Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach

This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.

• Record URL:
  • https://doi.org/10.1155/2020/8850654
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
• Supplemental Notes:
  • © 2020 Chenxi Chen et al.
• Authors:
  • Chen, Chenxi
  • Song, Yang
  • Hu, Xianbiao
  • Guardiola, Ivan G
• Publication Date: 2020-11

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: Article ID 8850654
• Serial:
  • Journal of Advanced Transportation
  • Volume: 2020
  • Publisher: John Wiley & Sons, Incorporated
  • ISSN: 0197-6729
  • EISSN: 2042-3195
  • Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Data analysis; Electric vehicle charging; Electric vehicles; Energy consumption
• Identifier Terms: Principal Component Analysis
• Geographic Terms: Kansas City (Missouri)
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01760639
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 21 2020 10:05AM