Ben Gharbia, Ibtihel (IFP Energies nouvelles), De Nunzio, Giovanni (IFP Energies nouvelles)

Location Planning of Fast Charging Infrastructure for Electric Vehicles and System Resilience Analysis Via a Demand-Based Queueing Model

Scheduled for presentation during the Invited Session "Sustainable Electrified Road Transportation" (ThAT3), Thursday, September 26, 2024, 12:10−12:30, Salon 6

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

The rapid uptake of electric vehicles necessitates an equally rapid development of an efficient and effective charging infrastructure network. In this paper, we build upon existing works to introduce a new charging infrastructure placement method aimed at optimizing trip time for long-distance journeys, also considering travel and charging demand, as well as land-use spatial constraints. The methodology relies upon an optimal routing strategy for EVs capable of identifying the spatio-temporal location of energy needs. Furthermore, the well-known M/M/C queueing model is used to estimate the waiting time that EVs are likely to experience at charging stations. A novel strategy to estimate the parameters of M/M/C model based on travel demand and energy needs is presented. The charging station location problem is cast as a bi-level optimization, then decomposed and tackled via a heuristic algorithm to enhance tractability. By applying our method to the French national charging infrastructure, we demonstrate significant improvements in overall trip time and robustness (e.g. system resilience to increased travel and charging demand, or resilience to out-of-order charging points) compared to the current infrastructure.