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An advanced multi-layer material model has been developed to simulate the complex behavior in case-carburized gears where hardness dependent strength and elastic-plastic behavior is characterized. Also, an advanced fatigue model has been calibrated to material fatigue tests over a wide range of conditions and implemented in FEMFAT software for root bending fatigue life prediction in differential gears. An FEA model of a differential is setup to simulate the rolling contact and transient stresses occurring within the differential gears. Gear root bending fatigue life is predicted using the calculated stresses and the FEMFAT fatigue model. A specialized rig test is set up and used to measure the fatigue life of the differential over a range of load conditions. Root bending fatigue life predictions are shown to correlate very well with the measured fatigue life in the rig test.

In view of BS-VI emission norms implementation in Commercial Vehicle (CV) application, the emissions are not only confirmed in standard condition but also in non-standard condition including different combinations of ambient temperature and pressure especially for checking the emission in WNTE cycle. However, achieving the emissions in different environmental conditions require physical emission calibration to be performed in those conditions. Hence, engine must be calibrated in climatic test chambers to ensure emission in different climatic conditions leading to multifold increase in the calibration effort. With addition of BS-VI emission regulation, After Treatment System (ATS) is a mandatory requirement to reduce the tail pipe emissions. Efficient functioning of ATS requires enough heating to convert the engine out emissions. Vehicle level Real Drive Emission (RDE) measurement without Conformity Factor (CF) limitation are added as an important legislative requirement.

This paper gives insights in the theoretical measurement uncertainty of E-Drive rotor position dependent results, like Id and Iq calculations, done by a modern propulsion power analyzer (PA). The calculation of Id and Iqis fundamental to perform control optimization and application tasks for an E-Drive system. To optimize the E-Drive system application towards e.g., best efficiency, best performance, or improved NVH the importance of the testing toolchain is described: a power analyzer delivering the required results, an automation system, and a Design of Experiment tool to set improved target values. Consequently, inverters applications featuring field-oriented control (FOC) with permanent magnet synchronous machines (PMSM) are updated with a chosen control strategy. For achieving a certain behavior of an E-Drive, different degrees of freedom in the Inverter Control Unit are available; Lookup tables Id and Iq represent two fundamental application labels to be considered.

The major objective of this paper is to develop thermal management strategy targeting optimum performance of Selective Catalytic Reduction (SCR) catalyst in a Medium Duty Diesel Engine performing in BS6 emission cycles. In the current scenario, the Emissions Norms are becoming more stringent and with the introduction of Real Drive Emission Test (RDE) and WHTC test comprising of both cold and hot phase, there is a need to develop techniques and strategies which are quick to respond in real time to cope with emission limit especially NOx. SCR seems to be suitable solution in reducing NOx in real time. However, there are limitations to SCR operating conditions, the major being the dosing release conditions which defines the gas temperature at which DEF (Diesel Exhaust Fluid) can be injected as DEF injection at lower gas temperatures than dosing release will lead to Urea deposit formation and will significantly hamper the SCR performance.

The introduction of autonomous vehicles has gained significant attention due to its potential to revolutionize mobility and safety. A critical aspect underpinning the functionality of these autonomous vehicles is their sensor perception system. Demonstrating the reliability of the environment perception sensors and sensor fusion algorithms is, therefore, a necessary step in the development of automated vehicles. Field tests offer testing conditions that come closest to the environment of an automated vehicle in the future. However, a significant challenge in field tests is to obtain a reference truth of the surrounding environment. Here, we propose a pipeline to assess the sensor reliabilities without the need for a reference truth. The pipeline uses a model to estimate the reliability of redundant sensors. To do this, it relies on a binary representation of the surrounding area, which indicates either the presence or absence of an object.

This paper provides an overview of possible engine design optimizations by utilizing highly knock-resistant potential greenhouse gas (GHG) neutral synthetic fuels. Historically the internal combustion engine was tailored to and highly optimized for fossil fuels. For future engine generations one of the main objectives is to achieve GHG neutrality. This means that either carbon-free fuels such as hydrogen or potential greenhouse gas neutral fuels are utilized. The properties of hydrogen make its use challenging for mobile application as it is very diffusive, not liquid under standard temperature/pressure and has a low volumetric energy density. C1-based oxygenated fuels such as methanol (MeOH), dimethyl carbonate (DMC) and methyl formate (MeFo) have properties like conventional gasoline but offer various advantages. Firstly, these fuels can be produced with renewable energy and carbon capture technologies to be GHG neutral.

