21. Internationales Stuttgarter Symposium

The brand-new S-Class is the beacon of automotive luxury, which addresses evolving customer expectations and technical opportunities. We enter into a new era in the automotive Powernet domain. Optimization in both power availability and drive hybridization, as well as new features such as partial networking characterize the highly integrated state-of-the-art Mercedes powernet. The new S-Class offers heightened energy efficiency and increased reliability for further automation of driving and vehicle features. The systematically optimized E/E network architecture for the 2020 S-Class maximizes the availability of automotive electronics innovations. Mercedes-Benz further enhances the energy-efficient and cost-effective power supply with the improved 48V system. It prepares for the latest driver assistance and automation systems with a fail-safe 12V power supply. Furthermore, a new vehicle feature experience is defined before and after driving with a “Living” powernet. Since the initial launch in the 222 predecessor, the fully integrated 12V and 48V power network system increases the energy availability for additional comfort and chassis innovations with consumption-reducing EQ hybrid features culminating in the realization of the 223 model.

The development of a dynamically configurable autonomous vehicle is subject to a high degree of complexity. This complexity is further intensified by usually domain-specific thinking and document-based development, leading to difficulties in managing the development processes. Therefore, there is a need for manageable and cross-domain approaches in the development of vehicle systems. For this purpose, Model-based Systems Engineering (MBSE) proposes an approach for modeling systems architectures with the necessary views for the development of vehicles. This contribution presents accordingly a cross-domain system architecture model in the sense of MBSE using the example of a dynamically configurable autonomous vehicle (DCAV) and highlights the resulting advantages for the development process. A DCAV includes an electrically and autonomously driven platform and exchangeable add-on capsules for different use cases. The system architecture model provides here a development environment where requirements on the DCAV, its behavior, and structure as well as their interrelations are described, structured, and unified at several levels for different use cases (e.g. passenger transport). In this way, a comprehensive basis for various development activities (e.g. function-oriented modularisation) is established, which at the same time indicates the great potential of system architecture models for the development of future vehicles.

In addition to the megatrends electrification, automation and connectivity, the whole mobility business model is experiencing substantial transformation through increasing car sharing services, less individual traffic and introducing new environmental protection measures. This leads to new modular vehicle concepts. These new modular and reconfigurable vehicle concepts should also consider the new trends such as automated/autonomous driving, connected vehicles and OTA updates. In addition, high security and safety requirements must be fulfilled. All this leads to an increasing complexity of the Electric/Electronic architecture (E/E-A) for modular vehicles. The adaptation of the existing approaches in the E/E-A design to those becomes challenging especially regarding the aspect of distributed and integrated E/E modules in different vehicle parts. We analyzed existing concepts for E/E-A design targeting the mentioned new trends and showed their limits for application in modular vehicle concepts. Based on that, we conducted a gap analysis to cover the needs for the considered aspects and integrated the results in a generic E/E-A model consisting of drive module, additional vehicle parts and cloud/infrastructure module. The resulting architecture is based on modular distributed services leading to a seamless service-oriented architecture which is able to handle the new challenges.

Climate change is a major challenge not only to Europe but the entire world. To cope with this threat, the European as well as other EU member countries – i.e., Germany – introduced substantial environmental protection regulations. On the EU level, this is called the “Green Deal”, providing an action plan to accelerate the use of renewable resources and hence, “moving to a clean, circular economy, restoring biodiversity and cut pollution”, ultimately reducing net emission to 0 by 2050 [27, 28].

Currently there is an intensive discussion about the use of hydrogen for mobile applications in particular for heavy-duty vehicles. Here, the focus is also on the integration of internal combustion engines into road vehicles and construction machinery. For maximizing engine efficiency and power density direct injection is generally speaking the favorable option for hydrogen combustion engines, although direct hydrogen injection is in a relatively early development stage and it imposes the demand of high supply pressure on the supply system.This paper focusses on possible supply system solutions that are suitable for supplying an ICE (internal combustion engine) with high gas pressures suitable for DI (direct injection). Gaseous and liquid hydrogen storage options that are relatively mature today as well as possible future innovative approaches are reviewed and necessary changes for supply of high pressure in all operational scenarios are presented. The additional efforts are evaluated and quantified in terms of package volume, weight, cost and impact of overall drivetrain efficiency.The components used in each technical solution will be described regarding their functionality; the options of pressurization techniques mentioned above will be explained and finally an outlook on potentially beneficial combinations of hydrogen storage system for supply of high pressure and highly efficient hydrogen DI ICE is presented.

