Data-Driven Planning of Plug-in Hybrid Electric Taxi Charging Stations in Urban Environments: A Case in the Central Area of Beijing

TL;DR

This paper presents a data-driven approach to optimize the placement of plug-in hybrid electric taxi charging stations in Beijing, combining demand forecasting with MILP modeling to minimize costs and improve service.

Contribution

It introduces a novel spatial-temporal demand forecasting method and a MILP model for planning charging stations based on real GPS data in an urban setting.

Findings

The demand forecasting method accurately predicts charging needs.

The MILP model effectively minimizes costs while satisfying demand.

Simulation results demonstrate the model's practical applicability.

Abstract

Plug-in electric vehicles (PEVs) can contribute to energy and environmental challenges. Among different types of PEVs, plug-in hybrid electric taxis (PHETs) go in advance. In this study, we provide a spatial and temporal PHET charging demand forecasting method based on one-month global positioning system (GPS)-based taxi travel data in Beijing. Then, using the charging demand forecasting results, a mixed integer linear programming (MILP) model is formulated to plan PHET charging stations in the central area of Beijing. The model minimizes both investment and operation costs of all the PHET charging stations and takes into account the service radius of charging stations, charging demand satisfaction and rational occupation rates of chargers. At last, the test of the planning method is carried out numerically through simulations and the analysis is complemented according to the results.