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Statistics Instead of Stopover—Range Predictions for Electric Vehicles

In: Operations Research Proceedings 2016

Author

Listed:
  • Christian Kluge

    (Technische Hochschule Deggendorf
    Technologiecampus Grafenau)

  • Stefan Schuster

    (Technische Hochschule Deggendorf)

  • Diana Sellner

    (Technische Hochschule Deggendorf)

Abstract

Electric vehicles (EVs) can play a central role in today’s efforts to reduce CO $$_2$$ emission and slow down the climate change. Two of the most important reasons against purchase or use of an EV are its short range and long charging times. In the project “E-WALD—Elektromobilität Bayerischer Wald”, we develop mathematical models to predict the range of EVs by estimating the electrical power consumption (EPC) along possible routes. Based on the EPC forecasts the range is calculated and visualized by a range polygon on a navigation map. The models are based on data that are constantly collected by cars within a commercial car fleet. The dataset is modelled with three methods: a linear model, an additive model and a fully nonparametric model. To fit the linear model, ordinary least squares (OLS) regression as well as linear median regression are applied. The other models are fitted by modern machine learning algorithms: the additive model is fitted by boosting algorithm and the fully nonparametric model is fitted by support vector regression (SVR). The models are compared by mean absolute error (MAE). Our research findings show that data preparation is more influential than the chosen model.

Suggested Citation

  • Christian Kluge & Stefan Schuster & Diana Sellner, 2018. "Statistics Instead of Stopover—Range Predictions for Electric Vehicles," Operations Research Proceedings, in: Andreas Fink & Armin Fügenschuh & Martin Josef Geiger (ed.), Operations Research Proceedings 2016, pages 51-56, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-55702-1_8
    DOI: 10.1007/978-3-319-55702-1_8
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