Electric vehicle demand estimation and charging station allocation using urban informatics
This paper performs a novel data-driven approach to optimize electric vehicle (EV)public charging. The authors translate the study area into a directed graph by partitioning it into discrete grids. A modified geographical PageRank (MGPR) model is developed to estimate EV charging demand, built upon trip origin–destination (OD)and social dimension features, and validated against real-world charging data.The results are fed into the capacitated maximal coverage location problem (CMCLP) model to optimize the spatial layout of public charging stations by maximizing their utilization. It is shown that MGPR can effectively quantify the EV charging demand with satisfactory accuracy. Optimized EV charging stations based on the CMCLP model can remedy the spatial mismatch between the EV demand and the existing charging station allocations. The developed methodological framework is highly generalizable and can be extended to other regions for EV charging demand estimation and optimal charging infrastructure siting.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
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Supplemental Notes:
- © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Yi, Zhiyan
- Liu, Xiaoyue Cathy
- Wei, Ran
- Publication Date: 2022-5
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 103264
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Serial:
- Transportation Research Part D: Transport and Environment
- Volume: 106
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 1361-9209
- Serial URL: http://www.sciencedirect.com/science/journal/13619209
Subject/Index Terms
- TRT Terms: Demand; Electric vehicle charging; Electric vehicles; Estimating; Intelligent transportation systems
- Subject Areas: Energy; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01843307
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 25 2022 10:06AM