@misc{ZimmerYoganathanGiebeletal.2014,
  author    = {Zimmer, Cordula and Yoganathan, K. and Giebel, Friederike and Luetzenkirchen-Hecht, Dirk and Klaus, T. and Peter, Kallis.},
  title     = {Photoemission properties of LaB6 thin films for the use in PIDs},
  year      = {2014},
  language  = {en}
}
@inproceedings{LoechteGebertHemingetal.2018,
  author    = {L{\"o}chte, Andre and Gebert, Ole and Heming, Daniel and Kallis, Klaus and Gl{\"o}sek{\"o}tter, Peter},
  title     = {State estimation of zinc air batteries using neural networks},
  series = {IWANN 2017: Advances in Computational Intelligence},
  volume    = {2018},
  booktitle = {IWANN 2017: Advances in Computational Intelligence},
  editor    = {Springer, Axel},
  issn      = {0941-0643},
  doi       = {10.1007/s00521-018-3705-9},
  pages     = {1 -- 9},
  year      = {2018},
  abstract  = {The main task of battery management systems is to keep the working area of the battery in a safe state. Estimation of the state of charge and the state of health is therefore essential. The traditional way uses the voltage level of a battery to determine those values. Modern metal air batteries provide a flat voltage characteristic which necessitates new approaches. One promising technique is the electrochemical impedance spectroscopy, which measures the AC resistance for a set of different frequencies. Previous approaches match the measured impedances with a nonlinear equivalent circuit, which needs a lot of time to solve a nonlinear least-squares problem. This paper combines the electrochemical impedance spectroscopy with neural networks to speed up the state estimation using the example of zinc air batteries. Moreover, these networks are trained with different subsets of the spectra as input data in order to determine the required number of frequencies.},
  language  = {de}
}