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The particulate emissions of two brake systems were characterized in a dilution tunnel optimized for PM10 measurements. The larger of them employed a fixed caliper (FXC) and the smaller one a floating caliper (FLC). Both used ECE brake pads of the same lining formulation. Measured properties included gravimetric PM2.5 and PM10, Particle Number (PN) concentrations of both untreated and thermally treated (according to exhaust PN regulation) particles using Condensation Particle Counters (CPCs) having 23 and 10 nm cut-off sizes, and an Optical Particle Sizer (OPS). The brakes were tested over a section (trip-10) novel test cycle developed from the database of the Worldwide harmonized Light-Duty vehicles Test Procedure (WLTP). A series of trip-10 tests were performed starting from unconditioned pads, to characterize the evolution of emissions until their stabilization. Selected tests were also performed over a short version of the Los Angeles City Cycle.

The increasing complexity of distributed embedded systems, as found today in airplanes or cars, becomes more and more a critical cost-factor for their development. Model-based approaches have recently demonstrated their potential for both improving and accelerating (software) development processes. Therefore, in the project DECOS1, which aims at improving system architectures and development of distributed safety-critical embedded systems, an integrated, model-driven tool-chain is established, accompanying the system development process from design to deployment. This paper gives an overview of this tool-chain and outlines important design decisions and features.

At the moment the documentation of failure inhibition matrices and the fault path management for different controller types and different vehicle projects are mainly maintained manually in individual Excel tables. This is not only time consuming but also gives a high potential for fault liability. In addition there is also no guarantee that the calibration of these failure inhibition matrices and its fault path really works. Conflicting aims between costs, time and fault liability require a new approach for the calibration, documentation and testing of failure inhibition matrices and the complete Diagnostic System Management (DSM) calibration. The standardization and harmonization of the Diagnostic System Management calibration for different calibration projects and derivates is the first step to reduce time and costs. Creating a master calibration for the conjoint fault paths and labels provides a significant reduction of efforts.

The present contribution gives an overview of the use of different simulation tools for the optimization of injection parameters of a diesel engine. With a one-dimensional tool, the behavior of the mechanics and fluid dynamics of the entire injection system is calculated. This simulation provides information on the dynamic needle lift, injection rates, pressures, etc. The flow within the injector is simulated using a three-dimensional CFD tool. By use of a two-phase model, it is possible to analyze the cavitating flow inside the injector and to calculate the effective nozzle hole area as well as the exit flow characteristics. Mixture formation, combustion and pollutant formation simulation is performed adopting three-dimensional CFD. In order to provide the initial and boundary conditions for the engine CFD simulation and to optimize the engine cycle performance a one-dimensional tool is adopted.

The paper gives an overview of the partially extremely complex problem when looking into commonalities and differences of the three main application areas of engines and powertrains - automotive, agricultural tractors, and industrial engines, the last being predominantly but not exclusively focused on construction equipment. The modern “platform” approach has been used in the automotive world to a large extent and the learned experiences may be of interest for the agricultural tractors and/or the construction equipment manufacturers. On the other hand the truck engine engineers and manufacturers will learn more about the special requirements of the tractor and the industrial engines fields, and thus influence concepts and development procedures and also the production of the automotive engines which in many cases serve as the basis for derivate engines.

This paper presents an overview of the results on heavy duty engines collected in the “PARTICULATES” project, which aimed at the characterization of exhaust particle emissions from road vehicles. The same exhaust gas sampling and measurement system as employed for the measurements on light duty vehicles [1] was used. Measurements were made in three labs to evaluate a wide range of particulate properties with a range of heavy duty engines and fuels. The measured properties included particle number, with focus separately on nucleation mode and solid particles, particle active surface and total mass. The sample consisted of 10 engines, ranging from Euro-I to prototype Euro-V technologies. The same core diesel fuels were used as in the light duty programme, mainly differentiated with respect to their sulphur content. Additional fuels were tested by some partners to extend the knowledge base.

