Assessing driving pattern factors for the specific energy use of electric vehicles: A factor analysis approach from case study data of the Mitsubishi i–MiEV minicar

Battery electric vehicles (BEVs) promise to contribute to the achievement of a more sustainable transport system. In order to estimate energetic efficiency potentials while taking into account operating conditions, insights on the factors of energy use are required. The driving pattern, i.e. the characteristics of the driving profile, is expected to affect the vehicles’ energy use to a great extent. This paper investigates whether the driving pattern parameters that have proved to be relevant for the fuel consumption of ICVs also apply to BEVs. In consequence, the authors analyse correlations between driving pattern factors and the specific energy use of BEVs. In order to record driving and energy data, four commercially used battery electric minicars were equipped with tracking devices. The resulting dataset contains 42 vehicle months. The driving pattern is described in 45 parameters that are calculated for segments of the logged driving profiles. Exploratory factor analysis is applied to reduce the large number of parameters into a smaller number of independent factors. Six independent driving pattern factors are identified. Suitable correlation coefficients are calculated to check for dependencies with energy use. The most significant correlations were found for the intensity of acceleration/deceleration, as well as for the oscillation factor. The authors' results could be used to inform further studies where driving pattern factors for ICVs and BEVs are directly compared. Also, results can be used to develop specific driving school training programs to learn to drive BEVs in an energy efficient manner.

• Record URL:
  https://doi.org/10.1016/j.trd.2017.11.011
• Record URL:
  https://www.sciencedirect.com/science/article/pii/S1361920916306538
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Braun, Andreas
  • Rid, Wolfgang
• Publication Date: 2018-1

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 225-238
• Serial:
  • Transportation Research Part D: Transport and Environment
  • Volume: 58
  • Publisher: Elsevier
  • ISSN: 1361-9209
  • Serial URL: http://www.sciencedirect.com/science/journal/13619209

Subject/Index Terms

• TRT Terms: Case studies; Correlation analysis; Detection and identification systems; Electric vehicles; Energy consumption; Factor analysis; Miniature automobiles
• Uncontrolled Terms: Driving cycles; Driving patterns
• Subject Areas: Data and Information Technology; Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01660569
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 20 2018 9:31AM