How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach

Abstract

This paper explores the impact of electric vehicles (EVs) on traffic congestion and energy consumption by proposing an integrated bi-level framework comprising of: a) a dynamic micro-scale traffic simulation suitable for modelling current and hypothetical traffic and charging demand scenarios and b) a queue model for capturing the impact of fast charging station use, informed by traffic flows, travel distances, availability of charging infrastructure and estimated vehicle battery state of charge. To the best of our knowledge, this paper represents the first integrated analysis of potential traffic congestion and energy infrastructure impacts linked to EV uptake, based on real traffic flows and the placement and design of existing fast-charging infrastructure. Results showcase that the integrated queue-energy-transport modelling framework can predict correctly the limitations of the EV infrastructure as well as the traffic congestion evolution. The modelling approach identifies concrete pain points to be addressed in both traffic and energy management and planning. The code for this project can be found at : https://github.com/ Future-Mobility-Lab/EV-charging-impact Index Terms-electric vehicles, traffic simulation modelling, queue modelling, recharging scenario evaluation, fast charge impact modelling I. MOTIVATIONThe adoption of electric vehicles (EVs) at large scale around the globe is largely dependent on two key factors: charging accessibility for its consumers and availability of sufficient electrical grid capacity to support vehicle charging, particularly where fast charging infrastructure is necessary. Under ideal conditions for the consumer, access to an EV charging station should be just as convenient as current access to a petrol station. Several studies have been conducted in recent years to model and identify suitable locations for such charging stations [2], [3]. The majority of these studies are based on the behavioural pattern of combustion engine (CE) drivers. However, the behaviour of electric vehicle (EV) drivers is significantly different in terms of location selection and their waiting time to charge [2], [4]. Multiple factors can influence the decision of EV drivers to choose a specific location to charge, among which we cite: the travel time