@article{RuppSchulzeKuperjans2018,
  author    = {Rupp, Matthias and Schulze, Sven and Kuperjans, Isabel},
  title     = {Comparative life cycle analysis of conventional and hybrid heavy-duty trucks},
  series = {World electric vehicle journal},
  volume    = {9},
  journal   = {World electric vehicle journal},
  number    = {2},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2032-6653},
  doi       = {10.3390/wevj9020033},
  pages     = {Article No. 33},
  year      = {2018},
  abstract  = {Heavy-duty trucks are one of the main contributors to greenhouse gas emissions in German traffic. Drivetrain electrification is an option to reduce tailpipe emissions by increasing energy conversion efficiency. To evaluate the vehicle's environmental impacts, it is necessary to consider the entire life cycle. In addition to the daily use, it is also necessary to include the impact of production and disposal. This study presents the comparative life cycle analysis of a parallel hybrid and a conventional heavy-duty truck in long-haul operation. Assuming a uniform vehicle glider, only the differing parts of both drivetrains are taken into account to calculate the environmental burdens of the production. The use phase is modeled by a backward simulation in MATLAB/Simulink considering a characteristic driving cycle. A break-even analysis is conducted to show at what mileage the larger CO2eq emissions due to the production of the electric drivetrain are compensated. The effect of parameter variation on the break-even mileage is investigated by a sensitivity analysis. The results of this analysis show the difference in CO2eq/t km is negative, indicating that the hybrid vehicle releases 4.34 g CO2eq/t km over a lifetime fewer emissions compared to the diesel truck. The break-even analysis also emphasizes the advantages of the electrified drivetrain, compensating the larger emissions generated during production after already a distance of 15,800 km (approx. 1.5 months of operation time). The intersection coordinates, distance, and CO2eq, strongly depend on fuel, emissions for battery production and the driving profile, which lead to nearly all parameter variations showing an increase in break-even distance.},
  language  = {en}
}
@article{PfaffGidaszewskiSchmidt2020,
  author    = {Pfaff, Raphael and Gidaszewski, Lars and Schmidt, Bernd},
  title     = {Ber{\"u}cksichtigung von No Fault Found im Diagnose- und Instandhaltungssystem von Schienenfahrzeugen},
  series = {ETR - Eisenbahntechnische Rundschau},
  journal   = {ETR - Eisenbahntechnische Rundschau},
  number    = {5},
  publisher = {DVV Media Group},
  address   = {Hamburg},
  issn      = {0013-2845},
  pages     = {56 -- 59},
  year      = {2020},
  abstract  = {Intermittierende und nicht reproduzierbare Fehler, auch als No Fault Found bezeichnet, treten in praktisch allen Bereichen auf und sorgen f{\"u}r hohe Kosten. Diese sind h{\"a}ufig auf unpr{\"a}zise Fehlerbeschreibungen zur{\"u}ckzuf{\"u}hren. Im vorliegenden Beitrag werden Anpassungen der Vorgehensweise bei der Entwicklung und Anpassungen des Diagnosesystems vorgeschlagen.},
  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{HoevelerBauknechtWolfetal.2020,
  author    = {Hoeveler, B. and Bauknecht, Andr{\´e} and Wolf, C. Christian and Janser, Frank},
  title     = {Wind-Tunnel Study of a Wing-Embedded Lifting Fan Remaining Open in Cruise Flight},
  series = {Journal of Aircraft},
  volume    = {57},
  journal   = {Journal of Aircraft},
  number    = {4},
  publisher = {AIAA},
  address   = {Reston, Va.},
  issn      = {1533-3868},
  doi       = {10.2514/1.C035422},
  year      = {2020},
