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How accurate are drivers’ predictions of their own mobility? Accounting for psychological factors in the development of intelligent charging technology for electric vehicles

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  • Hahnel, Ulf J.J.
  • Gölz, Sebastian
  • Spada, Hans

Abstract

Intelligent load management systems (ILMS) for electric vehicles (EVs) would make it possible to link EV use to renewable energy sources. ILMS require information about the departure time and length of EV drivers’ upcoming trips to optimize the charging process depending on the availability of renewable energy in the grid. Inaccurate information may lead to insufficient battery levels or inefficient charging processes. In a field test during two weeks 60 participants predicted the departure time and trip length of their upcoming trips after having arrived at home with their own gasoline-powered cars. Actual mobility behavior was assessed by means of logbooks and GPS tracking devices. The results show that participants are on average able to accurately predict their departure times and trip lengths although for some outliers their prediction errors would potentially have led to insufficient battery levels. The type of trip (work, leisure, shopping) significantly influenced the accuracy of mobility predictions.

Suggested Citation

  • Hahnel, Ulf J.J. & Gölz, Sebastian & Spada, Hans, 2013. "How accurate are drivers’ predictions of their own mobility? Accounting for psychological factors in the development of intelligent charging technology for electric vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 48(C), pages 123-131.
    • Handle: RePEc:eee:transa:v:48:y:2013:i:c:p:123-131
    DOI: 10.1016/j.tra.2012.10.011
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    References listed on IDEAS

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    Cited by:

    1. Luo, Yugong & Zhu, Tao & Wan, Shuang & Zhang, Shuwei & Li, Keqiang, 2016. "Optimal charging scheduling for large-scale EV (electric vehicle) deployment based on the interaction of the smart-grid and intelligent-transport systems," Energy, Elsevier, vol. 97(C), pages 359-368.
    2. Lagomarsino, Maria & van der Kam, Mart & Parra, David & Hahnel, Ulf J.J., 2022. "Do I need to charge right now? Tailored choice architecture design can increase preferences for electric vehicle smart charging," Energy Policy, Elsevier, vol. 162(C).

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