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The latest legislative tendencies for on-highway heavy duty vehicles in the United States such as the feasibility assessment of low NOX standards of CARB or EPA’s memorandum forecast further tightening of the NOX emissions limits. In addition, the GHG Phase 2 legislation and also phased-in regulations in the EU enforce a continuous reduction in CO2 emissions resp. fuel consumption. In order to meet such low NOX emission limits, a rapid heat-up of the exhaust after-treatment system (EATS) is inevitable. However, the required thermal management results in increased fuel consumption, i.e. CO2 emissions as shown in numerous previous works also by the authors. A NOX-CO2 trade-off for cumulative cycle emissions can be observed, which can be optimized by using more advance technologies on the engine and/or on the EATS side.

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 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.

The recent advance in the development of various hybrid vehicle technologies comes along with the need of establishing optimal energy management strategies, in order to minimize both fuel economy and pollutant emissions, while taking into account an increasing number of state and control variables, depending on the adopted hybrid architecture. One of the objectives of this research was to establish benchmarking performance, in terms of fuel economy, for real time on-board management strategies, such as ECMS (Equivalent Consumption Minimization Strategy), whose structure has been implemented in a SIMULINK model for different hybrid vehicle concepts.

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.

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.

Recent years have witnessed a dramatic increase in global ethanol production, while cellulosic feedstock or the algae-based production approach make more sustainable ethanol production foreseeable in many countries. The ethanol produced will increasingly penetrate the markets not only as blending component, but also as main fuel component, boosting demand for flex-fuel vehicles. One of the main challenges for flex-fuel vehicles is the cold start due to the poor vapor pressure of ethanol. This is detrimental to starting capability in DISI engines in particular, with increased cylinder wall wetting causing higher oil dilution. The most efficient solution for DISI engines is a smart injection strategy, enabling fuel vaporization during injection in the compression stroke. But this requires optimum injection parameters such as injection timing, split ratio and rail pressure.

Whereas the verification of non-safety-related, embedded software typically focuses on demonstrating that the implementation fulfills its functional requirements, this is not sufficient for safety-relevant systems. In this case, the control software must also meet application-specific safety requirements. Safety requirements typically arise from the application of hazard and/or safety analysis techniques, e.g. FMEA, FTA or SHARD. During the downstream development process it must be shown that these requirements cannot be violated. This can be achieved utilizing different techniques. One way of providing evidence that violations of the safety properties identified cannot occur is to thoroughly test each of the safety requirements. This paper introduces Evolutionary Safety Testing (EST), a fully automated procedure for the safety testing of embedded control software.

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.

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

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.

IAV has developed a strategy for transmission ratio selection that serves AMT, ATs, CVTs and Hybrid drivetrains. Since the power demand dependent strategy is applicable to all transmission types, it is possible to implement the same character of vehicle behavior. As a result, a manufacturer specific vehicle characteristic can be given to the complete range of powertrains. This universal field of application is made possible by the choice of ratio being dependent on the drivers demand of traction power instead of the usual dependency concerning the accelerator position and the vehicle velocity. Therefore, as opposed to conventional shifting strategies, the selected transmission ratio guarantees the demanded traction power. In the case of insufficient power at the actual transmission ratio, the engine speed will be increased.