The introduction of synthetic fuels is one approach to reducing CO2 emissions in the transport sector. In this context, synthetic methane is promising due to the high degree of development of the technology and easy substitution in the vehicle fleet. In particular, the existing infrastructure including gas grid, gas storage, and filling stations as well as existing trade mechanisms allow a comparatively fast substitution of fossil natural gas by synthetic methane for light and heavy duty vehicles. This study discusses the direct potential for substitution of parts of the newly registered vehicle fleet with gas vehicles, fueled with synthetic methane, and compares it to the potential of using fuel cell electric vehicles relying on hydrogen. The production path of hydrogen and synthetic methane is analyzed with respect to electricity demand and overall associated CO2 emissions. The result is an estimate of the well-to-wheel CO2 emissions of vehicles fueled with hydrogen or synthetic methane.

In vehicle development, more and more test sequences (diagnostic scripts) are established for function testing of individual components, systems and cross-functional methods. Due to decentralization and the modular approach, modern development vehicles consist of different numbers of electronic control units (ECU). The high number of ECUs in purpose and number pose a challenge for test creation and updating.The ECU software is also developed in cycles within the vehicle cycle. This also results in a very high software variance. This variance leads to the fact that in the vehicle development with global test conditions only one works. The vehicle structure (ECU and their software status) is uncertain, so errors and a longer script runtime must be expected during test execution.Due to this initial situation a concept was developed, which excludes the individual vehicle structure (global pattern) and verifies and stores this supported by an Artificial Intelligence (AI) database. This ensures traceability of the vehicle body at all times. In addition, it is possible to create individualized test sequences for each vehicle and to keep them up to date. Furthermore, the AI is able to identify the user and to generate person-specific test sequences. Finally, the AI evaluates the quality of the measured values in order to provide the ECU developer with a tool to detect discrepancies.

The automotive industry is facing a profound transformation: New functions such as autonomous driving lead to a continuous increase in the complexity of vehicles and the dynamics of development.One solution to face the according challenges for automotive testing is the use of Connected Mixed Reality (CMR). This involves test environments consisting of real and simulated components. The CMR approach combines the strengths of classic HiL systems with the advantages of virtualization solutions.Modern CMR environments are characterized by the following features: Modularization and standardization of all test components (models, ECU setups, environmental simulations) Dynamic configuration of test setups consisting of real ECUs and virtual components Real-time operation from the cloud with real ECUs Common integrated operation of best-in-class solutions Support of interface standards Middleware for coupling best-in-class components The combination of CMR environments with Continuous Integration Systems will allow test cycles to be shortened to an unprecedented extent. An automated triggered test during the check-in of a component will be possible. This contribution presents the current status of the CMR approach in MicroNova’s NovaCarts platform. The focus will be on experiences with the introduction of real-time operation in mixed cloud environments.

Within powertrain system development, testing has a major impact on development duration and cost. The introduction of new technologies, strongly driven by electrification, customization and connectivity, leads to increased testing effort.The paper will present a method based on systems engineering (introducing a systematic description of the powertrain using SysML and a systematic collection and management of the requirements).This enables to introduce automation for testcase generation as well as test automation and the judgement of test results. The whole process of “testing” could be finally automized.

Unsuitable lane-change maneuvers are one of the most common potential safety risks in traffic. Nevertheless, it is a maneuver that must be executed carefully by both human drivers and self-driving cars. Developing a suitable automated driving algorithm for self-driving cars to address the lane changing problem is not straightforward because the problem can only be stated as a high dimension problem with many variables and parameters. Therefore, safe driving needs to be assured by optimal trajectory generating algorithms as well as high-level behavioral planners, which assess the safety of the intended behavior and discard the infeasible trajectory candidates. In this paper, a hybrid approach to a behavioral planner algorithm with lane changing behavior using the Frenet coordinate system was developed to solve the lane changing maneuver problem on roads that consist of road segments with different curvature values. The controllers that need to generate the actuator commands according to the reference trajectory cannot always precisely follow the trajectories. Hence, the waypoint generation algorithm has to adapt to the controller and vehicle dynamics. The road structures for the driving scenarios were modeled according to the OpenDRIVE format and were implemented in a model-based traffic simulation environment MOBATSim. The proposed approach is evaluated and demonstrated by the simulating driving scenarios in the same environment.

This research is a part of publicly funded project – FlexCar, which aims to build an autonomous vehicle. The vehicle offers open source software and construction models and should serve as a platform for rapid development and testing of new technologies. The project strives to bring new solutions for future mobility and overcomes some of existing limitations. The FlexCar platform has symmetrical design - it consists of two drive modules and one energy module between them. Both axles are steered, which allows a small turning radius. Furthermore, each wheel is equipped with one electromotor and can be accelerated separately. This can be used as an advantage for slip based friction estimation. Currently, ADAS functions have restrictions due to bad weather or road condition. With a continuous and driver-independent friction information, it would be possible to improve these functions and make a step towards autonomous driving. This paper shows first results from measurements done on the Handling Roadway test rig. Both, longitudinal and lateral slip are observed during accelerating/braking and steering.

