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The technology used in hybrid vehicle concepts is significantly different from conventional vehicle technology with consequences also for the noise and vibration behavior. In conventional vehicles, certain noise phenomena are masked by the engine noise. In situations where the combustion engine is turned off in hybrid vehicle concepts, these noise components can become dominant and annoying. In hybrid concepts, the driving condition is often decoupled from the operation state of the combustion engine, which leads to unusual and unexpected acoustical behavior. New acoustic phenomena such as magnetic noise due to recuperation occur, caused by new components and driving conditions. The analysis of this recuperation noise by means of interior noise simulation shows, that it is not only induced by the powertrain radiation but also by the noise path via the powertrain mounts. The additional degrees of freedom of the hybrid drive train can also be used to improve the vibrational behavior.

Engine processes are subject to cyclic fluctuations, which a have direct effect on the operating and emission behavior of the engine. The fluctuations in direct injection gasoline engines are induced and superimposed by the flow and the injection. In stratified operation they can cause serious operating problems, such as misfiring. The current state of knowledge on the formation and causes of cyclic fluctuations is rather limited, which can be attributed to the complex nature of flow instabilities. The current investigation analyzes the cyclic fluctuations of the in-cylinder charge motion and the mixture formation in a direct injection gasoline engine using laser-optical diagnostics and numerical 3D-calculation. Optical measurement techniques and pressure indication are used to measure flow, mixture formation, and combustion processes of the individual cycles.

Due to experimental challenges, combustion of diesel-like jets has rarely been characterized by laser-based quantitative multiscalar measurements. In this work, recently developed laser diagnostics for combustion temperature and the concentrations of CO, O2, and NO are applied to a diesel-like jet, using a highly oxygenated fuel. The diagnostic is based on spontaneous Raman scattering (SRS) and laser-induced fluorescence (LIF) methods. Line imaging yields multiscalar profiles across the jet cross section. Measurements turn out to be particularly accurate, because near-stoichiometric combustion occurs in the central region of the jet. Thereby, experimental cross-influences by light attenuation and interfering emissions are greatly reduced compared to the combustion of conventional, sooting diesel fuel jets. This is achieved by fuel oxygenation and enhanced premixing.

Within a public founded project test cell investigations were undertaken to identify parameters which predominantly influence the development of critical deposits in injection nozzles. A medium-duty diesel engine was operated in two different coking cycles with a zinc-free lubricant. One of the cycles is dominated by rated power, while the second includes a wide area of the operation range. During the experiments the temperatures at the nozzle tip, the geometries of the nozzle orifice and fuel properties were varied. For a detailed analysis of the deposits methods of electron microscopy were deployed. In the course of the project optical access to all areas in the nozzle was achieved. The experiments were evaluated by means of the monitoring of power output and fuel flow at rated power. The usage of a SEM (scanning electron microscope) and a TEM (transmission electron microscope) revealed images of the deposits with a magnification of up to 160 000.

Dielectric barrier discharge (DBD) offers the advantage to excite and dissociate molecules in the exhaust gas stream. Those dissociated and excited species are oxidizing or reducing harmful exhaust gas components. The advantage of a plasma chemical system in comparison to a catalytic measure for exhaust gas treatment is the instantaneous activity at ambient temperature from the starting of the engine. The investigations reviewed in this paper are dealing with the plasma chemical oxidation of hydrocarbons in the exhaust gas stream during cold start conditions. The article concerns the design and development of a plasma-system in order to decrease the hydrocarbon emissions from engine start till catalyst light off. Vehicle results in the New European Driving Cycle show a hydrocarbon conversion of more than 42% in the first 11 seconds from engine start. In this period nearly all types of hydrocarbon were reduced.

In order to reduce engine out CO2 emissions, it is a main subject to find new alternative fuels from renewable sources. For identifying the specification of an optimized fuel for engine combustion, it is essential to understand the details of combustion and pollutant formation. For obtaining a better understanding of the flame behavior, dynamic structure large eddy simulations are a method of choice. In the investigation presented in this paper, an n-heptane spray flame is simulated under engine relevant conditions starting at a pressure of 50 bar and a temperature of 800 K. Measurements are conducted at a high-pressure vessel with the same conditions. Liquid penetration length is measured with Mie-Scatterlight, gaseous penetration length with Shadowgraphy and lift-off length as well as ignition delay with OH*-Radiation. In addition to these global high-speed measurement techniques, detailed spectroscopic laser measurements are conducted at the n-heptane flame.

