Optimizing Urban Electric Vehicle Charging Infrastructure Selection: An Approach Integrating GPS Data, Battery Levels, and Energy Availability

被引：0

作者：

Belaid, Meriem ^{[1
]}

El Beid, Said ^{[1
]}

Hatim, Anas ^{[2
]}

机构：

[1] Cadi Ayyad Univ, CISIEV Team, Marrakech 40160, Morocco

[2] Cadi Ayyad Univ, TIM Team, Marrakech 40160, Morocco

来源：

IFAC PAPERSONLINE | 2024年 / 58卷 / 13期

关键词：

Electric Vehicle (EV); Charging Infrastructure; Urban Environments; AC Chargers; DC Chargers; GPS Data; Battery Levels; Optimization Algorithm; Pandapower [!text type='Python']Python[!/text] Library; Sustainable Transportation; User Experience;

D O I：

10.1016/j.ifacol.2024.07.514

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This research paper offers a comprehensive exploratioli of how to enhance the charging infrastructure for Electric Vehicles (EVs) in urban settings. The analysis assesses three hypothetical charging infrastructures in a city, each with generators connected to thc national grid and strategically placed AC and DC chargers. The proposed method employs GPS data, EV battery levels, and energy availability across thc charging infrastructures. Using an optimization algorithm implemented via thc Pandapower Python library, the most appropriate charging infrastructure is identified for individual drivers. The algorithm considers factors such as GPS data and energy availability to suggest thc optimal charging station for each EV's battery level. This pioneering solution aims to streamline the charging process, enhance user experience, and encourage efficient use of urban charging infrastructure for electric vehicles.

引用

页码：392 / 397

页数：6

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