Полный текст

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Конспект

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 aggregated 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. Our demand estimation results heavily correlate with the government’s predictions under similar assumptions. The segmentation reveals clear city patterns, such as transportation hubs, commercial and industrial zones or residential districts, and stresses the importance of a deployment plan involving all available charging technologies.

Сведения

Название
Estimation of Public Charging Demand Using Cellphone Data and Points of Interest-Based Segmentation
Автор
Radermecker, Victor 1 ; Vanhaverbeke, Lieselot 2   Логотип VIAFID ORCID 

 MOBI Research Group, Université Libre de Bruxelles, 1050 Brussels, Belgium 
 Department BUTO—Business Technology & Operations, Faculty of Social Sciences & Solvay Business School, Vrije Universiteit Brussel, 1050 Brussels, Belgium 
Первая страница
35
Год публикации
2023
Дата публикации
2023
Издательство
MDPI AG
e-ISSN
20326653
Тип источника
Научный журнал
Язык публикации
English
ИД документа ProQuest
2779613841
Авторское право
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.