Zusammenfassung
Operating artificial neural networks (ANN) and especially the training of ANN requires an available database. Using the cloud connection of vehicles, measurement data from single vehicles and from whole fleets can be collected in big scale.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
M. Stöcker, Modellbasiertes Thermomanagement mit Navigationsdaten zur Energieeffizienzsteigerung der Fahrzeugkühlung. Dissertation - TU Darmstadt, 1st ed. Aachen: Shaker, 2018.
C. Ress, D. Balzer, A. Bracht, S. Durekovic, and J. Löwenaus, ADASIS Protocol for Advanced In-Vehicle Applications: ADASIS Forum. [Online] Available: http://durekovic.com/publications/default.html. Accessed on: Jan. 06 2020.
M. Steinhart, Entwicklung eines ADAS-basierten Kreuzungsidentifikationsmodells für das vorausschauende Thermomanagement in Personenkraftfahrzeugen: Master Thesis, 2014.
K. Weiler, Entwicklung eines Fahrer- Fahrzeugmodells zur Vorhersage der Motorbetriebspunkte und der Fahrzeuggeschwindigkeit aus ADAS-Streckendaten: Master Thesis, 2014.
O. Nelles, Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models: Springer, 2001.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning: MIT Press, 2016.
F. M. Bianchi, E. Maiorino, M. C. Kampffmeyer, A. Rizzi, and R. Jenssen, “An overview and comparative analysis of Recurrent Neural Networks for Short Term oad Forecasting,” CoRR, 2017.
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, 1997.
F. Kirschbaum, “Modellbasierte Applikationsverfahren: Vorlesungsskript,” 2017.
D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” Dec. 2014. [Online] Available: http://arxiv.org/pdf/1412.6980v9.
Y. Bengio, “Practical recommendations for gradient-based training of deep architectures,” Jun. 2012. [Online] Available: http://arxiv.org/pdf/1206.5533v2.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning Internal Representations by Error Propagation,” 1985.
W. H. Press, Numerical recipes in C: The art of scientific computing, 2nd ed. Cambridge, New York: Cambridge University Press, 1992.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Korthals, F., Stöcker, M., Rinderknecht, S. (2020). Artificial Intelligence in predictive thermal management for passenger cars. In: Bargende, M., Reuss, HC., Wagner, A. (eds) 20. Internationales Stuttgarter Symposium . Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-30995-4_46
Download citation
DOI: https://doi.org/10.1007/978-3-658-30995-4_46
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-30994-7
Online ISBN: 978-3-658-30995-4
eBook Packages: Computer Science and Engineering (German Language)