Automotive manufacturers are facing highly dynamic customer demands and shorter life cycles forcing them to continuously upgrade their vehicles by releasing new versions with innovative features. Accordingly, manufacturers have to determine well-conceived release-strategies in order to upgrade existing vehicle models in their portfolio. The influence of accelerated technological progress and the digital transformation of today’s vehicles require novel release strategies in development and use stage of the vehicles. Here, traditional release-strategies of the automotive industry, for instance, by introducing facelifts or special editions, are becoming less effective and very limited compared to other branches such as the software industry. Therefore, this contribution aims to present the current practice of release planning in the automotive industry and the resulting recommendations. For this purpose, a comprehensive study was conducted, analyzing six vehicles from different brands of the upper-range segment in the German automotive industry between the years of 2009–2019 regarding their introduced releases and features as well as their impact on market success. Moreover, interviews were conducted in the automotive industry to gain an overview of the understanding, challenges, needs, and current processes of release planning in practice. Additionally, release strategies and principles from different branches (e.g. smartphone industry) were analyzed and compared. As a result, recommendations for appropriate release planning and strategies to upgrade vehicles were defined for the automotive industry.

Battery simulations are an essential tool for the efficient development of electric powertrains, which require accurate models and reliable hardware. Surprisingly, today’s massively collected measurements are not yet used for realistic and virtual development environments. Among other reasons, handling the large and heterogeneous datasets of automotive batteries still prevents a consequent application. Hence, a data-enhanced electric model of the battery is presented, which relies on a recurrent neural network to compensate the error of a physically-motivated model. Such a hybrid model introduces the high accuracy of machine learning to simulations. Ultimately, it allows a replacement of real batteries with model-based simulators at test benches. The approach is validated based on a comparison of a real battery with a simulator and its different model variants. It is shown, that the data-driven enhancement of existing simulations increases the accuracy while maintaining the robustness of the original model.

The Research Institute of Automotive Engineering and Vehicle Engines (FKFS) already operates a range of state-of-the-art specialist test benches for engine and powertrain applications. Its portfolio covers both conventional drives as well as hybrid and battery electric vehicles. To meet demands for more testing capacity with test bench runs for electrified vehicle powertrains – and to comply with the significantly increased requirements for power, torque and especially dynamics – FKFS has planned, built and started operating a new high-performance electric powertrain test bench (HEP). The HEP is a single-axis test bench, characterized by a variable machine layout enabling setup of different drive and powertrain configurations. For example, it is possible to run test specimens consisting of a vehicle’s electric machine, gearbox and driveshafts, with a power of up to 1160 kW. A high-performance battery simulator provides up to 1600 A, with a voltage range from 0 to 1000 V. The portfolio includes back-to-back setups as well as testing of purely mechanical axles for electric vehicles. A highly dynamic drive machine permits speeds of up to 20,000 rpm, with a maximum power of up to 700 kW. This makes it possible to comply with new requirements coming from politics and automotive development, so we can continue to provide reliably tested drives in the future too.

Test benches are becoming increasingly important in the development of modern vehicles. It does not matter whether the vehicle has a conventional, hybridized or fully electric drive. This trend is further strengthened by shorter development times, cost effectiveness and measures such as Road-to-Rig. In order to generate long running times and thus operate the test bench as effectively as possible, downtimes must be reduced to a minimum. In addition to the interruptions for setup and commissioning work, the downtimes primarily include the time for measuring data analysis in the event of an error. The procedure in the event of an error related shutdown is first of all to isolate the affected components and convert the required measurement data. These are then manually evaluated, categorized and logged by the test bench operator. The approach of the diagnostic tool developed here is the automated pre-evaluation of measurement data in the event of an error, before the test bench operator arrives at the testbench. This offers the possibility of efficient error analysis and support for the test bench operator. Thanks to the AI-based approach, the diagnostic tool learns independently and without intervention from the test bench operator, possible wear-related changes to the components over the course of time.

This paper presents the first real world application of an active output selection strategy, which selects the leading model based on a normalized model quality criterion. The strategy is compared to two other baselines. The algorithm identifies three models of static criteria, which are used for the drivability calibration of the boost maneuver of an 48V HEV. The driving maneuvers are conducted on a powertrain test bench. To validate the results, the experiments were conducted for multiple times. The results confirm analyses on generic toy examples, which indicated great advantages of this learning strategy. In this application example, the strategy saves an amount of 20–65% measurements, depending on which baseline is referenced.

