Search Results

A new index to evaluate the inherent soot reduction in a diesel-like spray plume is proposed in this study. The index is named “Oxidation Potential Number” and was derived with the help of a computational fluid dynamics (CFD) software. C8 - C16 n-alkanes, 1-alcohols and di-n-ethers were studied with the help of this index over four part load engine operating conditions, representative of a C-class diesel vehicle. The CFD modelling results have shown that C8 molecules feature a higher potentiality to reduce the soot. Thus, C8 molecules were tested in a single cylinder diesel engine over the same operating conditions. In conclusion, the proposed index is compared with the soot engine out emission.

This article discusses highly flexible and accurate physics-based mean value modeling (MVM) for internal combustion engines and its wide applicability towards virtual vehicle calibration. The requirement to fulfill the challenging Real Driving Emissions (RDE) standards has significantly increased the demand for precise engine models, especially models regarding pollutant emissions and fuel economy. This has led to a large increase in effort required for precise engine modeling and robust model calibration. Two best-practice engine modeling approaches will be introduced here to satisfy these requirements. These are the exclusive MVM approach, and a combination of MVM and a Design of Experiments (DOE) model for heterogeneous multi-domain engine systems.

Knock control is one of the most vital functions for safe and fuel-efficient operation of gasoline engines. However, all knock control strategies rely on accurate knock detection to operate the engine close to the optimal set point. Knock detection is usually calibrated on the engine test bench, requiring the engine to run with knocking combustion in a time-consuming multi-stage campaign. Model-based calibration significantly reduces calibration loops on the test bench. However, this method requires a large effort in building and validating the model, which is often limited by the lack of function documentation, available measurements or hardware representation. As the software models are often not available, function structures vary between manufacturers and sub model functions are often documented as black boxes. Hence, using the model-based approach is not always possible.

In this paper a new approach is presented to evaluate the combustion behaviour of homogeneous gasoline engines by predicting burn delay and -duration in a way which can be obtained under the time constraints of the development process. This is accomplished by means of pure in-cylinder flow simulations without a classical combustion model. The burn delay model is based on the local distribution of the turbulent flow near the spark plug. It features also a methodology to compare different designs regarding combustion stability. The correlation for burn duration uses a turbulent characteristic number that is obtained from the turbulent flow in the combustion chamber together with a model for the turbulent burning velocity. The results show good agreement with the combustion process of the analyzed engines.

Due to the complex powertrain layout in hybrid vehicles, different configurations concerning internal combustion engine, electric motor and transmission can be combined - as is demonstrated by currently produced hybrid vehicles ([1], [2]). At the Institute for Combustion Engines (VKA) at RWTH Aachen University a combination of simulation, Design of Experiments (DoE) and numerical optimization methods was used to optimize the combustion engine, the powertrain configuration and the operation strategy in hybrid powertrains. A parametric description allows a variation of the main hybrid parameters. Parallel as well as power-split hybrid powertrain configurations were optimized with regard to minimum fuel consumption in the New European Driving Cycle (NEDC). Besides the definition of the optimum configuration for engine, powertrain and operation strategy this approach offers the possibility to predict the fuel consumption for any modifications of the hybrid powertrains.

Sustainable and energy-efficient consumption is a main concern in contemporary society. Driven by more stringent international requirements, automobile manufacturers have shifted the focus of development into new technologies such as Hybrid Electric Vehicles (HEVs). These powertrains offer significant improvements in the efficiency of the propulsion system compared to conventional vehicles, but they also lead to higher complexities in the design process and in the control strategy. In order to obtain an optimum powertrain configuration, each component has to be laid out considering the best powertrain efficiency. With such a perspective, a simulation study was performed for the purpose of minimizing well-to-wheel CO2 emissions of a city car through electrification. Three different innovative systems, a Series Hybrid Electric Vehicle (SHEV), a Mixed Hybrid Electric Vehicle (MHEV) and a Battery Electric Vehicle (BEV) were compared to a conventional one.

The current and future restrictions on pollutant emissions from internal combustion engines require a holistic investigation of the abilities of alternative fuels to optimize the combustion process and ensure cleaner combustion. In this regard, the Tailor-made Fuels from Biomass (TMFB) Cluster at Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University aims at designing production processes for biofuels as well as fuels optimal for use in internal combustion engines. The TMFB Cluster's scientific approach considers the molecular structure of the fuels as an additional degree of freedom for the optimization of both the production pathways and the combustion process of such novel biofuels. Thus, the model-based specification of target parameters is of the utmost importance to improve engine combustion performance and to send feedback information to the biofuel production process.

