|Table of Contents|

Stochastic dynamic user equilibrium assignment model considering penetration of electric vehicles(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2019年05期
Page:
150-161
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Stochastic dynamic user equilibrium assignment model considering penetration of electric vehicles
Author(s):
HUAN Ning;  YAO En-jian;  YANG Yang;  LI Bin-bin;  ZHANG Qian
(Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University, Beijing 100044, China)
Keywords:
traffic planning;  public charging facility;  dynamic user equilibrium;  hybrid traffic flow;  electric vehicle;  charging behavior
PACS:
U491.13
DOI:
-
Abstract:
To analyze the impact of dynamic charging demand of electric vehicles(EVs)on the service level of public charging facilities(CFs), and provide guidances for the planning and operation of public charging network, a nested Logit model was employed to describe the joint choice behavior of EV travel including charging demand judgment, charging facility and path choices under considering the behavioral differences between EV and gasoline vehicle travelers, congestion state of road section, energy consumption of vehicle, location and service level of charing station. A dynamic traffic flow assignment model considering users' en-route fast charging behavior was developed. The stochastic dynamic user equilibrium condition under the hybrid traffic conditions and an equivalent variational inequality model were proposed, and a dynamic traffic flow iterative algorithm containing the charing queuing simulation on EVs was designed. The effectivenesses of the model and algorithm were verified through a numerical example, and the influences of some key indicators regarding charging demand and supply on the service level of CFs in different promotion phases of EVs were further discussed. Research result shows that affected by the distributions of traffic flow and CFs, the utilization ratios of CFs are unevenly distributed from both space and time perspectives. The average charging waiting time tends to increase with the rise of EV penetration rate(PR). The rise of PR also affects the temporal distribution during the charging peak. The initial state of EV battery charge and queuing length at CF have significant negative effects on users' charging demand judgement. The mismatch between the number of CFs and the demand scale in the road network may lead to a sharp decline in the service level and can easily induce local congestion. For most users, the dwell time at CF is within 15-20 min, and the waiting times of approximate 90% users are less than9 min. Therefore, the proposed model is consistent with the reality and can fully reflect a series of influences caused by the charging behavior in the hybrid traffic network. 3 tabs, 14 figs, 30 refs.

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Last Update: 2019-11-13