Data-driven intelligent location of public charging stations for electric vehicles

Abstract

This paper investigates a location problem of public charging stations for electric vehicles with the objective of CO2 emissions minimization through massive GPS-enabled trajectory data. The problem considers two distinct features, including CO2 emissions generated in round trips to charging stations and remaining electricity restrictions on charging decisions. A data-driven and particle swarm optimization-based intelligent optimization approach is developed to handle this problem. We then present how to implement this approach by using taxi trip data in Chengdu, China as case data and explore how much data could reflect effectively the travel patterns of an area. The results of case study show that one-week taxi trip data are sufficient to handle the investigated problem. The results also validate the necessity of considering two realistic features, including CO2 emissions in round trips to charging stations and remaining electricity restrictions on charging decisions, in charging station location problems. It can lead to (1) the reduction of daily CO2 emissions captured by about 0.14–0.37 ha of forests in one year, and (2) 0.85%–2.64% more charging demands being satisfied per day.