TY - CHAP A1 - Busse, Daniel A1 - Esch, Thomas A1 - Muntaniol, Roman T1 - Thermal management in E-carsharing vehicles - preconditioning concepts of passenger compartments T2 - E-Mobility in Europe : trends and good practice N2 - The issue of thermal management in electric vehicles includes the topics of drivetrain cooling and heating, interior temperature, vehicle body conditioning and safety. In addition to the need to ensure optimal thermal operating conditions of the drivetrain components (drive motor, battery and electrical components), thermal comfort must be provided for the passengers. Thermal comfort is defined as the feeling which expresses the satisfaction of the passengers with the ambient conditions in the compartment. The influencing factors on thermal comfort are the temperature and humidity as well as the speed of the indoor air and the clothing and the activity of the passengers, in addition to the thermal radiation and the temperatures of the interior surfaces. The generation and the maintenance of free visibility (ice- and moisture-free windows) count just as important as on-demand heating and cooling of the entire vehicle. A Carsharing climate concept of the innovative ec2go vehicle stipulates and allows for only seating areas used by passengers to be thermally conditioned in a close-to-body manner. To enable this, a particular feature has been added to the preconditioning of the Carsharing electric vehicle during the electric charging phase at the parking station. KW - Carsharing KW - Thermal management KW - Thermal comfort KW - Electrical vehicle KW - Passenger compartment Y1 - 2015 SN - 978-3-319-13193-1 U6 - http://dx.doi.org/10.1007/978-3-319-13194-8_18 SP - 327 EP - 343 PB - Springer CY - Cham [u.a.] ER - TY - BOOK A1 - Esch, Thomas T1 - Experimentelle Untersuchungen an Antriebssystemen von Kraft-, Luft- und Raumfahrzeugen : Vorlesungsumdruck. 2. Aufl. Bd. 1 Y1 - 2013 PB - Fachhochschule Aachen, Lehr- und Forschungsgebiet Thermodynamik und Verbrennungstechnik CY - Aachen ET - 2. Aufl. ER - TY - BOOK A1 - Esch, Thomas T1 - Verbrennungsmotoren Y1 - 2015 PB - Fachhochschule Aachen, Lehr- und Forschungsgebiet Thermodynamik und Verbrennungstechnik CY - Aachen ET - 11. Aufl., [Umdruck] ER - TY - BOOK A1 - Esch, Thomas T1 - Verbrennungsmotoren Y1 - 2013 PB - Fachhochschule Aachen, Lehr- und Forschungsgebiet Thermodynamik und Verbrennungstechnik CY - Aachen ET - 10. Aufl., [Umdruck] ER - TY - BOOK A1 - Esch, Thomas T1 - Experimentelle Untersuchungen an Antriebssystemen von Kraft-, Luft- und Raumfahrzeugen : Vorlesungsumdruck. 3. Aufl. Bd. 1 Y1 - 2015 PB - Fachhochschule Aachen, Lehr- und Forschungsgebiet Thermodynamik und Verbrennungstechnik CY - Aachen ET - 3. Aufl. ER - TY - RPRT A1 - Esch, Thomas A1 - Damm, Marc André A1 - Kalbhenn, Hartmut T1 - Auslegung und Simulation eines Hybridantriebs für den teilelektrischen Betrieb eines Luftfahrzeuges der allgemeinen Luftfahrt : Schlussbericht für das Forschungsvorhaben ; Förderperiode 01.07.2009 - 31.05.2012 T1 - HyFly ; Design and simulation of a partially electrified operatin of an aircraft of the general aviation Y1 - 2013 U6 - http://dx.doi.org/10.2314/GBV:780055411 N1 - Report-Nr.: 1709X08 CY - Aachen ; Hannover ER - TY - JOUR A1 - Khayyam, Hamid A1 - Jamali, Ali A1 - Bab-Hadiashar, Alireza A1 - Esch, Thomas A1 - Ramakrishna, Seeram A1 - Jalili, Mahdi A1 - Naebe, Minoo T1 - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling 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 U6 - http://dx.doi.org/10.1109/ACCESS.2020.2999898 SN - 2169-3536 VL - 8 IS - Art. 9108222 SP - 111381 EP - 111393 PB - IEEE CY - New York, NY 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 - http://dx.doi.org/10.1109/ACCESS.2020.2999898 SP - 1 EP - 12 PB - IEEE CY - New York, NY 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 - http://dx.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 - CHAP A1 - Haugg, Albert Thomas A1 - Kreyer, Jörg A1 - Kemper, Hans A1 - Hatesuer, Katerina A1 - Esch, Thomas T1 - Heat exchanger for ORC. adaptability and optimisation potentials T2 - IIR International Rankine 2020 Conference N2 - The recovery of waste heat requires heat exchangers to extract it from a liquid or gaseous medium into another working medium, a refrigerant. In Organic Rankine Cycles (ORC) on Combustion Engines there are two major heat sources, the exhaust gas and the water/glycol fluid from the engine’s cooling circuit. A heat exchanger design must be adapted to the different requirements and conditions resulting from the heat sources, fluids, system configurations, geometric restrictions, and etcetera. The Stacked Shell Cooler (SSC) is a new and very specific design of a plate heat exchanger, created by AKG, which allows with a maximum degree of freedom the optimization of heat exchange rate and the reduction of the related pressure drop. This optimization in heat exchanger design for ORC systems is even more important, because it reduces the energy consumption of the system and therefore maximizes the increase in overall efficiency of the engine. Y1 - 2020 U6 - http://dx.doi.org/10.18462/iir.rankine.2020.1224 N1 - Conference: IIR International Rankine 2020 Conference - Heating, Cooling, Power Generation. Glasgow, 2020. ER -