One of the challenges faced when using Li-ion batteries in electric vehicles is to keep the cell temperatures below a given threshold. Mathematical modeling would indeed be an efficient tool to test virtually this requirement and accelerate the battery product lifecycle. Moreover, temperature predicting models could potentially be used on-board to decrease the limitations associated with sensor based temperature feedbacks. Accordingly, we present a complete modeling procedure which was used to calculate the cell temperatures during a given electric vehicle trip. The procedure includes a simple vehicle dynamics model, an equivalent circuit battery model, and a 3D finite element thermal model. Model parameters were identified from measurements taken during constant current and pulse current discharge tests. The cell temperatures corresponding to an actual electric vehicle trip were calculated and compared with measured values.

The presented paper deals with the set-up and performance of a newly developed control system as well as with achieved engine results. This control system is able to control the entire cylinder pressure trace by using a flexible rate shaping injector and iterative learning control (ILC). Standard thermodynamic cycles, like isobaric and Seiliger cycles, and a newly suggested class of cycles are generated and analyzed on a single cylinder engine. With this control system an extremely flexible tool for optimization of combustion processes is available to exploit the full potential of injection rate- shaping on diesel engines.

The efficient operation of powertrain test benches in research and development is strongly influenced by the state of “health” of the functional test object. Hence, the use of Early Damage Detection Systems (EDDS) with Unit Under Test (UUT) monitoring is becoming increasingly popular. An EDDS should primarily avoid total loss of the test object and ensure that damaged parts are not completely destroyed, and can still be inspected. Therefore, any abnormality from the standard test object behavior, such as an exceeding of predefined limits, must be recognized at an early testing time, and must lead to a shutdown of the test bench operation. With sensors mounted on the test object, it is possible to isolate the damage cause in the event of its detection. Advanced EDDS configurations also optimize the predefined limits by learning new shutdown values according to the test object behavior within a very short time.

Modeling thermal systems in Battery Electric Vehicles (BEVs) is crucial for enhancing energy efficiency through predictive control strategies, thereby extending vehicle range. A major obstacle in this modeling is the often limited availability of detailed system information. This research introduces a methodology using neural networks for system identification, a powerful technique capable of approximating the physical behavior of thermal systems with minimal data requirements. By employing black-box models, this approach supports the creation of optimization-based operational strategies, such as Model Predictive Control (MPC) and Reinforcement Learning-based Control (RL). The system identification process is executed using MATLAB Simulink, with virtual training data produced by validated Simulink models to establish the method's feasibility. The neural networks utilized for system identification are implemented in MATLAB code.

An improved plasma reactor has been designed, built and evaluated. It is characterized by a reduced power per area ratio, relative to previous designs, and includes several improvements to run the whole system safely in a car. The new reactor design includes a concentric inner high voltage electrode, a grounded outer electrode, a shielded high-voltage and high temperature resistant electrical connection. A generator controller has been developed for better control of operating conditions as required during the engine cold start phase. The new generator/reactor system was installed in the exhaust pipe of a gasoline direct injection engine. HC emissions could be reduced up to 30 % in the first 40 seconds of a cold start test. In addition to HC treatment the dielectric barrier discharge has also been investigated as a method for regenerating a diesel particulate trap.

Cyclic fluctuations of the in-cylinder processes in a Direct Injection Spark Ignition (DISI) engine may strongly affect the engine operation causing misfires or variations in the indicated mean effective pressure (imep). Particularly misfires prevent compliance with current or future exhaust emission legislations. Nevertheless, the origin of cyclic fluctuations is not well understood since fluctuations of in-cylinder air flow, fuel injection and wall interaction have to be considered. This paper focusses on a detailed experimental analysis of the origin of cyclic fluctuations in a DISI engine with an air guided combustion process by means of advanced Laser Induced Exciplex Fluorescence (LIEF) measurements. It reveals that cycle-to-cycle variations primarily originate from the air/fuel ratio at the spark plug.

Hybrid and electric vehicles present a promising trade-off between the necessary reductions in emissions and fuel consumption, the improvement in driving pleasure and performance of today's and tomorrow's vehicles. These hybrid vehicles rely primarily on electronics for the control and the coordination of the different sub-systems or components. The number and complexity of the functions distributed over many control units is increasing in these vehicles. Functional safety, defined as absence of unacceptable risk due to the hazards caused by mal-function in the electric or electronic systems is becoming a key factor in the development of modern vehicles such as electric and hybrid vehicles. This important increase in functional safety-related issues has raised the need for the automotive industry to develop its own functional safety standard, ISO 26262.

