Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Electric Energy Consumption in Urban Space

Sedar Olmez
* ORCID logo ,
Jason Thompson
,
Ellie Marfleet
,
Keiran Suchak
ORCID logo ,
Alison Heppenstall
,
Ed Manley
,
Annabel Whipp
ORCID logo ,
Rajith Vidanaarachchi
Version 1 : Received: 25 April 2022 / Approved: 29 April 2022 / Online: 29 April 2022 (11:05:15 CEST)

A peer-reviewed article of this Preprint also exists.

Olmez, S.; Thompson, J.; Marfleet, E.; Suchak, K.; Heppenstall, A.; Manley, E.; Whipp, A.; Vidanaarachchi, R. An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space. Energies 2022, 15, 4031. Olmez, S.; Thompson, J.; Marfleet, E.; Suchak, K.; Heppenstall, A.; Manley, E.; Whipp, A.; Vidanaarachchi, R. An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space. Energies 2022, 15, 4031.

Abstract

By 2020, over 100 countries expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to proven environmental and health implications of internal combustion engine vehicles (ICEVs), evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in fuel consumption, thus, simulating various levels of individual behaviour enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that 1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour) and 2) on average, for tested vehicles, EV/PHEVs were £116.33 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.

Keywords

agent-based model; electric vehicles; traffic simulation; energy intake; urban environment; fuel costs; public policy; electric mobility

Subject

Engineering, Transportation Science and Technology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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