The growing number of passenger car variants and derivatives in all global markets, their high degree of software differentiability caused by regionally different legislative regulations, as well as pronounced market-specific customer expectations require a continuous optimization of the entire vehicle development process. In addition, ever stricter emission standards lead to a considerable increase in powertrain hardware and control complexity. Also, efforts to achieve market and brand specific multistep adjustable drivability characteristics as unique selling proposition, rapidly extend the scope for calibration and testing tasks during the development of powertrain control units. The resulting extent of interdependencies between the drivability calibration and other development and calibration tasks requires frontloading of development tasks.

The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, 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 (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function 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.

In this work, a transport and mixing model that calculates mixing in thermodynamic phase space was derived and validated. The mixing in thermodynamic multizone space is consistent to the one in the spatially resolved physical space. The model is developed using a turbulent channel flow as simplified domain. This physical domain of a direct numerical simulation (DNS) is divided into zones based on the quantitative value of transported scalars. Fluxes between the zones are introduced to describe mixing from the transport equation of the probability density function based on the mixing process in physical space. The mixing process of further scalars can then be carried out with these fluxes instead of solving additional transport equations. The relationship between the exchange flux in phase space and the concept of scalar dissipation are shown and validated by comparison to DNS results.

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.

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.

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 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.

Noise, vibration and harshness (NVH) is one of the most important performance evaluation aspect of electric motors. Among the different causes of the NVH issues of electrical drives, the high-frequency spatial and temporal harmonics of the electrical drive system is of great importance. To reduce the tonal noise of the electric motors, harmonic injection methods can be applied. However, a lot of the existing related work focuses more on improving the optimization process of the parameter settings of the injected current/flux/voltage, which are usually limited to some specific working conditions. The applicability and effectivity of the algorithm to the whole frequency/speed range are not investigated. In this paper, a multi-domain pipeline of harmonic injection controller design for a permanent magnet synchronous motor (PMSM) is proposed.

In the field of gasoline powertrain calibration, the challenges are growing due to ever shorter time-to-market requirements and a simultaneous increase in powertrain complexity. In addition, the great variety of vehicle variants requires an increasing number of prototypes for calibration and validation tasks within the framework of the current Real Driving Emissions (RDE) regulations and the expected Post Euro 6 emission standards. Hardware-in-the-Loop (HiL) simulations have been introduced successfully to support the calibration tasks in parallel to the conventional vehicle development activities. The HiL approach enables a more reliable compliance with emission limits and improves the quality of calibrations, while reducing the number of prototype vehicles, test resources and thus overall development costs.

The main tasks for all future powertrain developments are: regulated emissions, CO2-values, comfort, good drivability, high reliability and affordable costs. One widely discussed approach for fuel consumption improvement within passenger car applications, is to incorporate the downsizing effect. To attain constant engine performance an increase of boost pressure and/or rated speed is mandatory. In both cases, the mass flow rate through the intake and exhaust ports and valves will rise. In this context, the impact of the port layout on the system has to be reassessed. In this paper, the impact of the port layout on a modern diesel combustion system will be discussed and a promising concept shall be described in detail. The investigations shown include flow measurements, PIV measurements of intake flow, CFD simulations of the flow field during intake and results from the thermodynamic test bench. One of the important topics is to prove the impact of the flow quality on the combustion.

For on- and off-highway applications in 2012/2014 new legislative emissions requirements will be applied for both European (EURO 6/stage 4) and US (US 2010/Tier4 final) standards. Specifically the NOX-emission limit will be lowered down to 0.46 g/kWh (net power ≻ 56 kW (EU)/130 kW (US) - 560 kW). While for the previous emissions legislation various ways could be used to stay within the emissions limits (engine internal and aftertreatment measures), DeNOX-aftertreatment systems will be mandatory to reach future limits. In these kinds of applications fuel consumption of the engines is a very decisive selling argument for customers. Total cost of ownership needs to be as low as possible. The trade-off between fuel consumption and NOX emissions forces manufacturers to find an optimal solution, especially with regard to increasing fuel prices. In state-of-the-art calibration processes the aftertreatment system is considered separately from the calibration of the thermodynamics.

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.

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.