@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{KreyerMuellerEsch2020,
  author    = {Kreyer, J{\"o}rg and M{\"u}ller, Marvin and Esch, Thomas},
  title     = {A Map-Based Model for the Determination of Fuel Consumption for Internal Combustion Engines as a Function of Flight Altitude},
  publisher = {DGLR},
  address   = {Bonn},
  doi       = {10.25967/490162},
  pages     = {13 Seiten},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@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}
}
@inproceedings{FingerKhalsaKreyeretal.2019,
  author    = {Finger, Felix and Khalsa, R. and Kreyer, J{\"o}rg and Mayntz, Joscha and Braun, Carsten and Dahmann, Peter and Esch, Thomas and Kemper, Hans and Schmitz, O. and Bragard, Michael},
  title     = {An approach to propulsion system modelling for the conceptual design of hybrid-electric general aviation aircraft},
  series = {Deutscher Luft- und Raumfahrtkongress 2019, 30.9.-2.10.2019, Darmstadt},
  booktitle = {Deutscher Luft- und Raumfahrtkongress 2019, 30.9.-2.10.2019, Darmstadt},
  pages     = {15 Seiten},
  year      = {2019},
  abstract  = {In this paper, an approach to propulsion system modelling for hybrid-electric general aviation aircraft is presented. Because the focus is on general aviation aircraft, only combinations of electric motors and reciprocating combustion engines are explored. Gas turbine hybrids will not be considered. The level of the component's models is appropriate for the conceptual design stage. They are simple and adaptable, so that a wide range of designs with morphologically different propulsive system architectures can be quickly compared. Modelling strategies for both mass and efficiency of each part of the propulsion system (engine, motor, battery and propeller) will be presented.},
  language  = {en}
}
@article{SchopenNarayanBeckmannetal.2024,
  author    = {Schopen, Oliver and Narayan, Sriram and Beckmann, Marvin and Najmi, Aezid-Ul-Hassan and Esch, Thomas and Shabani, Bahman},
  title     = {An EIS approach to quantify the effects of inlet air relative humidity on the performance of proton exchange membrane fuel cells: a pathway to developing a novel fault diagnostic method},
  series = {International Journal of Hydrogen Energy},
  volume    = {58},
  journal   = {International Journal of Hydrogen Energy},
  number    = {8},
  publisher = {Elsevier},
  address   = {Amsterdam},
  isbn      = {0360-3199 (print)},
  issn      = {1879-3487 (online)},
  doi       = {10.1016/j.ijhydene.2024.01.218},
  pages     = {1302 -- 1315},
  year      = {2024},
  abstract  = {In this work, the effect of low air relative humidity on the operation of a polymer electrolyte membrane fuel cell is investigated. An innovative method through performing in situ electrochemical impedance spectroscopy is utilised to quantify the effect of inlet air relative humidity at the cathode side on internal ionic resistances and output voltage of the fuel cell. In addition, algorithms are developed to analyse the electrochemical characteristics of the fuel cell. For the specific fuel cell stack used in this study, the membrane resistance drops by over 39 \% and the cathode side charge transfer resistance decreases by 23 \% after increasing the humidity from 30 \% to 85 \%, while the results of static operation also show an increase of ∼2.2 \% in the voltage output after increasing the relative humidity from 30 \% to 85 \%. In dynamic operation, visible drying effects occur at < 50 \% relative humidity, whereby the increase of the air side stoichiometry increases the drying effects. Furthermore, other parameters, such as hydrogen humidification, internal stack structure, and operating parameters like stoichiometry, pressure, and temperature affect the overall water balance. Therefore, the optimal humidification range must be determined by considering all these parameters to maximise the fuel cell performance and durability. The results of this study are used to develop a health management system to ensure sufficient humidification by continuously monitoring the fuel cell polarisation data and electrochemical impedance spectroscopy indicators.},
  language  = {en}
}
@article{FunkeEschRoosen2022,