In order to predict reality as accurately as possible leads to the fact that numerical models in automotive vibroacoustic problems become increasingly high dimensional. This makes applications with a large number of model evaluations, e.g. optimization tasks or uncertainty quantification hard to solve, as they become computationally very expensive. Engineers are thus faced with the challenge of making decisions based on a limited number of model evaluations, which increases the need for data-efficient methods and reduced order models. In this contribution, variational autoencoders (VAEs) are used to reduce the dimensionality of the vibroacoustic model of a vehicle body and to find a low-dimensional latent representation of the system.

To avoid frequent regeneration intervals leading to expeditious ageing of the catalyst and substantial fuel penalty for the owner, it is always desired to estimate the soot coming from diesel exhaust emission, the soot accumulated and burnt in the Diesel Particulate Filter (DPF). Certain applications and vehicle duty cycles cannot make use of the differential pressure sensor for estimating the soot loading in the DPF because of the limitations of the sensor tolerance and measurement accuracy. The physical soot model is always active and hence a precise and more accurate model is preferred to calibrate & optimize the regeneration interval. This paper presents the approach to estimate the engine-out soot and the accumulated soot in the DPF using a graphical calculation tool (AVL Concerto CalcGraf™).

Current developments in automotive industry such as hybrid powertrains and the continuously increasing demands on emission control systems, are pushing complexity still further. Validation of such systems lead to a huge amount of test cases and hence extreme testing efforts on the road. At the same time the pressure to reduce costs and minimize development time is creating challenging boundaries on development teams. Therefore, it is of utmost importance to utilize testing and validation prototypes in the most efficient way. It is necessary to apply high levels of instrumentation and collect as much data as possible. And a streamlined data pipeline allows the fleet managers to get new insights from the raw data and control the validation vehicles as well as the development team in the most efficient way. In this paper we will demonstrate a data-driven approach for validation testing.

In this study a mixture of dimethyl carbonate (DMC) and methyl formate (MeFo) was used as a synthetic gasoline replacement. These synthetic fuels offer CO2-neutral mobility if the fuels are produced in a closed CO2-cycle and they reduce harmful emissions like particulates and NOX. For base potential investigations, a single-cylinder research engine (SCE) was used. An in-depth analysis of real driving cycles in a series 4-cylinder engine (4CE) confirmed the high potential for emission reduction as well as efficiency benefits. Beside the benefit of lower exhaust emissions, especially NOX and particle number (PN) emissions, some additional potential was observed in the SCE. During a start of injection (SOI) variation it could be detected that a late SOI of DMC/MeFo has less influence on combustion stability and ignitability. With this widened range for the SOI the engine application can be improved for example by catalyst heating or stratified mode.

A reliable environment perception is a requirement for safe automated driving. For evaluating and demonstrating the reliability of the vehicle’s environment perception, field tests offer testing conditions that come closest to the vehicle’s driving environment. However, establishing a reference ground truth in field tests is time-consuming. This motivates the development of a procedure for learning the vehicle’s perception reliability from fleet data without the need for a ground truth, which would allow learning the perception reliability from fleet data. In Berk et al. (2019), a method based on Bayesian inference to determine the perception reliability of individual sensors without the need for a ground truth was proposed. The model utilizes the redundancy of sensors to learn the sensor’s perception reliability. The method was tested with simulated data.

The synthetic fuels dimethyl ether (DME) and polyoxymethylene dimethylether (POMDME or OME) are promising oxygenated fuels to meet the rising challenges of air pollution control, CO2-neutrality, and sustainability. The sootless combustion and high ignitability of DME and OME represent ideal properties for an application in diesel engines. However, recent investigations of oxygenates reported an increase of nanoparticles, which are known to have fatal effects on human’s health. Besides nanoparticles, ongoing discussions about future emission legislation focus on a drastic reduction of NOx. For this reason, the present work investigates different measures to reduce NOx emissions using DME/OME and a paraffinic diesel fuel (PDF) as reference. Different rail pressures, exhaust gas recirculation (EGR) rates, and injection timings are evaluated, considering the effectivity on NOx reduction and the impact on other emissions, especially on nanoparticles.

Polyoxymethylene dimethyl ethers (OME) are synthetic fuels, which offer the property of sustainability because the reactants of production base on hydrogen and carbon dioxide on the one hand, and the air pollution control in consequence of a soot-free combustion in a diesel engine on the other hand. High exhaust gas recirculation (EGR) rates are a promising measure for nitrogen oxide (NOx) reduction without increasing particle emissions because of the resolved soot-NOx trade-off. However, EGR rates towards stoichiometric combustion in OME operation reveals other trade-offs such as methane and formaldehyde emissions. To avoid these, a lean mixture with a combination of EGR and exhaust after-treatment with selective catalytic reduction (SCR) is useful. The limitation of urea dosing due to the light-off temperature of SCR systems requires heating measures.

