Charging Path Planning Strategy of Electric Vehicles with Integrating Dynamic Energy Consumption and Network Information

被引：5

作者：

Lin X. ^{[1
,2
]}

Zhou B. ^{[1
]}

Xia Y. ^{[1
]}

机构：

[1] College of Mechanical Engineering & Automation, Fuzhou University, Fuzhou

[2] Key Laboratory of Fluid Power and Intelligent Electro-Hydraulic Control, Fuzhou University, Fujian Province University, Fuzhou

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2021年 / 32卷 / 06期

关键词：

Charging path planning; Electric vehicle; Energy consumption prediction; Impedance cost;

D O I：

10.3969/j.issn.1004-132X.2021.06.010

中图分类号：

学科分类号：

摘要：

Aiming at the range anxiety problems of electric vehicles, taking the electric vehicles as the research objects, based on the "vehicle-road-network" intelligent system, the road topology model, impedance evaluation model and vehicle energy consumption model were established, the optimal driving time, optimal energy consumption and comprehensive optimization were as the objectives respectively, and the A* algorithm was used to plan the charging path of electric vehicles. Taking Fuzhou urban cycle as an example, the main driving cycle data was collected, the planned road sections were matched with the driving cycle data to predict the vehicle driving time and energy consumption, and the impedance cost of each objective was calculated by combining the waiting time for charging. The results show that the proposed charging path planning strategy may respectively plan the optimal charging path considering time, energy consumption and comprehensive optimization according to the needs of drivers. © 2021, China Mechanical Engineering Magazine Office. All right reserved.

引用

页码：705 / 713

页数：8

共 15 条

[1]

THOMAS C E S., Transportation Options in a Carbon-constrained World: Hybrids, Plug-in Hybrids, Biofuels, Fuel Cell Electric Vehicles, and Battery Electric Vehicles, International Journal of Hydrogen Energy, 34, 23, pp. 9279-9296, (2009)

[2]

TIAN Z Y, JUNG T, WANG Y, Et al., Real-time Charging Station Recommendation System for Electric-vehicle Taxis, IEEE Transactions on Intelligent Transportation Systems, 17, 11, pp. 3098-3109, (2016)

[3]

SU Shu, SUN Jinwen, LIN Xiangning, Et al., Intelligent Charging Navigation for Electric Vehicles, Proceedings of the CSEE, 33, S1, pp. 59-67, (2013)

[4]

KOBAYASHI Y, KIYAMA N, AOSHIMA H, Et al., A Route Search Method for Electric Vehicles in Consideration of Range and Locations of Charging Stations, IEEE Symposium on Intelligent Vehicle, (2011)

[5]

STROEHLE P, BECHER S, LAMPARTER S, Et al., The Impact of Charging Strategies for Electric Vehicles on Power Distribution Networks, International Conference on the European Energy Market (EEM), (2011)

[6]

ANSARI M, ALAWAMI A T, ABIDO M A, Et al., Optimal Charging Strategies for Unidirectional Vehicle-to-grid Using Fuzzy Uncertainties, IEEE PES T&D Conference and Exposition, pp. 1-5, (2014)

[7]

SUNDSTROM O, BINDING C., Flexible Charging Optimization for Electric Vehicles Considering Distribution Grid Constraints, IEEE Transactions on Smart Grid, 3, 1, pp. 26-37, (2012)

[8]

SONG Qing, WANG Xiaofan, Efficient Routing on Large Road Networks Using Hierarchical Communities, IEEE Transactions on Intelligent Transportation Systems, 12, 1, pp. 132-140, (2011)

[9]

HILL G A, NEAIMEH M, BLYTHE P T, Et al., Routing Systems to Extend the Driving Range of Electric Vehicles, IET Intelligent Transport Systems, 7, 3, pp. 327-336, (2013)

[10]

YANG S N, CHENG W S, HSU Y C, Et al., Charge Scheduling of Electric Vehicles in Highways, Mathematical and Computer Modelling, 57, 11, pp. 2873-2882, (2013)

← 1 2 →