  author    = {Funke, Harald and Esch, Thomas and Roosen, Petra},
  title     = {Antriebssystemanpassungen zur Verwendung von LPG als Flugkraftstoff},
  series = {Motortechnische Zeitschrift (MTZ)},
  volume    = {2022},
  journal   = {Motortechnische Zeitschrift (MTZ)},
  number    = {83},
  publisher = {Springer Nature},
  address   = {Basel},
  doi       = {10.1007/s35146-021-0778-2},
  pages     = {58 -- 62},
  year      = {2022},
  abstract  = {Auch in der allgemeinen Luftfahrt w{\"a}re es w{\"u}nschenswert, die bereits vorhandenen Verbrennungsmotoren mit weniger CO₂-tr{\"a}chtigen Kraftstoffen als dem heute weit verbreiteten Avgas 100LL betreiben zu k{\"o}nnen. Es ist anzunehmen, dass im Vergleich die unter Normalbedingungen gasf{\"o}rmigen Kraftstoffe CNG, LPG oder LNG deutlich weniger Emissionen produzieren. Erforderliche Antriebssystemanpassungen wurden im Rahmen eines Forschungsprojekts an der FH Aachen untersucht.},
  language  = {de}
}
@article{FayyaziSardarThomasetal.2023,
  author    = {Fayyazi, Mojgan and Sardar, Paramjotsingh and Thomas, Sumit Infent and Daghigh, Roonak and Jamali, Ali and Esch, Thomas and Kemper, Hans and Langari, Reza and Khayyam, Hamid},
  title     = {Artificial intelligence/machine learning in energy management systems, control, and optimization of hydrogen fuel cell vehicles},
  volume    = {15},
  number    = {6},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/su15065249},
  pages     = {38},
  year      = {2023},
  abstract  = {Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.},
  language  = {en}
}
@misc{SchmitzSchebitzEsch1997,
  author    = {Schmitz, G{\"u}nter and Schebitz, Michael and Esch, Thomas},
  title     = {Aus der Ruhelage selbstanziehender elektromagnetischer Aktuator},
  year      = {1997},
  abstract  = {Elektromagnetischer Aktuator zur Bet{\"a}tigung eines Stellgliedes (2), mit wenigstens einem Elektromagneten (4) und einem mit dem Stellglied (2) verbundenen Anker (3), der gegen die Kraft einer R{\"u}ckstellfeder (6) aus seiner Ruhelage in Richtung auf den Elektromagneten (4) bewegbar ist, mit einer R{\"u}ckstellfeder (6), die eine nicht lineare, bezogen auf die Ruhelage des Ankers (3) progressiv ansteigende Kennlinie aufweist.},
  language  = {de}
}
@inproceedings{VeettilRakshitSchopenetal.2022,
  author    = {Veettil, Yadu Krishna Morassery and Rakshit, Shantam and Schopen, Oliver and Kemper, Hans and Esch, Thomas and Shabani, Bahman},
  title     = {Automated Control System Strategies to Ensure Safety of PEM Fuel Cells Using Kalman Filters},
  series = {Proceedings of the 7th International Conference and Exhibition on Sustainable Energy and Advanced Materials (ICE-SEAM 2021), Melaka, Malaysia},
  booktitle = {Proceedings of the 7th International Conference and Exhibition on Sustainable Energy and Advanced Materials (ICE-SEAM 2021), Melaka, Malaysia},
  editor    = {Bin Abdollah, Mohd Fadzli and Amiruddin, Hilmi and Singh, Amrik Singh Phuman and Munir, Fudhail Abdul and Ibrahim, Asriana},
  publisher = {Springer Nature},
  address   = {Singapore},
  isbn      = {978-981-19-3178-9},
  issn      = {2195-4356},
  doi       = {10.1007/978-981-19-3179-6_55},
  pages     = {296 -- 299},
  year      = {2022},
  abstract  = {Having well-defined control strategies for fuel cells, that can efficiently detect errors and take corrective action is critically important for safety in all applications, and especially so in aviation. The algorithms not only ensure operator safety by monitoring the fuel cell and connected components, but also contribute to extending the health of the fuel cell, its durability and safe operation over its lifetime. While sensors are used to provide peripheral data surrounding the fuel cell, the internal states of the fuel cell cannot be directly measured. To overcome this restriction, Kalman Filter has been implemented as an internal state observer. Other safety conditions are evaluated using real-time data from every connected sensor and corrective actions automatically take place to ensure safety. The algorithms discussed in this paper have been validated thorough Model-in-the-Loop (MiL) tests as well as practical validation at a dedicated test bench.},
  language  = {en}
}