Driver-aware charging infrastructure design

TL;DR

This paper introduces a new mathematical model for optimally placing public electric vehicle charging stations in cities, considering individual driver demand, traffic patterns, and charging options to improve infrastructure planning.

Contribution

It formulates the station placement as a combinatorial optimization problem that accounts for detailed driver behavior and can be efficiently solved for large urban areas.

Findings

Model can optimize station placement for cities with up to 600,000 inhabitants.

Approach effectively incorporates traffic data and driver demand.

Solves large instances efficiently with reformulation techniques.

Abstract

Public charging infrastructure plays a crucial role in the context of electrifying the private mobility sector in particular for urban regions. Against this background, we develop a new mathematical model for the optimal placement of public charging stations for electric vehicles in cities. While existing approaches strongly aggregate traffic information or are only applicable to small instances, we formulate the problem as a specific combinatorial optimization problem that incorporates individual demand and temporal interactions of drivers, exact positioning of charging stations, as well as various charging speeds, and realistic charging curves. We show that the problem can be naturally cast as an integer program that, together with different reformulation techniques, can be efficiently solved for large instances. More specifically, we show that our approach can compute optimal placements of charging stations for instances based on traffic data for cities with up to $600\,000$ inhabitants and future electrification rates of up to $15\%$.