Abstract
Electric vehicle (EV) charging infrastructure is present all over the United States, but charging prices vary greatly, both in amount and in the methods by which they are assessed. For this paper, we interpret and analyze charging price information from PlugShare, a crowd-sourced EV charging data platform. Because prices in these data exist in a semi-structured textual format, an ad hoc text mining approach is used to extract quantitative price information. Descriptive analytics of the processed dataset demonstrate how the prices of EV charging vary with charging level (Direct Current Fast Charging versus Level 2), geographic location, network provider, and location type. Our research indicates that a great deal of diversity and flexibility exists in structuring the prices of EV charging to enable incentives for shaping charging behaviors, but that it has yet to be widely standardized or utilized. Comparisons with estimates of the levelized cost of EV charging illustrate some of the challenges associated with operating and using these stations.
Highlights
Descriptive analytics, in the form of graphs of the interpreted data, are presented. These analytics are intended to summarize the quantitative data extracted from the dataset, in part to demonstrate the utility and reliability of processing the data using the presented methods. They provide a high-level overview of public Electric vehicle (EV) charging prices and how they vary within the diverse U.S public EV charging network
Access to a comprehensive source of EV charging price data can facilitate decisionmaking for EV operators, charging station operators, policymakers, and business innovators
By employing ad hoc text mining to convert the data into a format amenable to direct analysis, this work lays the foundation for studies of a previously underutilized source of data
Summary
Academic Editors: L’uboš Buzna, Pasquale De Falco, Zhile Yang and Byoung Kuk Lee. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. There is no official and comprehensive repository of charging price data for public EV charging stations, PlugShare [8] has obtained price information and other metadata for a substantial portion of the stations in the U.S via crowd-sourcing through its app and website, and through partnerships with charging station providers These data (“the dataset”), which largely exist in textual form, are publicly accessible for individual stations via PlugShare’s app and website interfaces, but are not publicly accessible in the aggregate form necessary for the application of broad analytics. Ad hoc text mining techniques enable quantitative analysis of an otherwise opaque source of EV charging price data; Descriptive analytics provide a high-level image of EV charging price variability in the United States; and Discussion of trends in observed EV charging prices highlights decision-making implications for EV operators, charging station operators, policymakers, and business innovators
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