Zusammenfassung
Alternative drives become more important in our society continuously. Due to the target change of mobility from conventional to emission-free cars based on renewable energies, existing synthetic driving cycles are extended to determine the fuel consumption of hybrid vehicles. In reality the fuel consumption and the green gas emission of for example a battery electric vehicle with range extender depend on the operational state of the range extender. In this paper a method based on artificial neural networks is provided for the detection of the operational state of a range extender. The database for the training of the neural network contain measurements of the velocity of the car and the current of the traction battery.
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Literatur
[1] PREMIUM, „Plug-In, Range-Extender, und Elektrofahrzeuge unter realen Mobilitätsumständen: Infrastruktur, Umweltbedingungen und Marktakzeptanz,“ http://www.premium.uni-passau.de/das-projekt/, 2017.
[2] RUHRAUTOe, „RUHRAUTOe,“ http://www.ruhrauto-e.de/, 2017.
[3] colognE-mobil, „colognE-mobil - Elektromobilitätslösungen für NRW,“ http://colognemobil.de/, 2017.
[4] K. Reif, Kraftfahrzeug-Hybridantriebe, Vieweg+Teubner Verlag, 2012.
[5] UN/ECE, „Regelung Nr. 101 der Wirtschaftskommision der Vereinten Nationen für Europa,“ 2012.
[6] M. Koppers und P. Driesch, „Simulationsgestützte Analyse verschiedener (teil-) elektrifizierter Antriebsvarianten für Pkw unter realen Betriebsbedingungen,“ in Beiträge der 8. GMM-Fachtagung, Dortmund, VDA-Verlag, 2017, pp. 159-164.
[7] C. Bishop, Pattern Recognition and Machine Learning, New York: Springer-Verlag, 2006.
[8] Google Inc., „Google Maps,“ [Online]. Available: www.google.de/maps.
[9] V. Nair und G. E. Hinton, „Rectified linear units improve restricted boltzmann machines,“ in Proceedings of the 27th International Conference on Machine Learning, Haifa, 2010.
[10] T. Hastie, R. Tibshirani und J. Friedman, The Elements of Statistical Learning, New York: Springer-Verlag, 2009.
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Blume, S., Reicherts, S., Driesch, P., Schweig, S., Schramm, D. (2018). Identifizierung von Range Extender Fahrten anhand realer Bewegungsprofile durch künstliche neuronale Netze. In: Proff, H., Fojcik, T. (eds) Mobilität und digitale Transformation. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-20779-3_15
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DOI: https://doi.org/10.1007/978-3-658-20779-3_15
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