Fakultät Maschinenbau
Refine
Year of publication
Document Type
- Article (147)
- conference proceeding (article) (80)
- conference proceeding (presentation) (20)
- Part of a Book (11)
- Preprint (7)
- Book (6)
- Other (5)
- conference proceeding (volume) (4)
- Doctoral Thesis (4)
- Handout (3)
Language
- English (199)
- German (91)
- Multiple languages (3)
Publication reviewed
- begutachtet (250)
- nicht begutachtet (43)
Keywords
- Maschinelles Lernen (13)
- Fernwärme (12)
- Lastprognose (12)
- automated driving (12)
- deepDHC (11)
- KWK-Flex (10)
- advanced driver assistance systems (9)
- human factor (8)
- workload (8)
- KLEVERTEC / Vorlaufforschung (7)
Institute
- Fakultät Maschinenbau (293)
- IEAT - Institut für Energie- und Antriebstechnik (54)
- IFM - Institut für Fahrerassistenz und vernetzte Mobilität (30)
- KLEVERTEC (18)
- EPT – Institut für Effiziente Produktionstechnik (7)
- IPI – Institut für Produktion und Informatik (6)
- ECC-ProBell - Europäisches Kompetenzzentrum für Glocken (5)
- Fakultät Informatik (5)
- IFA – Institut für Innovative Fahrzeugantriebe (3)
- Fakultät Elektrotechnik (2)
- IMS - Institut für Maschinelles Sehen (2)
- IEES - Institut für Elektrische Energiesysteme (1)
Unified Framework and Survey for Model Verification, Validation and Uncertainty Quantification
(2020)
Simulation is becoming increasingly important in the development, testing and approval process in many areas of engineering, ranging from finite element models to highly complex cyber-physical systems such as autonomous cars. Simulation must be accompanied by model verification, validation and uncertainty quantification (VV&UQ) activities to assess the inherent errors and uncertainties of each simulation model. However, the VV&UQ methods differ greatly between the application areas. In general, a major challenge is the aggregation of uncertainties from calibration and validation experiments to the actual model predictions under new, untested conditions. This is especially relevant due to high extrapolation uncertainties, if the experimental conditions differ strongly from the prediction conditions, or if the output quantities required for prediction cannot be measured during the experiments. In this paper, both the heterogeneous VV&UQ landscape and the challenge of aggregation will be addressed with a novel modular and unified framework to enable credible decision making based on simulation models. This paper contains a comprehensive survey of over 200 literature sources from many application areas and embeds them into the unified framework. In addition, this paper analyzes and compares the VV&UQ methods and the application areas in order to identify strengths and weaknesses and to derive further research directions. The framework thus combines a variety of VV&UQ methods, so that different engineering areas can benefit from new methods and combinations. Finally, this paper presents a procedure to select a suitable method from the framework for the desired application.
This paper proposes a methodology for determining strategically bundled relevant test scenarios for the simulation- based evaluation of sensor constellations. This is achieved by gogri identification of important use cases for the autonomous driving operation, (b) the conversion of these use cases into Regions of Interests (ROIs) around the vehicle along with (c) a definition of a critical index (CI) for each of these regions and (d) a procedure to derive crucial scenarios and (e) categorise them into scenario families. The derived test scenarios help to optimise the field of view of the sensor constellation for the most important regions around the ego vehicle. The novelty lies in its independence from traditional methods of deriving test scenarios and its capability of providing targeted feedback to improve the sensor constellation at the identified pain points. The test scenario families can reduce the development time of highly automated vehicles by providing virtual testing of the sensor constellation performance in the vehicle concept phase.
Two-Dimensional ECMS for System Analysis of Hybrid Concepts featuring Two Electric Traction Motors
(2019)
This work is focusing on the extension of the well-known equivalent consumption minimization strategy (ECMS) for analysis of new hybrid electric vehicle (HEV) concepts featuring two traction motors. Given the contribution to advanced systems engineering, the control algorithm is derived under consideration of causality and practicality. Having more than one traction motor, a two-dimensional torque-split allowing full parallel operation of the HEV powertrain is required. Furthermore, this HEV enables series operation mode, and an example of implementation is given as well. Since we define the capability of comprehensive system analysis as a major attribute of the energy management, we abstain from restricting the solution space via heuristics e.g. mode selection in advance. This gives birth to affect the control behavior only by applying constraints directly to the control problem and thus enables analysis based on the respective (sub)optimal solutions. In this paper, a brief example of implementing so-called soft constraints for reducing the number of engine starts is given. The practicality of the proposed 2D-ECMS is proven within a simulation study and control behavior is analyzed. Additionally, different effects of variable constraints and parameter settings are investigated.
To find the optimal system-level design of hybrid electric vehicles (HEVs), component models are used in simulations to evaluate a large number of different designs within a high dimensional design space. As the electrical machine (EM) represents a key component of the HEV powertrain in terms of energy consumption, models require scalability and sufficient accuracy with manageable computational effort. This paper presents a novel approach for the development of scalable EM models based on Neural Networks (NN). The models are trained with data derived by a Finite Element Analysis (FEA) based scaling procedure and capable to represent the characteristics of a wide range of EM designs without the incorporation of further details. Once a model is trained, it can be directly used in system-level design optimization. The practicality of the model is proven within an exemplary simulation study and its goodness of fit to the training data is validated by a statistical analysis. This approach can help to reduce the computational effort of EM efficiency maps calculation, since only a small number of time-consuming FEA based scaling simulations must be performed prior to the optimization.
Polymer materials that regulate the relative humidity in their environment are relevant for applications in the packaging and building sectors. By integration of salts in polymer structures, such materials are able to absorb and desorb high amounts of water vapor. In this study, films of polylactic acid and polypropylene with dispersed calcium chloride (2 and 4 wt %) were produced and biaxially stretched to induce the formation of cavities. The resulting cavities in these films account up to 10 vol % and are able to contain emerging calcium chloride solution formed by water vapor absorption. These films absorb reversibly up to 15 wt % water vapor at 75% relative humidity at 23 °C. This absorption behavior is described by effective diffusion and effective sorption coefficients. Using a simple model, the effective water vapor diffusion coefficient of these films can be estimated from the permeation coefficient of the polymer and the sorption coefficient of the absorber.