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As the late combustion phase in SI engines is of high importance for a further reduction of fuel consumption and especially emissions, the impacts of unburnt mass, located in a small volume with a relatively large surface near the wall and in the top land volume, is of high relevance throughout the range of operation. To investigate and quantify the respective interactions, a state of the art Mercedes-Benz single cylinder research SI-engine was equipped with extensive measurement technology. To detect the axial and radial temperature distribution, several surface thermocouples were applied in two layers around the top land volume. As an additional reference, multiple surface thermocouples in the cylinder head complement the highly dynamic temperature measurements in the boundary zones of the combustion chamber.

This study investigates the effects of octane quality on the performance, i.e., acceleration and power, and fuel economy (FE) of one late model US vehicle, which is powered by a small displacement, turbocharged, gasoline direct injection (GDI) engine. The relative importance of the gasoline parameters Research and Motor Octane Number (RON and MON) in meeting the octane requirement of this engine to run at an optimum spark timing for the given demand was considered by evaluating the octane index (OI), where OI = (1-K) RON + K MON and K is a constant depending on engine design and operating conditions. Over wide open throttle (WOT) accelerations, the average K of this Pontiac Solstice was determined as −0.75, whereby a lower MON would give a higher OI, a higher knock resistance and better performance.

State-of-the-art powertrain concepts with automatic transmission must comply with increasingly stringent legislation on emissions and fuel consumption while fulfilling or surpassing customers' expectations as to driveability. In this respect, automated manual transmissions (AMT) and automated shift transmissions (AST) must compete with conventional automatic transmissions (AT) and continuously variable transmissions (CVT). In order to exploit the theoretical advantages of ASTs and put them into practice, complex ECU functions are needed to coordinate engine and transmission. Adaptive control, sophisticated clutch management and an intelligent shifting strategy allow shifting quality and shifting points to be simultaneously optimized to the effect that performance and comfort are increased while fuel consumption is reduced.

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

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 low-NOx standard for heavy-duty trucks proposed by the California Air Resources Board will require rapid warm-up of the aftertreatment system (ATS). Several different aftertreatment architectures and technologies, all based on selective catalytic reduction (SCR), are being considered to meet this need. One of these architectures, the close-coupled SCR (ccSCR), was evaluated in this study using two different physics-based, 1D models; the simulations focused on the first 300 seconds of the cold-start Federal Test Procedure (FTP). The first model, describing a real, EuroVI-compliant engine equipped with series turbochargers, was used to evaluate a ccSCR located either i) immediately downstream of the low-pressure turbine, ii) in between the two turbines, or iii) in a by-pass around the high pressure turbine.

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 majority of powertrain types considered important contributors to achieving the CO2 targets in the transportation sector employ a battery as an energy storage device. The need for batteries is hence expected to grow drastically with increasing market share of CO2-optimized powertrain concepts. The resulting huge pressure on the development of future electrochemical energy storage systems necessitates the application of advanced methodologies enabling a fast and cost-efficient concept definition and optimization process. This paper presents a model-based methodology for the optimization of BEV thermal management concept layouts and operation strategies targeting minimized energy consumption. Starting at the vehicle level, the proposed methodology combines appropriate representations of all primary powertrain components with 1D cooling and refrigerant circuit models and focuses on their interaction with the battery chemistry.

This paper shows the main results of a research activity carried out in order to investigate the impact of different hybridization concepts on vehicle fuel economy during standard homologation cycles (NEDC, FTP75, US Highway, Artemis). Comparative analysis between a standard passenger vehicle and three different hybrid solutions based on the same vehicle platform is presented. The following parallel hybrid powertrain solutions were investigated: Hybrid Electric Vehicle (HEV) solution (three different levels of hybridization are investigated with respect to different Electric Motor Generator size and battery storage/power capacity), High Speed Flywheel (HSF) system described as a fully integrated mechanical (kinetic) hybrid solution based on the quite innovative approach, and hydraulic hybrid system (HHV). In order to perform a fare analysis between different hybrid systems, analysis is also carried out for equal system storage capacities.

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 startability of SI engines, especially of DISI engines, is the greatest challenge when using ethanol blended fuels. The development of a suitable injection strategy is therefore the main engineering target when developing an ethanol engine with direct injection. In order to limit the test efforts of such a program, a vaporization model has been created that provides the quantity of vaporized fuel depending on pressure and on start and end, respectively number and split relation of injections. This model takes account of the most relevant fuel properties such as density, surface tension and viscosity. It also considers the interaction of the spray with cylinder liner, cylinder head and piston. A comparison with test results shows the current status and the need for action of this simulation model.

After the realization of very low exhaust gas emissions and corresponding OBD requirements to fulfill Euro VI and Tier 4 legislation, the focus in heavy-duty powertrain development is on the reduction of fuel consumption and thus CO₂ emissions again. Besides this, the total vehicle operation costs play another major role. A holistic view of the overall powertrain system including the combustion process, exhaust gas aftertreatment, energy recuperation and energy storage is necessary in order to obtain the best possible system for a given application. A management system coordinating the energy flow between the different subsystems while guaranteeing low exhaust emissions plays a major part in operating such complex architectures under optimal conditions.

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.

A promising combustion technology for DISI downsizing engines is the Miller cycle. It is based on an early intake valve closing for the separation of effective and geometric compression ratio. Therefore IAV has prepared a turbocharged DISI test engine with a high geometric compression ratio. This engine is equipped with the Schaeffler “UniAir” variable valve train in order to investigate a variable Miller cycle valve timing in the turbocharged map area. The goal is to investigate whether and how a rapidly variable Miller cycle can influence the knocking behavior. Therefore its potential for a SI knock control can be evaluated. The investigated parameters in a steady-state engine dyno mode were the intake valve closing timing, the intake camshaft phasing and the ignition timing. A variable intake valve closing Miller cycle strategy, a variable intake camshaft phasing Miller cycle strategy and a state-of-the- art ignition timing strategy have been investigated.