@article{GerhardtCooperWhitbreadetal.1985,
  author    = {Gerhardt, Hans Joachim and Cooper, K.-R. and Whitbread, R. and Gary, K.-P. (u.a.)},
  title     = {A comparison of aerodynamic drag measurements on model trucks in closed-jet and open-jet wind tunnels},
  pages     = {261 -- 274},
  year      = {1985},
  language  = {en}
}
@article{DachwaldTsinas1994,
  author    = {Dachwald, Bernd and Tsinas, L.},
  title     = {A combined neural and genetic learning algorithm / Tsinas, L. ; Dachwald, B.},
  series = {Proceedings of the First IEEE Conference on Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence.},
  journal   = {Proceedings of the First IEEE Conference on Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence.},
  address   = {Orlando, Fl},
  isbn      = {0-7803-1899-4},
  pages     = {770 -- 774},
  year      = {1994},
  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{DachwaldOhndorf2007,
  author    = {Dachwald, Bernd and Ohndorf, A.},
  title     = {1st ACT Global Trajectory Optimisation Competition : Results found at DLR},
  series = {Acta Astronautica. 61 (2007), H. 9},
  journal   = {Acta Astronautica. 61 (2007), H. 9},
  isbn      = {0094-5765},
  pages     = {742 -- 752},
  year      = {2007},
  language  = {en}
}