skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Long-term strategic planning of inter-city fast charging infrastructure for battery electric vehicles

Abstract

Here, this study introduces a multistage mixed integer modeling framework for long-term strategic planning of the battery electric vehicle (BEV) inter-city fast charging infrastructure. In response to the growing BEV inter-city travel demand, the framework integrates both an optimization model for decisions on where and when to build charging stations and a stochastic queuing model to determine how many chargers are needed for each station. A genetic algorithm based heuristic method is developed to efficiently solve the problem. The model is applied to investigate the long-term infrastructure requirement in the state of California where significant growth in BEV demand is expected in coming decades. Our findings indicate that the charging infrastructure is expanded in both network coverage (number of stations) and service capacity (number of chargers per station) as the BEV demand grows. We also found that the infrastructure requirement is dependent on many factors, such as the BEV electrified range, the required level of service at charging stations, and the range anxiety cost. For most simulated scenarios, the results show that it is beneficial to invest in the inter-city fast charging infrastructure, even though the range anxiety cost is at its low end.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [1]; ORCiD logo [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Clemson Univ., Clemson, SC (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1471941
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Transportation Research Part E: Logistics and Transportation Review
Additional Journal Information:
Journal Volume: 109; Journal Issue: C; Journal ID: ISSN 1366-5545
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; 33 ADVANCED PROPULSION SYSTEMS; Battery electric vehicle; Inter-city charging infrastructure; Charger capacity; Chance-constrained stochastic model; Genetic algorithm

Citation Formats

Xie, Fei, Liu, Changzheng, Li, Shengyin, Lin, Zhenhong, and Huang, Yongxi. Long-term strategic planning of inter-city fast charging infrastructure for battery electric vehicles. United States: N. p., 2017. Web. doi:10.1016/j.tre.2017.11.014.
Xie, Fei, Liu, Changzheng, Li, Shengyin, Lin, Zhenhong, & Huang, Yongxi. Long-term strategic planning of inter-city fast charging infrastructure for battery electric vehicles. United States. doi:10.1016/j.tre.2017.11.014.
Xie, Fei, Liu, Changzheng, Li, Shengyin, Lin, Zhenhong, and Huang, Yongxi. Fri . "Long-term strategic planning of inter-city fast charging infrastructure for battery electric vehicles". United States. doi:10.1016/j.tre.2017.11.014. https://www.osti.gov/servlets/purl/1471941.
@article{osti_1471941,
title = {Long-term strategic planning of inter-city fast charging infrastructure for battery electric vehicles},
author = {Xie, Fei and Liu, Changzheng and Li, Shengyin and Lin, Zhenhong and Huang, Yongxi},
abstractNote = {Here, this study introduces a multistage mixed integer modeling framework for long-term strategic planning of the battery electric vehicle (BEV) inter-city fast charging infrastructure. In response to the growing BEV inter-city travel demand, the framework integrates both an optimization model for decisions on where and when to build charging stations and a stochastic queuing model to determine how many chargers are needed for each station. A genetic algorithm based heuristic method is developed to efficiently solve the problem. The model is applied to investigate the long-term infrastructure requirement in the state of California where significant growth in BEV demand is expected in coming decades. Our findings indicate that the charging infrastructure is expanded in both network coverage (number of stations) and service capacity (number of chargers per station) as the BEV demand grows. We also found that the infrastructure requirement is dependent on many factors, such as the BEV electrified range, the required level of service at charging stations, and the range anxiety cost. For most simulated scenarios, the results show that it is beneficial to invest in the inter-city fast charging infrastructure, even though the range anxiety cost is at its low end.},
doi = {10.1016/j.tre.2017.11.014},
journal = {Transportation Research Part E: Logistics and Transportation Review},
number = C,
volume = 109,
place = {United States},
year = {2017},
month = {12}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 1 work
Citation information provided by
Web of Science

Save / Share: