TY - CHAP A1 - Schulze, Sven A1 - Mühleisen, M. A1 - Feyerl, Günter T1 - Adaptive energy management strategy for a heavy-duty truck with a P2-hybrid topology T2 - 18. Internationales Stuttgarter Symposium. Proceedings Y1 - 2018 U6 - https://doi.org/10.1007/978-3-658-21194-3 SP - 75 EP - 89 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Nowack, N. A1 - Röth, Thilo A1 - Bührig-Polaczek, A. A1 - Klaus, G. ED - Hirsch, Jürgen T1 - Advanced Sheet Metal Components Reinforced by Light Metal Cast Structures T2 - Aluminium alloys : their physical and mechanical properties ; [proceedings of the 11th International Conference on Aluminium Alloys, 22 - 26 Sept. 2008, Aachen, Germany ; ICAA 11] Y1 - 2008 SN - 978-3-527-32367-8 IS - 2 SP - 2374 EP - 2381 ER - TY - JOUR A1 - Kreyer, Jörg A1 - Müller, Marvin A1 - Esch, Thomas T1 - A Calculation Methodology for Predicting Exhaust Mass Flows and Exhaust Temperature Profiles for Heavy-Duty Vehicles JF - SAE International Journal of Commercial Vehicles N2 - The predictive control of commercial vehicle energy management systems, such as vehicle thermal management or waste heat recovery (WHR) systems, are discussed on the basis of information sources from the field of environment recognition and in combination with the determination of the vehicle system condition. In this article, a mathematical method for predicting the exhaust gas mass flow and the exhaust gas temperature is presented based on driving data of a heavy-duty vehicle. The prediction refers to the conditions of the exhaust gas at the inlet of the exhaust gas recirculation (EGR) cooler and at the outlet of the exhaust gas aftertreatment system (EAT). The heavy-duty vehicle was operated on the motorway to investigate the characteristic operational profile. In addition to the use of road gradient profile data, an evaluation of the continuously recorded distance signal, which represents the distance between the test vehicle and the road user ahead, is included in the prediction model. Using a Fourier analysis, the trajectory of the vehicle speed is determined for a defined prediction horizon. To verify the method, a holistic simulation model consisting of several hierarchically structured submodels has been developed. A map-based submodel of a combustion engine is used to determine the EGR and EAT exhaust gas mass flows and exhaust gas temperature profiles. All simulation results are validated on the basis of the recorded vehicle and environmental data. Deviations from the predicted values are analyzed and discussed. Y1 - 2020 U6 - https://doi.org/10.4271/02-13-02-0009 SN - 1946-3928 VL - 13 IS - 2 SP - 129 EP - 143 PB - SAE International CY - Warrendale, Pa. ER - TY - JOUR A1 - Serror, Martin A1 - Hack, Sacha A1 - Henze, Martin A1 - Schuba, Marko A1 - Wehrle, Klaus T1 - Challenges and Opportunities in Securing the Industrial Internet of Things JF - IEEE Transactions on Industrial Informatics Y1 - 2021 U6 - https://doi.org/10.1109/TII.2020.3023507 SN - 1941-0050 VL - 17 IS - 5 SP - 2985 EP - 2996 PB - IEEE CY - New York ER - TY - CHAP A1 - Merkens, Torsten A1 - Hebel, Christoph T1 - Sharing mobility concepts – flexible, sustainable, smart T2 - Proceedings of the 1st UNITED – Southeast Asia Automotive Interest Group (SAIG) International Conference KW - Sharing mobility KW - electro mobility KW - business models KW - mobility behaviour Y1 - 2021 SN - 978-3-902103-94-9 N1 - 1st UNITED-SAIG International Conference, 21-22 APR 2021, Chulalongkorn University, Thailand SP - 43 EP - 44 ER - TY - CHAP A1 - Wu, Ziyi A1 - Kemper, Hans T1 - The optimal 48 V – battery pack for a specific load profile of a heavy duty vehicle T2 - 8. Internationale Fachtagung Kraftwerk Batterie : 26. – 27. April 2016, Münster, Deutschland Y1 - 2016 ER - TY - CHAP A1 - Neu, Eugen A1 - Janser, Frank A1 - Khatibi, Akbar A. A1 - Orifici, Adrian C. T1 - In-flight vibration-based structural health monitoring of aircraft wings T2 - 30th Congress of the internatonal council of the aeronautical sciences : 25.