Pragmatically, the ZF vision EVplus is a good solution to close the gap between flexibility, infrastructure, costs and CO2 efficiency. This concept is based on classic plugin hybrid technology with a stronger shift towards an electric vehicle. The primary drivetrain would be the electric one with an electric range of 80 km - enough for everyday mobility. The rare long-distance use cases can be supported by an efficient combustion engine. With this mix of energy sources, it is possible to generate the best of both. Charging infrastructure can be focused on everyday mobility. CO2 emissions over lifetime can be optimized by a high rate of electric driven distances with less battery capacity than necessary for long-range battery electric vehicles. ZF Friedrichshafen AG develops not only pure electric drivetrain solutions but also a sustainable and powerful plugin hybrid solution for EVplus: The 8HP 4th generation. This modular plugin hybrid transmission kit with over 150 kW electric power can cover the requirements of the vision EVplus. The modular approach with mild hybrid and conventional usage ensure high cost efficiency. The fully integrated solution includes the Power Inverter Module as well and fits into standard vehicle architecture.

Modern hybrid powertrains consist of several energy storage and energy converters and allow multiple operating modes to propel a vehicle. In the context of energy management these operating modes are crucial for the consumption of fossil fuel and the recuperation of kinetic energy. A supervisory control strategy is mandatory to meet the driver expectations and to control the energy flow in an efficient manner. Their applicability should cover all possible driving maneuvers, respect component limits and minimize fossil fuel consumption. The “Equivalent Consumption Minimization Strategy” algorithm, as a local optimal control strategy, is derived from literature and applied to a holistic system simulation model of a hybrid powertrain and a thermal management system in the form of an embedded supervisory controller. The objective is to minimize fossil fuel consumption and to include the response dynamics and the thermal effects of the underlying components and subsystems. A special attention is given to the formulation of the cost function which includes three modifications to the well-known [1] equivalent consumption equation. The derived optimal control strategy and the simulation results of the system model are discussed regarding their applicability and the resulting energy economy to an a priori known maneuver. The proposed modifications and extensions prove their applicability in the virtual test environment and recommend themselves for the utilization in further application areas.

The mixed operation between motorized and non-motorized, as well as (partially) autonomous and non-autonomous traffic participants represents a major challenge for a reliable operation of automated and autonomous vehicles. How do road users behave towards autonomous vehicles and which environmental elements (static and dynamic) are relevant for the automated vehicle to interpret the current situation? How can the highest level of road safety be guaranteed? These and other questions cannot be verified and validated by real-world test drives alone, but instead must be answered extensively with the help of simulations.This publication outlines the state-of-the-art of the virtual validation of automated driving functions placing a focus on environment modelling, scenario generation, as well as traffic and driving simulations in urban contexts. The creation of a photorealistic 3D representation of a district in the city of Ingolstadt using high-precision laser scanners and OpenDRIVE maps is described. Furthermore, the generation of test scenarios using the open standard OpenSCENARIO and coupling of the traffic simulator SUMO with the microscopic and submicroscopic vehicle and environment simulation in Tronis® is presented.

Automated and connected driving up to autonomous vehicles is increasingly being deployed. But the public distrust in their reliability is growing and thus concerns on deployment. The underlying algorithms are not transparent, fast changing and easy to manipulate [1,3,8].

The technical complexity of automotive Cyber-Physical Systems (CPS) traditionally demands high development and validation efforts. Due to the new technologies entering the automotive market, such as Highly Automated Driving (HAD) ( $$\ge $$ ≥ SAE L3) and connected infotainment, the overall system complexity is currently increasing significantly, challenging traditional system development methods and requiring new approaches for validation and verification (V&V). In parallel, new Electric/Electronic (E/E) architecture patterns are emerging in the automotive industry, distributing the functionalities across several multi-core Electrical Control Units (ECU) connected via Ethernet-based in-vehicle networks. This distributed approach leads to complex inter- and intra-ECU timing relations challenging the concept of freedom from interference according to the ISO 26262, and adding another dimension of effects analysis during V&V in the context of ISO PAS 21448 and the upcoming ISO TR 4804. This work enhances a cyber-physical functional simulation tool to include timing effects in distributed cause-effect chains and multi-technology-communication networks (incl. Ethernet and CAN). The resulting simulation allows the system designer to evaluate the impact of timing properties on a given distributed vehicle function, enabling an early validation of the system, avoiding rework during later stages of the development process resulting from wrong design choices.

Ensuring continuing environmental and health improvements, it is important regularly to reassess what pollutants from vehicles are targeted. Are the right compounds being regulated? The Emissions Analytics’ presentation looks at a range of pollutant sources that may need to be considered to give a holistic view of the environmental impact of vehicles, supported by data from its independent, real-world EQUA test programme. Post-Euro-6 emissions regulation in Europe is an opportunity to simplify and refocus on emerging environment threats. Certain unregulated tailpipe pollutants, such as ammonia, which contributes to secondary particle formation, are candidates for future regulation. Volatile organic compounds are of interest from several angles: vehicle interior air quality and the off-gassing from materials; tailpipe speciation of hydrocarbons including formaldehyde; and off-gassing from tyres. Tyre wear emissions are currently unregulated but are believed to be a growing contributor to air and marine pollution. Emissions Analytics runs independent test programmes that investigate and quantify real-world exhaust, cabin and tyre pollution. Resulting measurements form the EQUA Index database, which is the source of results presented in this paper.

