Electric Vehicle Charging Demand Forecasting: Framework and Practical Exemplary Study-Reference-Cited by-同舟云学术

Electric Vehicle Charging Demand Forecasting: Framework and Practical Exemplary Study

Published:2024-06-13 Issue: Volume:33 Page:324-337
ISSN:
Container-title:International Conference on Transportation and Development 2024
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Author:

Lin Wei¹²³¹,Wei Heng¹²³¹,Li Zhixia¹²³¹,Nian Dong¹²³¹

Affiliation:

1. Ph.D. Candidate, ART-Engines Transportation Research Laboratory, Dept. of Civil and Architectural Engineering and Construction Management, Univ. of Cincinnati, Cincinnati, OH. ORCID: .

2. Professor and Director, ART-Engines Transportation Research Laboratory, Dept. of Civil and Architectural Engineering and Construction Management, Univ. of Cincinnati, Cincinnati, OH. ORCID: .

3. Associate Professor, Dept. of Civil and Architectural Engineering and Construction Management, Univ. of Cincinnati, Cincinnati, OH. ORCID: .

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485521.030

Reference17 articles.

1. Abbas, F., Feng, D., Habib, S., Rasool, A. and Numan, M. 2019. An Improved Optimal Forecasting Algorithm for Comprehensive Electric Vehicle Charging Allocation. Energy technology (Weinheim, Germany), 7, 1900436-n/a.

2. Autolist. 2019. Survey: Price range and weak charging network are top reasons consumers avoid EVs. https://www.autolist.com/news-and-analysis/survey-electric-vehicles.

3. Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet;Cai H.;Transportation research. Part D, Transport and environment,2014

4. Chen T.-H. and Liao R.-N. Analysis of charging demand of electric vehicles in residential area. 2013. 2013. 27–31.

5. Optimal Charging of Electric Vehicles Taking Distribution Network Constraints Into Account;De Hoog J.;IEEE transactions on power systems,2015