-30. September 2016, Daejeon, Korea N2 - This work presents a methodology for automated damage-sensitive feature extraction and anomaly detection under multivariate operational variability for in-flight assessment of wings. The method uses a passive excitation approach, i. e. without the need for artificial actuation. The modal system properties (natural frequencies and damping ratios) are used as damage-sensitive features. Special emphasis is placed on the use of Fiber Bragg Grating (FBG) sensing technology and the consideration of Operational and Environmental Variability (OEV). Measurements from a wind tunnel investigation with a composite cantilever equipped with FBG and piezoelectric sensors are used to successfully detect an impact damage. In addition, the feasibility of damage localisation and severity estimation is evaluated based on the coupling found between damageand OEV-induced feature changes. Y1 - 2016 ER - TY - CHAP A1 - Dinghofer, Kai A1 - Hartung, Frank T1 - Analysis of Criteria for the Selection of Machine Learning Frameworks T2 - 2020 International Conference on Computing, Networking and Communications (ICNC) N2 - With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs. Y1 - 2020 U6 - https://doi.org/10.1109/ICNC47757.2020.9049650 N1 - 2020 International Conference on Computing, Networking and Communications (ICNC), 17-20 February 2020, Big Island, HI, USA SP - 373 EP - 377 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Kreyer, Jörg A1 - Müller, Marvin A1 - Esch, Thomas T1 - A Map-Based Model for the Determination of Fuel Consumption for Internal Combustion Engines as a Function of Flight Altitude N2 - In addition to very high safety and reliability requirements, the design of internal combustion engines (ICE) in aviation focuses on economic efficiency. The objective must be to design the aircraft powertrain optimized for a specific flight mission with respect to fuel consumption and specific engine power. Against this background, expert tools provide valuable decision-making assistance for the customer. In this paper, a mathematical calculation model for the fuel consumption of aircraft ICE is presented. This model enables the derivation of fuel consumption maps for different engine configurations. Depending on the flight conditions and based on these maps, the current and the integrated fuel consumption for freely definable flight emissions is calculated. For that purpose, an interpolation method is used, that has been optimized for accuracy and calculation time. The mission boundary conditions flight altitude and power requirement of the ICE form the basis for this calculation. The mathematical fuel consumption model is embedded in a parent program. This parent program presents the simulated fuel consumption by means of an example flight mission for a representative airplane. The focus of the work is therefore on reproducing exact consumption data for flight operations. By use of the empirical approaches according to Gagg-Farrar [1] the power and fuel consumption as a function of the flight altitude are determined. To substantiate this approaches, a 1-D ICE model based on the multi-physical simulation tool GT-Suite® has been created. This 1-D engine model offers the possibility to analyze the filling and gas change processes, the internal combustion as well as heat and friction losses for an ICE under altitude environmental conditions. Performance measurements on a dynamometer at sea level for a naturally aspirated ICE with a displacement of 1211 ccm used in an aviation aircraft has been done to validate the 1-D ICE model. To check the plausibility of the empirical approaches with respect to the fuel consumption and performance adjustment for the flight altitude an analysis of the ICE efficiency chain of the 1-D engine model is done. In addition, a comparison of literature and manufacturer data with the simulation results is presented. Y1 - 2020 U6 - https://doi.org/10.25967/490162 N1 - 68. Deutscher Luft- und Raumfahrtkongress 30.09.-02.10.2019, Darmstadt PB - DGLR CY - Bonn ER - TY - JOUR A1 - Khayyam, Hamid A1 - Jamali, Ali A1 - Bab-Hadiashar, Alireza A1 - Esch, Thomas A1 - Ramakrishna, Seeram A1 - Jalil, Mahdi A1 - Naebe, Minoo T1 - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0 JF - IEEE Access N2 - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. Y1 - 2020 SN - 2169-3536 U6 - https://doi.org/10.1109/ACCESS.2020.2999898 SP - 1 EP - 12 PB - IEEE CY - New York, NY ER -