People have been altering the atmosphere on a small scale ever since they learned to make fire. Today's air pollution can influence ecosystems and transform climate worldwide. Motorized transport has become essential, today about 1000 million vehicles are on the world's roads [1]. Vehicle registrations are still sharply upward, where the future growth is most rapid in Asia and Latin America. Over the past, global pollution concerns have increased and air quality targets have been established. Also the reduction of green house gases like CO2 (Kyoto protocol) is considered. Aligned with such air quality targets automotive emission limits have been implemented. The future emission limits will require advanced engine technologies, but will also require adjustments to the measurement technologies. Furthermore new trends in the emission legislation will increase test requirements to represent the real world conditions in a more realistic way.

The recent growth of available computational resources has enabled the automotive industry to utilize unsteady Computational Fluid Dynamics (CFD) for their product development on a regular basis. Over the past years, it has been confirmed that unsteady CFD can accurately simulate the transient flow field around complex geometries. Concerning the aerodynamic properties of road vehicles, the detailed analysis of the transient flow field can help to improve the driving stability. Until now, however, there haven’t been many investigations that successfully identified a specific transient phenomenon from a simulated flow field corresponding to driving stability. This is because the unsteady flow field around a vehicle consists of various time and length scales and is therefore too complex to be analyzed with the same strategies as for steady state results.

The development of modern combustion engines (spark ignition as well as compression ignition) for vehicles compliant with future oriented emission legislation (BS6, Euro VI, China 6) has introduced several technologies for improvement of both fuel efficiency as well as low emissions combustion strategies. Some of these technologies as there are high pressure multiple injection systems or sophisticated exhaust gas after treatment system imply substantial increase in test and calibration time as well as equipment cost. With the introduction of 48V systems for hybridization a cost- efficient enhancement and, partially, an even attractive alternative is now available. An overview will be given on current technologies as well as on implemented test procedures. The focus will be on solutions which have potential for the Indian market, i.e. solutions which can be implemented with moderate application effort for currently available compact and medium size cars.

Over the past years, the question as to what may be the powertrain of the future has become ever more apparent. Aiming to improve upon a given technology, the internal combustion engine still offers a number of development paths in order to maintain its position in public and private mobility. In this study, an innovative combustion process is investigated with the goal to further approximate the ideal Otto cycle. Thus far, similar approaches such as Homogeneous Charge Compression Ignition (HCCI) shared the same objective yet were unable to be operated under high load conditions. Highly increased control efforts and excessive mechanical stress on the components are but a few examples of the drawbacks associated with HCCI. The approach employed in this work is the so-called Spark Assisted Compression Ignition (SACI) in combination with a pre-chamber spark plug, enabling short combustion durations even at high dilution levels.

Icing is a major hazard for aviation safety. Over the last decades an additional risk has been identified when flying in clouds with high concentrations of ice-crystals where ice accretion may occur on warm parts of the engine core, resulting in engine incidents such as loss of engine thrust, strong vibrations, blade damage, or even the inability to restart engines. Performing physical engine tests in icing wind tunnels is extremely challenging, therefore, the need for numerical simulation tools able to accurately predict ICI (Ice Crystal Icing) is urgent and paramount for the aeronautics industry, especially regarding the development of new generation engines (UHBR = Ultra High Bypass Ratio, CROR = Counter rotating Open Rotor, ATP = Advanced Turboprop) for which analysis methods largely based on previous engines experience may be less and less applicable. The European research project MUSIC-haic has been conceived to fill this gap and has started in September 2018.

Deep neural network models have been widely used for environment perception of intelligent vehicles. However, due to models’ innate probabilistic property, the lack of transparency, and sensitivity to data, perception results have inevitable uncertainties. To compensate for the weakness of probabilistic models, many pieces of research have been proposed to analyze and quantify such uncertainties. For safety-critical intelligent vehicles, the uncertainty analysis of data and models for environment perception is especially important. Uncertainty estimation can be a way to quantify the risk of environment perception. In this regard, it is essential to deliver a comprehensive survey. This work presents a comprehensive overview of uncertainty estimation in deep neural networks for environment perception of intelligent vehicles.