vehicle variants.

Originally representing a constructive profession, thermal management has become an interdisciplinary field in the automotive industry, which includes computational fluid dynamics, chemical and process engineering, as well as material science. This growth, especially in recent years, has been accompanied by an increasing automation concerning on-board thermal control. This development leads to e.g. on-demand temperature control, as this offers several benefits for various drivetrains.

Operation temperature of vehicle components in internal combustion engines is far above ambient temperature, in particular compared to winter ambient temperatures of down to -20 °C. At the cold start phase at the beginning of a driving cycle, the systems’ performance is highly inefficient, which leads to increased pollutants (NOx, CO, HC) and degradation of the components. 60-80% of all pollutants of the whole driving cycle are generated during this cold start phase. It takes several minutes until the system reaches operation temperature.

Efficient energy management of electric vehicles, EVs, in terms of range optimization and cabin climate control, undoubtedly depends on the efficient battery thermal management. Due to inherently transient system behavior of EVs, the dynamic behavior of battery packs is almost always examined in conjunction with the entire vehicle. As a result, it is desired to have battery models that are not only fast running but also accurate.

Operating artificial neural networks (ANN) and especially the training of ANN requires an available database. Using the cloud connection of vehicles, measurement data from single vehicles and from whole fleets can be collected in big scale.

Today’s powertrain ECU development uses Agile SW development, continuous integration, testing and deployment. Typically, the aim is to achieve high maturity of the function modules in the integrated system as quickly as possible. Further it is necessary to have high reactivity to changes in requirements (innovations, market changes, etc.).

Customer requirements evolve frequently in the automotive domain. A the same time, rapid integration of innovative solutions is important to maintain and extend the customer base (Vitale et al. 2020). Thus, automotive software components are subject to constant change and variations, even when the vehicle is already on the road (Guissouma et al. 2018).

Participants in our 2020 Vector survey observe three significant challenges [1]. Cost and efficiency have emerged as the single most relevant short-term challenge, indicating the need to succeed in a fast-changing world with unclear business drivers. At the same time, quality is further growing compared to our last-year survey as short- and mid-term target.

The industry is working intensively on the precision of thermal management. By using complex thermal management strategies, it is possible to make engine heat distribution more accurate and dynamic, thereby increasing efficiency. Significant efforts are made to improve the cooling efficiency of the engine water jacket by using 3D CFD.

In this paper, a methodology is presented, which enables a calibration of 3D-CFD injection models by the use of optical investigations obtained from a constant volume chamber.

Hybrid electric vehicles (HEVs) are forecasted remarkably spreading in the global vehicle market over the next years due to their capability of reducing fuel consumption and tailpipe emissions while simultaneously tackling current limitations for on-board storable electrical energy [1]. Nevertheless, design procedures and sizing methodologies for HEV powertrains are consistently complicated compared to both battery electric vehicles (BEVs)that are powered by electric motors solely, and conventional vehicles that are powered by internal combustion engines (ICEs) solely [2]. Difficulties in HEV powertrain design and sizing relate both to the amount of power components embedded (i.e. one ICE, one or multiple electric motor/generators (MGs), a high-voltage battery, dedicated power electronics) and the necessity of a related proper energy management strategy (EMS) [3].

Automotive OEMs are facing many challenges today. One of them is to rapidly increase within the next couple of years, the portfolio of their electric vehicles, and to do this, in the shortest possible development time and with the minimum occurred costs. Considering the battery as one of the critical components within the electric powertrain, the solution is to approach battery development for new electric vehicles based on an already existing battery pack project.

eMobility is a clear trend in the automotive industry and will soon dominate new vehicle registrations. The providence of a sufficient electric range is one of the major challenges on the way there. An enabler that is associated with this issue is battery technology.

Worldwide more and more fluctuating renewable energy, such as solar and wind power, is being strongly increased (so called “Energiewende”). The expansion of energy storage to compensate these energy fluctuations will become an important issue in the future. There is the possibility to store e.g. photovoltaic (PV) electricity during sunny days and give it back to the home grid as needed during night or cloudy days.

Passenger cars with combustion engines dominate the 20th century because of their significant expanded range, the quick refuel process as well as availability and price of the fossil fuels. During the last years, electric vehicles experience a renaissance from their previous developments at the end of the 19th century to one enabler for future mobility [1]. Especially social desirability of our society as well as resulting political and legal framework, but also the increased price and limitedness of fossil fuels, promote a change in the type of drive.