Using mobility data and agent-based models to generate future E-mobility charging demand patterns

Bart Nijenhuis, Sjoerd C. Doumen, Jens Hönen, Gerwin Hoogsteen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)
59 Downloads (Pure)

Abstract

This paper presents an approach to generating electric vehicle (EV) charging demand patterns for residential areas, working locations, and public charging points for three different user groups and nine typical trip motives. We present a method to convert single-day trip diaries of a national travel survey to weekly vehicle mobility patterns. These weekly vehicle mobility patterns are used as input for a flexible agent-based model that describes the charging probability of an EV based on its State-of-Charge (SoC). The results are presented in a case study with 1,000 EV schedules and validated against the original dataset. The resulting approach is a feasible method to create EV demand patterns that can be used in a wide variety of research directions involving smart EV charging. The source code for the tool is publicly available.
Original languageEnglish
Title of host publicationCIRED Porto Workshop 2022: E-mobility and power distribution systems
PublisherIET
Pages214-218
Number of pages5
Volume2022
ISBN (Print)978-1-83953-705-9
DOIs
Publication statusPublished - 3 Jun 2022
EventCIRED Porto Workshop 2022: E-mobility and power distribution systems - Hybrid Conference, Porto, Portugal
Duration: 2 Jun 20223 Jun 2022

Conference

ConferenceCIRED Porto Workshop 2022
Country/TerritoryPortugal
CityPorto
Period2/06/223/06/22

Keywords

  • 22/4 OA procedure

Fingerprint

Dive into the research topics of 'Using mobility data and agent-based models to generate future E-mobility charging demand patterns'. Together they form a unique fingerprint.

Cite this