The diesel engine plays an essential role offering competitive total cost of ownership and in reducing CO2 for manufacturers (OEM) of commercial vehicles and off-road applications - and it will continue to do so in the next years in the context of increasing diversity in the powertrain mix striving for carbon neutrality in the near decades to come.Requirements regarding NOx emissions have been and will remain a major driver for further development of this technology and complying with these requirements will induce further actions on the engine and the exhaust system. SCR-based exhaust-gas treatment (EGT) is the method of choice to support OEMs in achieving future emission requirements.For Bosch, a double injection SCR with closed-loop control based on adequate EGT sensors represents an efficient, robust and targeted method to achieve this goal. As a major part of the SCR system the urea dosing system needs to provide adequate and precise dosing rates to ensure the efficiency of the NOx conversion. Taking On Board Diagnostics requirements into account, not only low dosing tolerances are important, but also an accurate monitoring of potential deviations. In this paper, the key drivers for a new dosing system “Denoxtronic 8” are presented. In the course of developing this optimized system, Bosch profited from years of EGT domain experience and succeeded in implementing newly designed Big Data Analysis methods helping to master the rising numbers of requirements.

A diesel engine with a Lean NOx Trap (LNT) operates alternating in lean and rich mode. In lean mode, the LNT stores the emitted nitrogen oxide emissions (NOx). In rich mode, the engine produces large amounts of carbon monoxide (CO), hydrogen (H2), and hydrocarbons (HC), which reduces the stored NOx in the LNT. Law restricts NOx, CO, and HC emissions. The aim is a low level of these emitters, but they can pass through the LNT. The right timing of lean and rich mode can help to reduce NOx emissions with acceptable CO emissions. A variable valve lift system (VVT), which generates high internal residual gas rates, is considered to extend the operation range in which regeneration is possible. The present work deals with investigating regeneration-timing influences and developing an LNT regeneration strategy for real-driving-emission (RDE) simulation. Used this strategy, the VVT system’s potential is evaluated.

Gaseous fuels have a high CO2-reduction potential and knock resistance due to their chemical and thermal properties, which make them an interesting fuel alternative for the use in modern spark-ignited engines. Within this research project the stoichiometric and homogeneous combustion process with compressed natural gas (CNG) direct injection is investigated in combination with high-load exhaust gas recirculation (EGR).To study the flow characteristics and mixture formation of CNG injection in a combustion chamber, experimental investigations were performed in a low-pressure injection chamber and on an optically accessible single-cylinder engine. The CNG spray investigations in the low-pressure injection chamber were performed with the aim to macroscopically characterize the natural gas jet using the Schlieren technique and planar laser-induced fluorescence (pLIF). A 3D-CFD model was created and validated with the optical single cylinder investigations and a qualitative PIV method at low-end torque and catalyst heating conditions. Both central and lateral injection positions were investigated using PIV and these results proved a good compatibility with the 3D-CFD simulations. Both the optical investigations in the low-pressure chamber and with the motored, optically accessible single-cylinder engine show that the direct natural gas injection with the side injector position has advantages compared to the central position, since the primary and secondary tumble are increased by an early and late injection timing.Experimental studies with the thermodynamic single-cylinder research engine show that the potential to control combustion using EGR is limited, however, an optimum combustion phasing could be maintained despite a lower combustion speed.The dilution capability of CNG was investigated using both EGR and excess air using two compression ratios of CR = 13 and 14.7. The investigations with excess air dilution showed the highest in-crease in indicated efficiency. The CR = 13 configuration allowed an increase of ~2.5%-points and a maximum air/fuel-ratio of λ = 1.5.A 0D/1D engine model was created using a predictive combustion model (SITurb) for the direct injection of natural gas and validated using measurement results. The investigations showed that low-pressure EGR cannot be used for high-load EGR with single supercharging due to reaching the com-pressor map limits. Therefore, a high-pressure EGR system was implemented.A Ford Eco Boost Engine was used for the engine tests and modified for CNG operation. The full-load tests at IMEP = 23 bar showed that no reduction in the cylinder peak pressure could be achieved with cooled EGR at constant center of combustion. The indicated efficiency could be slightly in-creased up to n = 4500 1/min due to the reduced wall heat losses. NOX emissions were significantly reduced over the entire speed range from 25–62% at 5% EGR. However, HC, CH4, and CO emissions increased simultaneously by 6–26%. No knocking occurred throughout the entire full-load test at IMEP = 23 bar. A trend line comparison indicates that the results are transferable to engines with higher peak pressure limits.A longitudinal dynamics simulation of an RDE cycle indicated a reduction of CO2 emissions by 22%.

