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Zero-dimensional heat release rate models have the advantage of being both easy to handle and computationally efficient. In addition, they are capable of predicting the effects of important engine parameters on the combustion process. In this study, a zero-dimensional combustion model based on physical and chemical sub-models for local processes like injection, spray formation, ignition and combustion is presented. In terms of injection simulation, the presented model accounts for a phenomenological nozzle flow model considering the nozzle passage inlet configuration and an approach for modeling the characteristics of the Diesel spray and consequently the mixing process. A formulation for modeling the effects of intake swirl flow pattern, squish flow and injection characteristics on the in-cylinder turbulent kinetic energy is presented and compared with the CFD simulation results.

Introducing new exhaust-gas aftertreatment concepts at mass production level places exacting demands on the overall development process - from defining process engineering to developing and calibrating appropriate control-unit algorithms. Strategies for operating and controlling exhaust-gas aftertreatment components, such as oxidation and selective catalytic reduction catalysts (DOC and SCR), diesel particulate filters (DPF) and SCR on DPF systems (SCR/DPF), have a major influence on meeting statutory exhaust-emission standards. Therefore it is not only necessary to consider the physical behavior of individual components in the powertrain but also the way in which they interact as the basis for ensuring efficient operation of the overall system.

The paper shows the possibilities of using a test management system to combine test tasks and test stages and to control test tools in a varied and manageable way. This makes it possible to optimize test procedure efficiency by applying seamless processes. The following provides a comparison between contemporary procedures and the many different possibilities offered by this type of process configuration.

The increasing complexity of automotive design includes elementary aspects such as lighting. In order to validate modern light systems, new approaches must be developed. Innovative solutions are provided with an indoor lighting facility which performs tests under ideal and repeatable environmental conditions, as well as a ground-breaking approach in evaluating the static and dynamic aspects of lighting. This combination enables the simulation of traffic scenarios and the stimulation of lighting assistance systems.

IAV GmbH is currently developing a test case generator, which uses information from Simulink®/Stateflow® models to generate test cases automatically. These test cases can then be applied during software tests for an ECU to show conformance to the original model. Using predefined rules, test cases for individual blocks are generated and converted into test cases for a whole model. The test cases can be saved as a XML file. Then, this file can be converted into test script languages which are used by tools for test execution. With the test case generator, the time-consuming and error-prone task of manual test case definition can be automated, thus decreasing test expenses for each test while increasing test quality.

The protection of pedestrians from injuries by accidental collision is a primary focus of the automotive industry and of government legislation [1]. In this area, scientists and developers are faced with a multitude of requirements. Complex scenes are to be analyzed. The wide spectrum of where pedestrians and cyclists appear on the road, weather, and light conditions are just examples. Data fusion of raw or preprocessed signals for several sensors (cameras, radar, lidar, ultrasonic) need to be considered as well. Accordingly, algorithms are very complex. When moving from prototypic environments to embedded systems, additional constraints must be considered. Limited system resources drive the need to simplify and optimize for technical and economic reasons. With all these constraints, how can the safety functions be safe-guarded? This submission considers scene-based methods for the development of vehicle functions from prototype to series production focusing on functional safety.

There is an enormous effort to implement safety functionality into battery systems to prevent any accidents with the poisonous and inflammable ingredients of the electrolytes and electrode materials. But not only the safety regulation for lithium ion batteries will be different in comparison to the home electronics application, also the operating strategy must be different to guaranty the required lifetime in the automotive industry up to 10-12 years. This paperwork will show an approach to get offline (on test benches) and/or online (installed inside the car) information regarding the current healthy and state inside the cell. As an approach modeling of physical effects by the help of electro impedance spectroscopy (EIS) will be applied.

Under the persistent pressure to further reduce fuel consumption worldwide, it is necessary to advance the processes that influence the efficiency of gasoline engines. In doing so, harnessing the entire potential of fully variable mechanical valve trains will involve targeting efforts on optimizing all design parameters. A new type of valve timing system is used to portray thermodynamic and mechanical as well as electronic aspects of developing fully variable mechanical valve timing and lift systems

A physico-chemical model of a Cu-zeolite SCR/DPF-system involving NH₃ storage and SCR reactions as well as soot oxidation reactions with NO₂ has been developed and validated based on fundamental experimental investigations on synthetic gas test bench. The goal of the work was the quantitative modeling of NOx and NH₃ tailpipe emissions in transient test cycles in order to use the model for concept design analysis and the development of control strategies. Another focus was put on the impact of soot on SCR/DPF systems. In temperature-programmed desorption experiments, soot-loaded SCR/DPF filters showed a higher NH₃ storage capacity compared to soot-free samples. The measured effect was small, but could affect the NH₃ slip in vehicle applications. A bimodal desorption characteristic was measured for different adsorption temperatures and heating rates.

Global warming is a climate phenomenon with world-wide ecological, economic and social impact which calls for strong measures in reducing automotive fuel consumption and thus CO2 emissions. In this regard, turbocharging and the associated designing of the air path of the engine are key technologies in elaborating more efficient and downsized engines. Engine performance simulation or development, parameterization and testing of model-based air path control strategies require adequate performance maps characterizing the working behavior of turbochargers. The working behavior is typically identified on test rig which is expensive in terms of costs and time required. Hence, the objective of the research project “virtual Exhaust Gas Turbocharger” (vEGTC) is an alternative approach which considers a physical modeled vEGTC to allow a founded prediction of efficiency, pressure rise as well as pressure losses of an arbitrary turbocharger with known geometry.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

The Noise, Vibration and Harshness (NVH) behaviour of the powertrain, the driveline and the mounting is playing a very important role in the vehicle development process. The method described in this paper presents the coupling of Multi Body Simulation (MBS) with mathematical optimization tools exemplary for a powertrain mounting at a passenger car vehicle. It is shown, how this approach is integrated in the IAV - development process for validation and for optimization, i.e. finding the best solution for reaching the NVH targets. In early stage of development process, torsional vibration models are used to simulate e.g. the decoupling between engine and transmission. To simulate further physical effects, the models must be more and more detailed with a lot of additional parameter. One challenge for valid models is the parameter identification. The process to do this successfully with mathematical methods will be described.