The latest generation of internal combustion engines may emit significant levels of sub-23 nm particles. The main objective of the Horizon 2020 “DownToTen” project was to develop a robust methodology and provide policy recommendations towards the particle number (PN) emissions measurements in the sub-23 nm region. In order to achieve this target, a new portable exhaust particle sampling system (PEPS) was developed, being capable of measuring exhaust particles down to at least 10 nm under real-world conditions. The main design target was to build a system that is compatible with current PMP requirements and is characterized by minimized losses in the sub-23 nm region, high robustness against artefacts and high flexibility in terms of different PN modes investigation, i.e. non-volatile, volatile and secondary particles.

Beside the main trend technologies such as downsizing, down speeding, external exhaust gas recirculation, and turbocharging in combination with Miller cycles, the optimization of the mechanical efficiency of gasoline engines is an important task in meeting future CO2 emission targets. Friction in the piston assembly is responsible for up to 45% of the total mechanical loss in a gasoline engine. Therefore, optimizing piston assembly friction is a valuable approach in improving the total efficiency of an internal combustion engine. The form honing process enables new specific shapes of the cylinder liner surface. These shapes, such as a conus or bottle neck, help enlarge the operating clearance between the piston assembly and the cylinder liner, which is one of the main factors influencing piston assembly friction.

Fuel injection is a key process influencing the performance of Gasoline Direct Injection (GDI) Engines. Injecting fuel at elevated temperature can initiate flash boiling which can lead to faster breakup, reduced penetration, and increased spray-cone angle. Thus, it impacts engine efficiency in terms of combustion quality, CO2, NOx and soot emission levels. This research deals with modelling of flash boiling processes occurring in gasoline fuel injectors. The flashing mass transfer rate is modelled by the advanced Hertz-Knudsen model considering the deviation from the thermodynamic-equilibrium conditions. The effect of nucleation-site density and its variation with degree of superheat is studied. The model is validated against benchmark test cases and a substantiated comparison with experiment is achieved.

Laser induced fluorescence (LIF) is used to investigate oil transport mechanisms under real engine conditions. The engine oil is mixed with a dye that can be induced by a laser. The emitted light intensity from the dye correlates with the residual oil at the sensor position and the resulting oil film thicknesses can be precisely determined for each crank angle. However, the general expectation is not always achieved, e.g. an exact representation of piston ring barrel shapes. In order to investigate the responsible lubrication effects of this behavior, a new cavitation algorithm for the Reynolds equation has been developed. The solution retains the mass conservation and does not use any switch function in its mathematical approach. In contrast to common approaches, no vapor-liquid ratio is used, but one or several bigger bubbles are approximated, as have been observed in other experiments already.

Diesel engines are arguably the superior device in the ground transportation sector in terms of efficiency and reliability, but suffer from inferior emission performance due to the diffusive nature of diesel combustion. Great research efforts gradually reduced nitrogen oxide (NOX) and particulate matter (PM) emissions, but the PM-NOX trade-off remained to be a problem of major concern and was believed to be inevitable for a long time. In the process of engine development, the modification of fuel properties has lately gained great attention. In particular, the oxygenate fuel oxymethylene ether (OME) has proven potential to not only drastically reduce emissions, but possibly resolve the formerly inevitable trade-off completely.

In the context of today’s and future legislative requirements for NOx and soot particle emissions as well as today’s market trends for further efficiency gains in gasoline engines, computational fluid dynamics (CFD) models need to further improve their intrinsic predictive capability to fulfill OEM needs towards the future. Improving fuel chemistry modelling, knock predictions and the modelling of the interaction between the chemistry and turbulent flow are three key challenges to improve the predictivity of CFD simulations of Spark-Ignited (SI) engines. The Flamelet Generated Manifold (FGM) combustion modelling approach addresses these challenges. By using chemistry pre-tabulation technologies, today’s most detailed fuel chemistry models can be included in the CFD simulation. This allows a much more refined description of auto-ignition delays for knock as well as radical concentrations which feed into emission models, at comparable or even reduced overall CFD run-time.

The thermal and emission efficiency of diesel engines depends to a large extent on the quality of fuel injection. However, over engine lifetime, injection rate and quality will change due to adverse nozzle aging effects, such as coking or cavitation. In this study, we discuss the influences of these effects on injection and heat release rate. The injection rates of previously unused nozzles and a nozzle that had been operated in a vehicle engine were compared in order to clarify the impact of aging effects. The key to the detection of alterations of injection nozzles is the identification of strongly correlating parameters. As a first step, an instrumented injector was set up to measure fuel pressure inside the feed line of the injector and the lift of the control piston. Different nozzles showed a distinguishable control piston motion depending on their different geometric specifications, which also affect the injection rates.