This study combines advanced optical diagnostics and high-fidelity Direct Numerical Simulations (DNS) to deepen the understanding of wall heat transfer processes in Otto engines under motored and fired conditions. To this end, a combination of optical diagnostics was applied simultaneously: High-resolution Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV) to resolve the velocity boundary layer (BL) above the piston, Thermographic Phosphor Thermometry (TPT) to measure the wall temperature spatially resolved and Laser Induced Fluorescence (LIF) of SO2 to track the evolution of the flame. For the complementing simulations, an entire workflow was developed that employs process calculations (GT-Power), multi-cycle scale-resolving simulations (SRSs), and DNS. Well-calibrated GT-Power models provided boundary conditions for the experimentally validated SRSs, which in turn yielded initial conditions for the DNS. Using initial conditions from the SRSs at intake valve closure, the first ever DNS of a real engine geometry was successfully performed for one motored and one fired compression/expansion stroke. It was seen that momentum and thermal BLs evolve differently: the former are affected by changes in the bulk velocity (large scale tumble motion and its breakdown), while the temperature gradients monotonically follow the increase in pressure/Reynolds number. Both the scaled momentum and thermal BLs do not exhibit a logarithmic region and the law of the wall does not hold. Several sources for deviations thereto, both in momentum and thermal BLs, are extracted. For the reactive case, it was found that the early flame kernel development is significantly affected by the strong convective flow due to tumble and only when the flame is strong enough to counter-balance the strong convection it can propagate against it. A criterion has further been developed, which allows for distinction between head-on and side-wall quenching. The vast amount of high-fidelity experimental and fully resolved numerical data generated in this project provides a comprehensive database for validation of existing computational fluid dynamics (CFD) tools and can be used for the development of improved wall heat flux models. A first attempt has been made towards this direction by developing an algebraic wall heat transfer model for LES using a data-driven approach.

Homogeneous lean or diluted combustion can significantly increase the efficiency of SI engines. Active fuelled Pre-Chamber ignition systems can overcome the problem that common spark ignitions systems are incapable to ignite strongly diluted mixtures. In the research project LEANition, an active Pre-Chamber with an integrated pressure transducer was developed. The active fuelling is done by volatile components of gasoline fuel using an in-house developed fuelling system. To understand the combustion processes with active Pre-Chamber ignition, optical investigations of the flame luminescence are performed inside an optical accessible engine through a piston crown window. To record single cycles with high temporal resolution, an highspeed intensifier and a highspeed camera was used to reach an imaging frequency of 36 kHz. The selected engine speed is 1200 rpm. Both stochiometric composition and lean charges at Lambda = 1.8 in the cylinder are investigated regarding the ignition and combustion processes. In lean cylinder environments the jets from the Pre-Chamber entrain the surrounding gas and the ignition expands from the center of the jets to the remaining cylinder. In contrast to this, at stochiometric cylinder charges, the jets cross the cylinder bore at high speed, the cylinder combustion is induced from the outside of the cylinder bore inwards to the middle of the bore. Increased charge motion in the cylinder with imposed swirl decreases the combustion duration as the flame front reaches the area between the reactive jets faster.

Pressure measurements in hydraulic systems and components are normally indispensable for the development and reliable operation and are mostly without alternatives. In 2017, results were presented that illustrate a relation between the measured value and the selection of the sensor including its adaptation. Deviations of more than 100% between several measurement signals were detected. Further analysis is necessary to clarify the relationships and causes.This article provides detailed insights into a cavitation-like fluid mechanical effect based on the influence of the sensor adaption on the pressure traces inside the damper or high-pressure chamber in hydraulic chain tensioners. Results from measurements in complete engine operation as well as experiments on an engine dummy address the difficulties in development that can arise from distorted measurement values. Previously undiscovered effects of sensor and plug on the hydraulic and mechanical behavior of the entire system are documented as well.In 2017, component tests provided evidence that an interaction of: I. geometry of the hydraulic component, II. system dynamics and III. volatile (local) gas content are the cause. The research from 2019 confirms this without any doubt. Based on these results, a 3D-CFD simulation was developed, which enabled an analysis of the different forms of interactions in individual system areas – both by external stimulations and by varying fluidic elasticities.This result could not have been achieved without the intensive exchange between the areas: developments on behalf of the customer, component tests and simulation, as this article proves.

