The Scheduling Control Strategy for In-motion EV Wireless Charging Based on Cooperative Vehicle Infrastructure System

ZHOU Xi-wei; WANG Gui-ping; WANG Hui-feng; SHANG Xiao

doi:10.12263/DZXB.20200954

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The Scheduling Control Strategy for In-motion EV Wireless Charging Based on Cooperative Vehicle Infrastructure System

更新时间：2025-12-08

- The Scheduling Control Strategy for In-motion EV Wireless Charging Based on Cooperative Vehicle Infrastructure System
- Acta Electronica Sinica Vol. 49, Issue 5, Pages: 904-911(2021)
- 作者机构：
  
  长安大学电子与控制工程学院,陕西,西安,710064
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China (No.2018YFB1600600);General Program of Key Research and Development Project of Shanxi Province (No.2018GY-065);Xi'an Science and Technology Program of Shaanxi Province (No.2020KJRC0123)
- DOI：10.12263/DZXB.20200954
  CLC： TP11U495
- Published：2021
- 稿件说明：
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ZHOU Xi-wei, WANG Gui-ping, WANG Hui-feng, et al. The Scheduling Control Strategy for In-motion EV Wireless Charging Based on Cooperative Vehicle Infrastructure System[J]. Acta Electronica Sinica, 2021, 49(5): 904-911.
DOI：

ZHOU Xi-wei, WANG Gui-ping, WANG Hui-feng, et al. The Scheduling Control Strategy for In-motion EV Wireless Charging Based on Cooperative Vehicle Infrastructure System[J]. Acta Electronica Sinica, 2021, 49(5): 904-911. DOI： 10.12263/DZXB.20200954.

摘要

在高级公路中设置无线充电专用道路

对行驶中的电动车辆进行动态无线充电

引发了交通工程领域的深刻变革.然而在最大限度的保证电动车辆充电能效的前提下

如何对这类车辆进行调度和管理

以提高行车安全和道路的通行能力是一个无法回避的关键问题.为此

本文首先建立了系统的车辆调度模型.然后提出了一种新的精英反向变异粒子群算法(Reverse Elitist Mutation Particle Swarm Optimization

REMPSO)

通过与传统的粒子群和遗传算法的比对

证明了该算法的稳定性和寻优能力.然后使用这一算法对系统模型进行求解

得出充电行驶中的优化移动隔离分区.通过车-路协同为电动汽车动态无线充电的路权调度提供了一种可行的控制策略.

Abstract

Special roads set up in highway used to realize dynamic wireless charging for In-motion electric vehicles that leads to a profound change in the field of traffic engineering. However

on the premise of the maximum charging effect of EV

how to schedule and manage such vehicles to improve traffic safety and road capacity is a key issue that cannot be avoided. Therefore

this paper first establishes the vehicle scheduling model of the system. A new reverse elitist mutation particle swarm optimization (REMPSO) algorithm is proposed.And its rapidity

stability and optimization ability are proved by comparing with the traditional particle swarm optimization and genetic algorithm. Finally

this algorithm is used to solve the system model

and the optimal moving isolation partition is obtained. Based on cooperative vehicle infrastructure system

the paper provides a feasible control strategy for the right of way scheduling of dynamic wireless charging for In-motion EV.

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