Estimation of Electric Vehicle Public Charging Demand using Cellphone Data and Points of Interest-based Segmentation
This addresses the need for robust EV charging infrastructure to support road electrification, though it is incremental as it builds on existing data and segmentation approaches.
The study tackled the problem of estimating public charging demand for electric vehicles by using cellphone data to assess neighborhood-level demand and a points of interest-based segmentation model to allocate needs among charging technologies, applied to Brussels with results showing useful trends for city planning.
The race for road electrification has started, and convincing drivers to switch from fuel-powered vehicles to electric vehicles requires robust Electric Vehicle (EV) charging infrastructure. This article proposes an innovative EV charging demand estimation and segmentation method. First, we estimate the charging demand at a neighborhood granularity using cellular signaling data. Second, we propose a segmentation model to partition the total charging needs among different charging technology: normal, semi-rapid, and fast charging. The segmentation model, an approach based on the city's points of interest, is a state-of-the-art method that derives useful trends applicable to city planning. A case study for the city of Brussels is proposed.