Electric vehicle charging stations in the workplace with high-resolution data from casual and habitual users

Problems of poor network interoperability in electric vehicle (EV) infrastructure, where data about realtime usage or consumption is not easily shared across service providers, has plagued the widespread analysis of energy used for transportation. In this article, we present a high-resolution dataset of realtime EV charging transactions resolved to the nearest second over a one-year period at a multi-site corporate campus. This includes 105 charging stations across 25 different facilities operated by a single firm in the U.S. Department of Energy Workplace Charging Challenge. The high-resolution data has 3,395 real-time transactions and 85 users with both paid and free sessions. The data has been expanded for re-use such as identifying charging behaviour and segmenting user groups by frequency of usage, stage of adoption, and employee type. Potential applications include but are not limited to simulating and parameterizing energy demand models; investigating flexible charge scheduling and optimal power flow problems; characterizing transportation emissions and electric mobility patterns at high temporal resolution; and evaluating characteristics of early adopters and lead user innovation.

Automated summary

This article presents a high-resolution dataset of realtime EV charging transactions, useful for identifying charging behavior, analyzing user groups, and exploring potential applications in energy demand modeling, charge scheduling, and transportation emissions analysis.