Multi-objective Optimal Charging Scheduling Strategy for Electric Vehicles Considering Traffic Flow, Power Grid, and Charging Stations

被引：1

作者：

Kong, Weiwei ^{[1
]}

Cai, Tianmao ^{[1
]}

Fan, Yuezhen ^{[2
]}

Jiang, Fachao ^{[1
]}

Wan, Shuang ^{[2
]}

机构：

[1] China Agr Univ, Coll Engn Beijing, Beijing, Peoples R China

[2] Beijing Forestry Univ, Sch Engn Beijing, Beijing, Peoples R China

来源：

INTERNATIONAL CONFERENCE ON INTELLIGENT TRAFFIC SYSTEMS AND SMART CITY (ITSSC 2021) | 2022年 / 12165卷

基金：

中国国家自然科学基金;

关键词：

electric vehicle; charging scheduling strategy; multi-objective optimization; traffic efficiency;

D O I：

10.1117/12.2627941

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper aims to propose a multi-objective optimal charging scheduling strategy for large-scale electric vehicles (EVs), considering traffic flow, power grid and charging stations all together. First, based on the characteristics of these three systems, the mathematical model that characterizes the performance of the traffic flow, power grid and charging stations is designed separately. The multi-objective optimization function and constraints are established, taking the speed of traffic flow, charging load of the power grid, and the quantity of EVs in charging stations as the optimization targets. Then, a simulation platform is built, and a practical case is studied within the third ring of Beijing. 24 h simulation test for 242,880 EVs is performed, and the effectiveness of the proposed strategy is verified by comparative analysis.

引用

页数：7

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