The precision of direct fuel injection systems of combustion engines is crucial for the further reduction of emissions and fuel consumption. It is influenced by the dynamic behavior of the fuel system, in particular the injection valves and the common rail pressure. As model based control strategies for the fuel system could substantially improve the dynamic behavior, an accurate model of the common rail injection system for gasoline engines - consisting of the main components high-pressure pump, common rail and injection valves - that could be used for control design is highly desirable. Approaches for developing such a model are presented in this paper. For each key component, two models are derived, which differ in temporal resolution and number of degrees of freedom. Experimental data is used to validate and compare the models. The data was generated on a test bench specifically designed and built for this purpose.

The low-NOx standard for heavy-duty trucks proposed by the California Air Resources Board will require rapid warm-up of the aftertreatment system. Several different engine technologies are being considered to meet this need. In this study, a 1-D engine model was first used to evaluate several individual control strategies capable of increasing the exhaust enthalpy and decreasing the engine-out NOX over the initial portion of the cold start FTP cycle. The additional fuel consumption resulting from these strategies was also quantified with the model. Next, several of those strategies were combined to create a hypothetical aftertreatment warm-up mode for the engine. The model was then used to evaluate potential benefits of an air gap manifold (AGM) and two different turbine by-pass architectures. The detailed geometry of the AGM model was taken into account, having been constructed from a real prototype design.

The development and calibration of exhaust aftertreatment (EAT) systems for the most diverse applications of diesel powertrain concepts requires EAT models, capable of performing concept analysis as well as control and OBD system development and calibration. On the concept side, the choice of an application-specific EAT layout from a wide technology selection is driven by a number of requirements and constraints. These include statutory requirements regarding emissions of criteria pollutants and greenhouse gases (GHG), technical constraints such as engine-out emissions and packaging, as well as economic parameters such as fuel consumption, and EAT system and system development costs. Fast and efficient execution of the analysis and multi-criteria system optimization can be done by integrating the detailed EAT models into a total system simulation.

In this paper a practical methodology for automated tuning of simulation models is introduced, which is widely and successfully adapted in IAV. For this, stochastic optimization algorithms (like Genetic Algorithms or Particle Swarm Optimization), and appropriate algorithms for optimization tasks with very long computation time (e.g. Adaptive Surrogate-Model Optimization or Adaptive Hybrid Strategies) are used in combination with commercial and internal simulation tools. Often it is necessary to evaluate several contradictory objectives at the same time which leads to multi-criterion optimization. Effective post processing methods (mathematical decision aids) are used to select the best compromises for the problem. As a practical example, this automated tuning methodology is applied to an engine performance simulation model developed in GT-Power.

In-cylinder charge motion plays a key role in optimizing the combustion process of modern reciprocating engines. The present paper describes a method for obtaining the volumetric, isothermal, in-cylinder velocity flow field using Doppler Global Velocimetry (DGV). The DGV system is designed for measuring time-averaged velocity data in three different light sheet directions using a single camera system with the aim of providing planar, spatially resolved, three-component velocity data of the cylindrical cross section. As DGV provides time-averaged data, the results can be directly compared with data obtained by 3-D CFD analysis. An automated program code generates characteristic numbers of the measured velocity fields with the aim of assessing and comparing the results of different engine concepts.

This paper sketches a model based E/E-architecture analysis and design process for the development of distributed in-vehicle systems. Together with a model based system development, the sketched analysis and design process results in a cost-effective E/E-architecture which fulfills all the requirements for the developed in-vehicle system. As an example scenario, the integration of a new feature into a car, which requires integrating a new distributed function into the existing E/E-architecture of the car, is shown.

Conventional methods of physicochemical models require various experts and a high measurement demand to achieve the required model accuracy. With an additional request for faster development time for diagnostic algorithms, this method has reached the limits of economic feasibility. Machine learning algorithms are getting more popular in order to achieve a high model accuracy with an appropriate economical effort and allow to describe complex problems using statistical methods. An important point is the independence from other modelled variables and the exclusive use of sensor data and actuator settings. The concept has already been successfully proven in the field of modelling for exhaust gas aftertreatment sensors. An engine-out nitrogen oxide (NOX) emission sensor model based on polynomial regression was developed, trained, and transferred onto a conventional automotive electronic control unit (ECU) and also proves real-time capability.

For the NOx removal from diesel exhaust, the selective catalytic reduction (SCR) and lean NOx traps are established technologies. However, these procedures lack efficiency below 200 °C, which is of importance for city driving and cold start phases. Thus, the present paper deals with the development of a novel low-temperature deNOx strategy implying the catalytic NOx reduction by hydrogen. For the investigations, a highly active H2-deNOx catalyst, originally engineered for lean H2 combustion engines, was employed. This Pt-based catalyst reached peak NOx conversion of 95 % in synthetic diesel exhaust with N2 selectivities up to 80 %. Additionally, driving cycle tests on a diesel engine test bench were also performed to evaluate the H2-deNOx performance under practical conditions. For this purpose, a diesel oxidation catalyst, a diesel particulate filter and a H2 injection nozzle with mixing unit were placed upstream to the full size H2-deNOx catalyst.