With the implementation of the Euro 6d-Temp legislation, the measurement of pollutant emissions under real driving conditions (RDE) has been put more into focus. It can be assumed that with the introduction of future emission standards, other components such as unburned hydrocarbons (HC) will be regulated. The demand for a mobile HC measurement forms the basis of this work to develop a suitable principle. In contrast to common measurement systems for determining hydrocarbon concentration such as flame ionisation detectors (FID), the presented principle works without a flame and a hydrogen supply. Through numerical simulations and experiments a prototype based on the patented novel sensor principle was developed as part of a research project. This sensor contains a temperature-controlled glow plug whose temperature is significantly lower than the flame of an FID, but which nevertheless causes ion formation in HC-containing gases, which is measurable and correlates with the HC concentration. First tests with the prototype show reproducible and HC-dependent ion currents. Based on these promising measurements, further research and development is underway to determine the potential of the sensor principle with regard to its suitability as a measurement technique for RDE.

Developing and testing autonomous driving (AD) systems requires the analysis and storage of more data than ever before. Clients who can deliver insights faster while managing rapid infrastructure growth will be the industry leaders. To deliver these insights faster, the underlying IT technology must support both new big data as well as traditional applications with security, reliability, and high-performance. To handle massive, unstructured data growth, the solution must scale seamlessly while matching data value to the capabilities and costs of different storage tiers and types. This whitepaper covers IBM solutions for AI workloads with a focus on IBM Storage for AI with NVIDIA DGX support.

The automotive OEMs and their suppliers are facing challenging times which are further reinforced by COVID-19. Due to technological breakthroughs and innovation such as the increasing level of automation and connectivity of the vehicles, new risks and threats arise which in the worst case may jeopardize human safety. Cyber-attacks in the automotive industry have drastically increased over the last years making Cyber Security a crucial topic. To tackle this issue, UNECE WP.29 provides a new disruptive legal framework with a tight timeline to implement. Among others, the framework addresses the implementation of a Cyber Security Management System (CSMS), a Software Update Management System (SUMS) and the Automated Lane Keeping System (ALKS). It has an impact on the organization of the OEMs and poses challenges to transform their current development and production processes and to build up the post-production activities. Dedicating resources to Cyber Security does not only have the goal to be compliant, but it can also be a differentiator. With a step-by-step pragmatic and adaptive approach, a holistic security strategy can be achieved, Cyber Security challenges appropriately met and the compliance to WP.29 ensured at acceptable costs.

A novel method of analyzing and controlling hybrid systems has been developed. Utilizing engine operating maps along with hybrid electrical system efficiency data, the actual cost or savings of fuel related to the generation or usage of electric energy can be calculated. These calculations can be used to analyze a vehicle’s operation, or they can produce optimized Electric Fuel Savings (EFS) or Electric Fuel Cost (EFC) maps which can be used for the supervisory control of a hybrid propulsion system. This method is similar in concept to the well-known Equivalent Consumption Minimization Strategy (ECMS) but has some unique attributes. The operating strategy is less computationally demanding than ECMS and tends to be more intuitive which lends itself well to system analysis and calibration.

For the new Rosenbauer Revolutionary Technology (RT) fire truck, the developers at Rosenbauer didn’t just improve an existing vehicle, instead reconsidered the entire fire truck concept. The objective was to conceive the future of firefighting vehicles. And finally this goal was achieved: The groundbreaking innovations make the RT the most modern emergency vehicle in the world. The firefighting truck of the future. This paper take a look on the Rosenbauer RT, and an insight into the history of the project from the first prototype to the serial product. It shows the architecture of the hybrid drive train and the advantages of the system architecture including the degrees of freedom, which are possible with a hybrid drive train.In the second part of the paper, the Range Extender and the aftertreatment system get a spotlight. Explaining why this special combustion engine is necessary in a heavy-duty hybrid firefighting truck and how it is possible to fulfill the emission standard in a not common project.

This paper describes a methodology for the design and dimensioning of a hybridized powertrain for heavy-duty vehicles. It is based on a two-stage approach in which a suitable hybrid configuration is first found by pure simulation with the aid of a genetic algorithm and then a multi-criterial optimization is carried out on the Engine-in-the-Loop test bench with the inclusion of DoE. In this process, the electrical components of the hybrid powertrain are optimally dimensioned. In principle, the presented methodology is open to changing boundary conditions and target variables, so that it is ideally suited as a tool for the design of hybrid powertrains. A variable level of detail can be specified to achieve an optimal result for each application. In addition, an exemplary optimization is presented herein, in which initially only the reduction of the CO2-equivalent during operation is included as a cost function. In the second step, nitrogen oxides and particulate emissions are additionally considered. The example is based on a real existing and previously measured long-haul truck.

The control of the driver’s steering torque of electromechanical power steering systems is state of the art. However, due to nonlinear characteristics and degrees of freedom of the plant which are unconsidered in the control design, the challenge still is the robust implementation of this control approach. Therefore, a new control approach was presented in previous papers that solves this robustness problems. In this paper, the control approach is analyzed in an augmented simulation environment. It is demonstrated that this control approach ensures a high robustness even when simulating critical driving situations. In addition, the control approach allows a nearly unconstrained design of the steering feel.Based on a detailed nonlinear model of an electromechanical power steering, a reduced linearized model for the design of an optimal state space controller and an optimal state space observer is derived. Furthermore, a feeling system is presented. It determines the requested steering torque that the driver should feel at the steering wheel. This feeling system allows that the driver can experience an almost freely configurable steering feel. Moreover, a vehicle model with tire models and a driver model are added to the control system. Thus, real driving situations can be simulated. The results of the analysis of these real driving situations are presented in this paper. It is shown that the control approach ensures good dynamic characteristics and good robustness characteristics.

Hardware-in-the-Loop (HiL) tests are common practice in the development process of “Electric Power Steering – EPS” systems. The HiL tests are carried out on power pack test benches, which emulate the real environment, that an EPS motor is exposed to in a real vehicle.This paper describes highly dynamic HiL tests of an EPS motor using the power pack test bench of [1] To precisely emulate the real environment of the EPS motor, the detailed steering model of [2] is used and moreover embedded in a vehicle dynamics model. The resulting HiL system consists of the detailed steering model, the vehicle dynamics model, the power pack test bench, and the corresponding test bench actuator control. The HiL system is used to perform the highly dynamic HiL tests and allows realistic parameter sets for every involved model.During the HiL tests, the fishhook and the double lane-change are performed. Both are standardized road tests, that include a severe change of the driving direction of the vehicle and are hence highly dynamic excitations of the EPS system. Consequently, the measurement results of both HiL tests are used to prove the performance of the power pack test bench in highly dynamic HiL tests.

Extensive safety assurance for automated driving functions is a prerequisite to allow automated vehicles on the road. However, test drives with failure scenarios must ensure safety of the tests and test personal involved. In this paper, a novel validation environment on a steerable VEhicle-in-the-Loop (VEL) test bench is developed. Previous vehicle test benches usually only offer the possibility to represent load profiles in longitudinal direction. On the VEL test bench, the aligning torque is also simulated, so that the steering system of vehicles can also be loaded correctly, and simulated vehicle behaviors are more realistic. In addition, an environment simulation module provides sensor signals like Lidar and Camera to the vehicle control unit. This allows the testing of high-level algorithms like trajectory planning or track control on the test bench. One scenario with degraded vehicle steering motor has been carried out in this validation environment to demonstrate its application.

The increasing number of functions in modern vehicle leads to an exponential increase in software complexity. The validity and reliability of all components must nevertheless be ensured, making the use of appropriate vehicle diagnostics systems indispensable. The demands on the software of those systems have become dynamic and multifaceted. This paper proposes the approach of service-oriented software architecture as an answer to increased complexity and flexibility of a vehicle diagnostic system.In order to offer competitive diagnostic tools, new requirements must be implemented and offered as quickly as possible. Instead of a monolithic software package that is fixed when the tester is delivered, the diagnostic system breaks down complex functionality into “services” that are less restrictively interconnected. It is important to have a forward-looking design that defines the interfaces and leaves room for changes later-on.Strict modularization made it possible to realize a new type of vehicle diagnostic system that can offer versatile tester functions such as interactive vehicle diagnostics or monitoring and recording of vehicle CAN-bus and other communication and reaction to events. These functions are independent and run in parallel, and can thus be optimally adapted to customer requirements.

The automotive industry is undergoing an unprecedented transformation. From new E/E architectures to central vehicle computers, new developments are emerging to deal with increasing complexity and a growing number of functions. As well as making increasing use of affordable consumer IT technologies, vehicle software is evolving into a living object that receives a continuous stream of updates and even functional enhancements. This is creating a new world of entirely new business models in which conventional development methods are reaching their limits. A vehicle exists both as a physical object in the real world and as a digital model, or “digital twin”, each of which is closely synchronized with the other. Development and operations cycles are increasingly merging in a way that would be impossible without virtualization and modern development approaches such as continuous deployment. Bosch and its subsidiary ETAS are shaping this new world. They offer a Software-in-the-Loop (SiL) suite that links the various elements of virtualization to the required development tools in a modular and flexible manner. Coordinated interactions between the key elements of the SiL framework ensure the quality and usability of the virtual world elements. These include, for example, virtual ECUs that guarantee realistic behavior, a powerful simulation and integration tool, virtual networks, and validation through the monitoring of system behavior in the field. Virtualization at the complete-vehicle level is no longer a vision, but a reality that is already used by OEMs.