IEEE Transactions on Industrial Informatics · 2024

A Data-Driven Approach for Optimizing Early-Stage Electric Vehicle Charging Station Placement

Chenxi Sun, Tongxin Li, Xiaoying Tang

IEEE Transactions on Industrial Informatics

Published online in 2023 and assigned to the October 2024 journal issue.

How can early charging infrastructure encourage electric vehicle adoption?

Two proximity models represent user satisfaction with station locations. Population, travel, road network, and point of interest data inform a submodular optimization problem, solved approximately with a greedy method and evaluated in Haikou.

Research themes

Cite this paper

Chenxi Sun, Tongxin Li, Xiaoying Tang. A Data-Driven Approach for Optimizing Early-Stage Electric Vehicle Charging Station Placement. IEEE Transactions on Industrial Informatics, 2024. https://doi.org/10.1109/TII.2023.3245633

BibTeX
@article{tongxin-early-stations,
  title = {{A Data-Driven Approach for Optimizing Early-Stage Electric Vehicle Charging Station Placement}},
  author = {Chenxi Sun and Tongxin Li and Xiaoying Tang},
  year = {2024},
  journal = {IEEE Transactions on Industrial Informatics},
  url = {https://doi.org/10.1109/TII.2023.3245633},
  doi = {10.1109/TII.2023.3245633}
}
2021SmartGridComm

Data-driven Electric Vehicle Charging Station Placement for Incentivizing Potential Demand

Chenxi Sun, Tongxin Li, Xiaoying Tang

A proximity based satisfaction model turns station placement into monotone submodular maximization. A greedy algorithm has a provable approximation ratio, with a Haikou case study using population, travel, and point of interest data.

Where should a city place charging stations with a limited budget? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization