Deep Reinforcement Learning for Optimal Planning of Fast Electric Vehicle Charging Stations at a Large Scale-Reference-Cited by-同舟云学术

Deep Reinforcement Learning for Optimal Planning of Fast Electric Vehicle Charging Stations at a Large Scale

Published:2024-01-25 Issue: Volume:10 Page:680-688
ISSN:
Container-title:Computing in Civil Engineering 2023
language:
Short-container-title:

Author:

Heo Jae¹²,Chang Soowon¹²

Affiliation:

1. Smart and Sustainable Human-Urban-Building Interactions Laboratory, School of Construction Management Technology, Purdue Univ., West Lafayette, IN. ORCID: .

2. Assistant Professor and Director of Smart and Sustainable Human-Urban-Building Interactions Laboratory, School of Construction Management Technology, Purdue Univ., West Lafayette, IN. ORCID: .

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485248.082

Reference18 articles.

1. Planning and optimizing electric-vehicle charging infrastructure through system dynamics;Abdullah H. M.;IEEE Access,2022

2. AECOM. (2019). City of Chicago greenhouse gas inventory report, City of Chicago.

3. “Integrated planning of electric vehicles routing and charging stations location considering transportation networks and power distribution systems;Arias A.;International Journal of Industrial Engineering Computations,2018

4. Chicago Data Portal. Average daily traffic counts – Map, City of Chicago.

5. Chicago Metropolitan Agency for Planning. Land Use Data. (Mar 7 2023).

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