The modern engine design process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. The introduction of predictive real-time simulation tools that represent the entire powertrain can likely contribute to improving the efficiency of the calibration process. Engine models, which are purely based on physical first principles, are usually not capable of real-time applications, especially if the simulation is focused on cold start and warm-up behavior. However, the initial data definition for the ECU using a Hardware-in-the-Loop (HiL)-Simulator requires a model with both real-time capability and sufficient accuracy. The use of artificial intelligence systems becomes necessary, e.g. neural networks. Methods, structures and the realization of a hybrid real-time model are presented in this paper, which combines physical and neural network models.

A main focus of development in modern SI engine technology is variable valve timing, which implies a high potential of improvement regarding fuel consumption and emissions. Variable opening, period and lift of inlet and outlet valves enable numerous possibilities to alter gas exchange and combustion. However, this additional variability generates special demands on the calibration process of specific engine control devices, particularly under cold start and warm-up conditions. This paper presents procedures, based on Hardware-in-the-Loop (HiL) simulation, to support the classical calibration task efficiently. An existing approach is extended, such that a virtual combustion engine is available including additional valve timing variability. Engine models based purely on physical first principles are often not capable of real time execution. However, the definition of initial parameters for the ECU requires a model with both real time capability and sufficient accuracy.

The demand for lower CO2 emissions requires not just the optimization of every single component but the complete system. For a transmission system, it is important to optimize the transmission hardware as we well as the interaction of powertrain components. For automatic transmission with wide ratio spreads, the main losses are caused by the actuation system, which can be reduced with use of ondemand actuation systems. In this paper, a new on-demand electromechanical actuation system with validation results on a clutch test bench is presented. The electro-mechanical actuator shows an increase in the efficiency of 4.1 % compared to the conventional hydraulic actuation in a simulated NEDC (New European Driving Cycle) cycle. This increase is based on the powerless end positions of the actuator (engaged and disengaged clutch). The thermal tension and wear are compensated with a disk spring. This allows a stable control over service life.

The modern engine development process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. It is expected that predictive simulation tools that encompass the entire powertrain can potentially improve the efficiency of the calibration process. The testing of an ECU using a HiL system requires a real-time model. Additionally, if the initial parameters of the ECU are to be defined and tested, the model has to be more accurate than is typical for ECU functional testing. It is possible to enhance the generalization capability of the simulation, with neuronal network sub-models embedded into the architecture of a physical model, while still maintaining real-time execution. This paper emphasizes the experimental investigation and physical modeling of the port fuel injected SI engine.

To understand how laminar burning velocities of standard unleaded gasoline-air-mixtures change by varying the concentration of oxygen in the combustible mixture, experimentally and numerical investigations are conducted in this work. Experiments were performed using a heatable pressure vessel which enables optical access. A monochromatic high-speed Schlieren cinematography measurement system combined with a high-speed CCD camera were used to track the propagating spherical flame fronts in the vessel. Numerically, freely propagating one dimensional laminar steady flame calculations were conducted for Primary-Reference-Fuel Air Mixtures (PRF87 or RON87), corresponding for standard gasoline combustible mixtures. Two combustible mixtures were investigated: (1) with air as oxidizer; (2) oxidizer consisting of 15% O2 and 85% N2 by mole fractions. The initial temperature for all investigated mixtures was 373 K.

In this work very rich premixed laminar spherical flame kernels of hydrocarbon-air combustible mixtures were experimentally and numerically investigated under microgravity conditions. These microgravity combustion experiments were carried out in the Drop Tower of Bremen University. The Closed-Vessel-Bomb-Method (CVBM) was applied for all experimental investigations combined with a monochromatic Helium-Neon-Schlieren Measurement Technique. Images of the propagating spherical flames were tracked with a High-Speed-Camera. The pressure vessel enables optical access and contains a volume of approx. half a litre. Combustible Mixtures were investigated at initial pressures up to 30 bar and initial temperatures were 420 K for all experiments, whereas the equivalence ratio for investigated N-Pentane-Air-Mixtures was Φ=3.0, N-Hexane-Air-Mixtures was Φ=3.3, N-Heptane-Air-Mixtures was Φ=3.5 and the equivalence ratio for investigated Isooctane-Air-Mixtures was Φ=3.9 for all experiments.

The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with reduced development times. The growing number of vehicle variants, sharing similar engines and control algorithms, requires different calibrations. Additionally, these engines feature an increasing number of calibration parameters, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art logic-level (white box) model-based calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited functional documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (black box) for two distinct ECU functional structures with minimal software documentation.