This study addresses a new charging station network planning problem for smart connected electric
vehicles. We embed a charging station choice model into a charging network planning model that explicitly
considers the heterogeneity of the charging behavior in a data-driven manner. To cope with the deficiencies
from a small size and sparse behavioral data, we propose a robust charging demand prediction method that can
significantly reduce the impact of sample errors and missing data. On the basis of these two building blocks,
we form and solve a new optimal charging station location and capacity problem by minimizing the construction
and charging costs while considering the charging service level, construction budget, and limit to the number of
chargers. We use a case study of planning charging stations in Shanghai to validate our contributions and provide
managerial insight in this area.
ZHANG Yilun1‡ (张轶伦), XU Sikun2‡ (徐思坤), XU Jie1 (徐 捷), ZENG Xueqi3 (曾学奇),
LI Zheng4 (李 铮), XIE Chi5∗ (谢 驰)
. Robust Charging Demand Prediction and Charging Network Planning for Heterogeneous Behavior of Electric Vehicles[J]. Journal of Shanghai Jiaotong University(Science), 2023
, 28(1)
: 136
-149
.
DOI: 10.1007/s12204-023-2576-0
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