Data-Driven Location Selection for Battery Swapping Stations

Abstract

Electric Vehicles (EVs) have been encouraged to penetrate deeper in the vehicle market for the green transportation system. One of the key issues to promote EV industry is to deploy Battery Swapping Stations (BSSs) that can satisfy the electricity demand of EV users. Since large scale data of vehicles such as GPS locations and electricity requests can be collected, the data-driven approach can be a cost-effective and useful method to select the locations of BSSs. In this paper, we propose a data-driven framework to solve the BSS location selection problem based on a large scale of GPS data of taxies in metropolitan area. The proposed solution consists of three main steps: Hidden Markov Model (HMM) based map matching and trajectory extraction, electricity consumption rate model based battery swapping demand estimation and clustering strategy based BSS location determination. Compared to the state-of-art deployment baseline, our proposed scheme is more easily to implement in reality and the mean distance error between the location that battery swapping demand is generated and the nearest BSS is reduced by 52.5% and 62.7%, which will definitely reduce the range anxiety of EV users and help improve the will of using EVs.