  abstract  = {It is investigated whether a nonrotating lifting fan remaining uncovered during cruise flight, as opposed to being covered by a shutter system, can be realized with limited additional drag and loss of lift during cruise flight. A wind-tunnel study of a wing-embedded lifting fan has been conducted at the Side Wind Test Facility G{\"o}ttingen of DLR, German Aerospace Center in G{\"o}ttingen using force, pressure, and stereoscopic particle image velocimetry techniques. The study showed that a step on the lower side of the wing in front of the lifting fan duct increases the lift-to-drag ratio of the whole model by up to 25\% for all positive angles of attack. Different sizes and inclinations of the step had limited influence on the surface pressure distribution. The data indicate that these parameters can be optimized to maximize the lift-to-drag ratio. A doubling of the curvature radius of the lifting fan duct inlet lip on the upper side of the wing affected the lift-to-drag ratio by less than 1\%. The lifting fan duct inlet curvature can therefore be optimized to maximize the vertical fan thrust of the rotating lifting fan during hovering without affecting the cruise flight performance with a nonrotating fan.},
  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}
}
@article{GoettenHavermannBraunetal.2020,
  author    = {G{\"o}tten, Falk and Havermann, Marc and Braun, Carsten and Marino, Matthew and Bil, Cees},
  title     = {Improved Form Factor for Drag Estimation of Fuselages with Various Cross Sections},
  series = {Journal of Aircraft},
  journal   = {Journal of Aircraft},
  publisher = {AIAA},
  address   = {Reston, Va.},
  issn      = {1533-3868},
  doi       = {10.2514/1.C036032},
  pages     = {1 -- 13},
  year      = {2020},
  abstract  = {The paper presents an aerodynamic investigation of 70 different streamlined bodies with fineness ratios ranging from 2 to 10. The bodies are chosen to idealize both unmanned and small manned aircraft fuselages and feature cross-sectional shapes that vary from circular to quadratic. The study focuses on friction and pressure drag in dependency of the individual body's fineness ratio and cross section. The drag forces are normalized with the respective body's wetted area to comply with an empirical drag estimation procedure. Although the friction drag coefficient then stays rather constant for all bodies, their pressure drag coefficients decrease with an increase in fineness ratio. Referring the pressure drag coefficient to the bodies' cross-sectional areas shows a distinct pressure drag minimum at a fineness ratio of about three. The pressure drag of bodies with a quadratic cross section is generally higher than for bodies of revolution. The results are used to derive an improved form factor that can be employed in a classic empirical drag estimation method. The improved formulation takes both the fineness ratio and cross-sectional shape into account. It shows superior accuracy in estimating streamlined body drag when compared with experimental data and other form factor formulations of the literature.},
  language  = {en}
}
@article{WilbringEnningPfaffetal.2020,
  author    = {Wilbring, Daniela and Enning, Manfred and Pfaff, Raphael and Schmidt, Bernd},
  title     = {Neue Perspektiven f{\"u}r die Bahn in der Produktions- und Distributionslogistik durch Prozessautomation},
  series = {ETR - Eisenbahntechnische Rundschau},
  volume    = {69},
  journal   = {ETR - Eisenbahntechnische Rundschau},
  number    = {3},
  issn      = {0013-2845},
  pages     = {15 -- 19},
  year      = {2020},
  language  = {de}
}
@article{Schueckhaus2020,
  author    = {Sch{\"u}ckhaus, Ulrich},
  title     = {Die SkyCab-Erfinder im WFMG-Interview},
  series = {Business in MG},
  journal   = {Business in MG},
  number    = {1},
  pages     = {6 -- 7},
  year      = {2020},
  language  = {de}
}
@article{SerrorHackHenzeetal.2021,
  author    = {Serror, Martin and Hack, Sacha and Henze, Martin and Schuba, Marko and Wehrle, Klaus},
  title     = {Challenges and Opportunities in Securing the Industrial Internet of Things},
  series = {IEEE Transactions on Industrial Informatics},
  volume    = {17},
  journal   = {IEEE Transactions on Industrial Informatics},
  number    = {5},
  publisher = {IEEE},
  address   = {New York},
  issn      = {1941-0050},
  doi       = {10.1109/TII.2020.3023507},
  pages     = {2985 -- 2996},
  year      = {2021},
  language  = {en}
}