@incollection{BusseEschMuntaniol2015,
  author    = {Busse, Daniel and Esch, Thomas and Muntaniol, Roman},
  title     = {Thermal management in E-carsharing vehicles - preconditioning concepts of passenger compartments},
  series = {E-Mobility in Europe : trends and good practice},
  booktitle = {E-Mobility in Europe : trends and good practice},
  publisher = {Springer},
  address   = {Cham [u.a.]},
  isbn      = {978-3-319-13193-1},
  doi       = {10.1007/978-3-319-13194-8_18},
  pages     = {327 -- 343},
  year      = {2015},
  abstract  = {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.},
  language  = {en}
}
@book{Esch2013,
  author    = {Esch, Thomas},
  title     = {Experimentelle Untersuchungen an Antriebssystemen von Kraft-, Luft- und Raumfahrzeugen : Vorlesungsumdruck. 2. Aufl. Bd. 1},
  edition   = {2. Aufl.},
  publisher = {Fachhochschule Aachen, Lehr- und Forschungsgebiet Thermodynamik und Verbrennungstechnik},
  address   = {Aachen},
  pages     = {Getr. Z{\"a}hlung : Ill. und graph. Darst.},
  year      = {2013},
  language  = {de}
}
@book{Esch2015,
  author    = {Esch, Thomas},
  title     = {Verbrennungsmotoren},
  edition   = {11. Aufl., [Umdruck]},
  publisher = {Fachhochschule Aachen, Lehr- und Forschungsgebiet Thermodynamik und Verbrennungstechnik},
  address   = {Aachen},
  pages     = {Getr. Z{\"a}hlung : Ill. und graph. Darst.},
  year      = {2015},
  language  = {de}
}
@book{Esch2013,
  author    = {Esch, Thomas},
  title     = {Verbrennungsmotoren},
  edition   = {10. Aufl., [Umdruck]},
  publisher = {Fachhochschule Aachen, Lehr- und Forschungsgebiet Thermodynamik und Verbrennungstechnik},
  address   = {Aachen},
  pages     = {Getr. Z{\"a}hlung : Ill. und graph. Darst.},
  year      = {2013},
  language  = {de}
}
@book{Esch2015,
  author    = {Esch, Thomas},
  title     = {Experimentelle Untersuchungen an Antriebssystemen von Kraft-, Luft- und Raumfahrzeugen : Vorlesungsumdruck. 3. Aufl. Bd. 1},
  edition   = {3. Aufl.},
  publisher = {Fachhochschule Aachen, Lehr- und Forschungsgebiet Thermodynamik und Verbrennungstechnik},
  address   = {Aachen},
  pages     = {Getr. Z{\"a}hlung : Ill. und graph. Darst.},
  year      = {2015},
  language  = {de}
}
@techreport{EschDammKalbhenn2013,
  author    = {Esch, Thomas and Damm, Marc Andr{\´e} and Kalbhenn, Hartmut},
  title     = {Auslegung und Simulation eines Hybridantriebs f{\"u}r den teilelektrischen Betrieb eines Luftfahrzeuges der allgemeinen Luftfahrt : Schlussbericht f{\"u}r das Forschungsvorhaben ; F{\"o}rderperiode 01.07.2009 - 31.05.2012},
  address   = {Aachen ; Hannover},
  doi       = {10.2314/GBV:780055411},
  pages     = {1 Online-Ressource (138 Seiten)},
  year      = {2013},
  language  = {de}
}
@article{KhayyamJamaliBabHadiasharetal.2020,
  author    = {Khayyam, Hamid and Jamali, Ali and Bab-Hadiashar, Alireza and Esch, Thomas and Ramakrishna, Seeram and Jalili, Mahdi and Naebe, Minoo},
  title     = {A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0},
  series = {IEEE Access},
  volume    = {8},
  journal   = {IEEE Access},
  number    = {Art. 9108222},
  publisher = {IEEE},
  address   = {New York, NY},
  issn      = {2169-3536},
  doi       = {10.1109/ACCESS.2020.2999898},
  pages     = {111381 -- 111393},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{KhayyamJamaliBabHadiasharetal.2020,
  author    = {Khayyam, Hamid and Jamali, Ali and Bab-Hadiashar, Alireza and Esch, Thomas and Ramakrishna, Seeram and Jalil, Mahdi and Naebe, Minoo},
  title     = {A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0},
  series = {IEEE Access},
  journal   = {IEEE Access},
  publisher = {IEEE},
  address   = {New York, NY},
  isbn      = {2169-3536},
  doi       = {10.1109/ACCESS.2020.2999898},
  pages     = {1 -- 12},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{KreyerMuellerEsch2020,
  author    = {Kreyer, J{\"o}rg and M{\"u}ller, Marvin and Esch, Thomas},
  title     = {A Calculation Methodology for Predicting Exhaust Mass Flows and Exhaust Temperature Profiles for Heavy-Duty Vehicles},
  series = {SAE International Journal of Commercial Vehicles},
  volume    = {13},
  journal   = {SAE International Journal of Commercial Vehicles},
  number    = {2},
  publisher = {SAE International},
  address   = {Warrendale, Pa.},
  issn      = {1946-3928},
  doi       = {10.4271/02-13-02-0009},
  pages     = {129 -- 143},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@inproceedings{HauggKreyerKemperetal.2020,
  author    = {Haugg, Albert Thomas and Kreyer, J{\"o}rg and Kemper, Hans and Hatesuer, Katerina and Esch, Thomas},
  title     = {Heat exchanger for ORC. adaptability and optimisation potentials},
  series = {IIR International Rankine 2020 Conference},
  booktitle = {IIR International Rankine 2020 Conference},
  doi       = {10.18462/iir.rankine.2020.1224},
  pages     = {10 Seiten},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}