Data-driven Electric Vehicle Charging Station Placement for Incentivizing Potential Demand
- Creators
- Sun, Chenxi
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Li, Tongxin
- Tang, Xiaoying
Abstract
It is believed that Electric Vehicles (EVs) will play an increasingly important role in making the city greener and smarter. However, a critical challenge raised by the transportation electrification process is the proper planning of city-wide EV charging infrastructures, i.e., the siting and sizing of charging stations, especially for the cities that just start promoting the adoption of EVs. In this paper, we investigate the following problem: For a city with a limited budget for public EV charging infrastructure construction, where should the charging stations be deployed to promote the transition of EVs from traditional cars? We propose a δ-nearest model that captures people's satisfaction towards a certain design and formulate the EV charging station placement problem as a monotone submodular maximization problem, equipped with gridded population data and trip data. We then propose a greedy-based algorithm to solve the problem efficiently with a provable approximation ratio. A case study using fine-grained Haikou population data, Point of Interest (POI) data, and trip data is also provided to demonstrate the effectiveness of our approach.
Additional Information
© 2021 IEEE. This work is supported in part by the funding from Shenzhen Institute of Artificial Intelligence and Robotics for Society, the National Key R&D Program of China with grant No. 2018YFB1800800, and the National Natural Science Foundation of China (NSFC) under Grant No. 62001412.Additional details
- Eprint ID
- 112796
- Resolver ID
- CaltechAUTHORS:20220107-765204700
- Shenzhen Institute of Artificial Intelligence and Robotics for Society
- National Key Research and Development Program of China
- 2018YFB1800800
- National Natural Science Foundation of China
- 62001412
- Created
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2022-01-09Created from EPrint's datestamp field
- Updated
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2022-01-09Created from EPrint's last_modified field