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

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

Transportation Research Part A: Policy and Practice, 2013, vol. 48, issue C, 123-131

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.

Keywords: Electric vehicles; Intelligent charging; Drivers’ mobility predictions (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0965856412001504
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:transa:v:48:y:2013:i:c:p:123-131

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.tra.2012.10.011

Access Statistics for this article

Transportation Research Part A: Policy and Practice is currently edited by John (J.M.) Rose

More articles in Transportation Research Part A: Policy and Practice from Elsevier
Bibliographic data for series maintained by Catherine Liu ().